Annotating protein function with both high accuracy and sensitivity remains a major challenge in structural genomics. One proven computational strategy has been to group a few key functional amino acids into templates and search for these templates in other protein structures, so as to transfer function when a match is found. To this end, we previously developed Evolutionary Trace Annotation (ETA) and showed that diffusing known annotations over a network of template matches on a structural genomic scale improved predictions of function. In order to further increase sensitivity, we now let each protein contribute multiple templates rather than just one, and also let the template size vary.Retrospective benchmarks in 605 Structural Genomics enzymes showed that multiple templates increased sensitivity by up to 14% when combined with single template predictions even as they maintained the accuracy over 91%. Diffusing function globally on networks of single and multiple template matches marginally increased the area under the ROC curve over 0.97, but in a subset of proteins that could not be annotated by ETA, the network approach recovered annotations for the most confident 20-23 of 91 cases with 100% accuracy.We improve the accuracy and sensitivity of predictions by using multiple templates per protein structure when constructing networks of ETA matches and diffusing annotations. Predicting function for the ever-increasing number of sequences and structures produced by Genomics Centers remains a major problem ,2. FewerThe computational tools for this purpose  are baseThis observation spurred template approaches on the hypothesis that if relatively few residues determine binding or catalytic activity, then the presence of these identical residues in identical geometries may suggest an identical function even if the structure is different -16. ThisWe systematically tested this protocol for enzymes ,23,24 anIn this work, we aim to raise sensitivity further by allowing more than one template for each protein. A single template may on occasion fail to capture a true functional site , and if Mus musculus  (see Methods for details). First, ET ranks the evolutionary importance of the residues from the query protein, 3f0nA, for which the function is sought. These top-ranked ET residues usually form surface clusters, and ET identifies two significant clusters that may be functional sites: the first is {26K, 156S, 158S, 161R and 215M} and the second is {123G, 125A, 126S, 127S, 305D, 306A, 307G, 309N}. In single template mode, ETA picks template residues from the larger of the two  as a reciprocal match in the single template mode, while human mevalonate diphosphate decarboxylase [3d4j; chain B; EC 4.1.1.33) is added to reciprocal matches in multiple template mode. In the fifth step, ETA selects the function seen in a plurality of the target structures that matched reciprocally to the query. In our example, EC 4.1.1.33 is selected with one and two votes in single and multiple template modes, respectively.ETA follows an annotation strategy that consists of five steps, illustrated in both single and multiple templates ETA modes in Figure s aureus ; the second relies instead on one six-residue template per protein (6RT); the third uses multiple five-residue templates per protein (M5RT); and the last uses multiple six-residue templates per protein (M6RT). In each mode, ETA was applied on a set of 605 enzymes with full-EC annotations (see Methods). Performance of each template selection mode and sequencebased strategy was measured in terms of accuracy, sensitivity and by the weighted mean of both with the F-measure (see Methods). The results, depicted in Figure Next, we sought to assess performance in a subgroup of especially difficult cases. To this end, we identified 73 query proteins that share no more than 30% sequence identity with any annotated protein in the target set with the same function. The results Figure  show thaIn order to compare each template selection mode with a sequence-based annotation strategy, we identified the closest hits from target set to each protein in both query sets based on sequence identity between the query and the target. We label this strategy SeqID. Each template selection mode outperformed SeqID in terms of accuracy and F-measure in both test sets . Tables This led us to an iterative annotation strategy that applies first the template selection method with the best accuracy, followed in further rounds by the next best, and so on. The methods were ordered by their accuracies as 6R > 5R > M6R > M5R following Figure Aquifex Aeolicus  and matched it to a N5-carboxyaminoimidazole ribonucleotide synthetase from Esherichia Coli with 30% sequence identity  [An example, in Figure chain A) . HoweverIn order to further assess performance, we constructed networks of ETA matches from each of the modes of ETA described above. Competitive diffusion was then carried out as described previously  in orderTo evaluate the ability of our confidence score to identify accurate predictions, we plotted cumulative accuracy against confidence (z-score)  [The gene chain A) highlightThis work aimed to increase the sensitivity of function annotation in protein structures through alternative ET-based template matching strategies. We found that using more than one template per protein raised sensitivity by 6-9% at a cost of 1-5% in accuracy. Overall, the use of multiple templates did yield a better annotation performance as shown by the increase in F-measure. Furthermore, network diffusion based on multiple-template matches outperformed single template-based network diffusion with 3-8% increase in AUC in recovery of the functional classification of cases where ETA completely failed. Interestingly, the simultaneous use of multiple templates arising from all of the structures under consideration was always best, and robust enough to be insensitive to the size or number of templates per protein. Thus, when functional labels are diffused over a network of ETA matches, the result plateaus at a very high AUC value between 0.96 and 0.97. Most usefully, these experiments define prediction confidence thresholds that distinguished reliable prediction, from those less so, and those that likely to be no better than chance. The results show that among proteins with no ETA prediction, diffusion over a network can add novel predictions. More broadly, this strategy of competitive annotation diffusion over ETA-networks should help accurate large-scale annotation of the structural proteome.Data Sets: Our query test set contains 605 enzymes from Structural Genomics centers that cover 348 distinct full-EC numbers. Each pair of proteins in this set share less than 90% sequence identity with one another. Non-trivial test set constitute 73 proteins of the query set. These proteins share at most 30% sequence identity with any protein of the same function at the full-EC level in the target set. Target proteins are selected from 17824 2008PDB90  proteinsEvolutionary Trace: In this work, we used the real-valued version of Evolutionary Trace (ET) algorithm whose detailed description can be found elsewhere ,35. Brie2, which is computed by DSSP [α's coordinates. Template selection algorithm further identifies the center of mass (CM) of the chosen clusters and the closest best ranked-amino acid as the first residue of the template. In order to identify the other residues of the template, the algorithm iteratively selects other most important residues that are closer to the midpoint between the CM of cluster of selected residues in the previous iteration and the CM of the chosen cluster. Residue positions in the templates are labeled by both original residue types in the structure and any non-gapped combination that is at least seen twice in the multiple sequence alignment. In order to generate multiple templates, ETA first identifies a cluster containing at least 11 important surface residues as the first cluster. Next, ETA identifies other clusters, if any, that do not completely overlap with the first cluster, and that contain surface residues with better evolutionary importance ranks than those in the first cluster. Additional templates from those clusters were generated by following the same steps as in case of single templates. Therefore, the number of templates for a given protein was dictated by the number of identified distinct clusters of evolutionary important surface residues for that protein. For example, if there is no other distinct cluster than the cluster chosen for single templates, the template selection algorithm yields only a single template.Template selection: In this work, we used five and six-residue templates relying on a previous study showing that these sizes maximized accuracy and sensitivity . Templat by DSSP . Each reTemplate searching: ETA utilizes paired-distance matching (PDM) algorithm to probe geometric similarities between query templates and other structures. In doing so, PDM first identifies all the residues that are identical to the first residue types of the query template in the other structure. In the next iteration, PDM identifies the residues that are identical to the type of second template residue. PDM retains only the pair of residues whose paired-distance is within 2.5Å with the paired distance of first and second template residues. Further, residues that are identical to the type of third template residue are identified. PDM again applies distance constraint of 2.5 Å upon comparison of combination of all possible-paired distances in order to select the third residue. In further iterations, these steps are repeated to select other residues in the target structure. Each match is assigned with a value of root mean square deviation (RMSD) to quantify the geometric similarity.Match filtering: In the next round, ETA eliminates self-matches and matches with RMSD greater than 2 Å. The remaining matches are fed into the Support Vector Machine (SVM) that is trained for enzymes . Each maReciprocal match: Geometric and evolutionary similarity is probed by both template(s) from query protein onto target structures and templates from target structures onto the query protein. Reciprocal matches constitute the intersection of significant matches selected by SVM in both directions.Function prediction: In the final round, ETA assigns votes to the functions of unique reciprocal matches and thereby suggests the function with the highest vote as a predicted function. If a particular protein is identified as a significant match several times for a query protein, its function gets only one vote.2). accuracy. sensitivity/(β2 . accuracy + sensitivity), where β equals 0.5 to put more emphasis on accuracy.Performance measures: We used accuracy, sensitivity and F-measure to assess ETA's performance, which are defined as follows: Accuracy = TP/(TP+FP) and Sensitivity=(TP)/(TP+FN), where TP is true positive, both known and predicted function agree at the fourth EC level, FP is false positive, predicted and known function does not agree at the fourth EC level, and FN is false negative, which represents the cases with no prediction. F-measure = . We use these elements to pose an optimization problem:We store the resulting network in an adjacency matrix, W. If there are n nodes, W is n by n and Wii = Σj wij) [i - fμ)/fσ where fμ is the average fi across all proteins with unknown function and fσ is the standard deviation of fi across all proteins with unknown function. This results in a z score, which we use as a measure of confidence. The function with the largest z score at for node i becomes our prediction. The Cytocape plugin for ETA networks is available at http://mammoth.bcm.tmc.edu/networks/[http://mammoth.bcm.tmc.edu/eta/. Σj wij) . We repenetworks/. MultiplThe authors declare that they have no competing interests.SE, EV and AML developed the methods. SE and EV collected and analyzed the data. SE and OL led the research design. The manuscript was jointly written by SE and OL with contributions from all authors.Graph of accuracy versus confidence for global diffusion over a network of ETA's six-residue templates. The vertical axis shows the cumulative accuracy of cases at a given confidence score and aboveClick here for file(A) Bar charts showing the number of PFAM accession codes that are associated with the number of proteins in 605-protein query test set and 73-protein query test set  in black and gray respectively. (B) Bar chart showing the number of PFAM accession codes that are associated with the number of proteins in 3082-protein target set.Click here for file


Identifying biologically relevant protein complexes from a large protein-protein interaction (PPI) network, is essential to understand the organization of biological systems. However, high-throughput experimental techniques that can produce a large amount of PPIs are known to yield non-negligible rates of false-positives and false-negatives, making the protein complexes difficult to be identified.We propose a binary matrix factorization (BMF) algorithm under the Bayesian Ying-Yang (BYY) harmony learning, to detect protein complexes by clustering the proteins which share similar interactions through factorizing the binary adjacent matrix of a PPI network. The proposed BYY-BMF algorithm automatically determines the cluster number while this number is pre-given for most existing BMF algorithms. Also, BYY-BMF’s clustering results does not depend on any parameters or thresholds, unlike the Markov Cluster Algorithm (MCL) that relies on a so-called inflation parameter. On synthetic PPI networks, the predictions evaluated by the known annotated complexes indicate that BYY-BMF is more robust than MCL for most cases. On real PPI networks from the MIPS and DIP databases, BYY-BMF obtains a better balanced prediction accuracies than MCL and a spectral analysis method, while MCL has its own advantages, e.g., with good separation values. Protein-protein interactions (PPI) play key roles in the biological processes including cell cycle control, differentiation, protein folding, signaling, transcription, translation and transport etc. Protein complexes are groups of proteins that densely interact with each another . They arPPI networks are generally represented as undirected graphs with nodes being proteins and edges being interactions. Various algorithms have been used to detect subgraphs with high internal connectivity -4. One rX of the PPI interaction graph as a product of two low-rank matrices A and Y with binary entries, i.e., X ≈ AY , where each column of Y represents the interaction pattern of the corresponding protein via weighting the columns of A. A cluster consists of proteins sharing similar interaction patterns. The roles of A and Y are exchangeable due to their symmetric positions in X ≈ AY , and thus BMF gives a biclustering on both the rows and columns of X, where ijx = 1 if there is an interaction between iv and jv, otherwise ijx = 0. Mathematically, protein complexes are defined as sets of nodes with more edges amongst its members than between its members and the rest. Many methods  ,4, whereee e.g., , which wee e.g., .X is binary, and analysis on binary data has been studied in the literature, e.g., in n×N, ijx ∈ {0,1}, and A = [ija]n×m, Y = [jty] m×N , ija, jty ∈ {0,1}. As in 9] for a q(·) gives the Ying representation, and p(·) gives the Yang representation. All components in Ying representation are given by eq.(1), eq.(2), and eq.(3). In Yang representation, the empirical density p(X) = δ(NX - X) is adopted with H(p||q).where initm , and reduces during the implementation of this algorithm at its “Model-Selection-Step”. This automatic reduction results from a least complexity nature of maximizing H(p||q), which can be understood from several perspectives [H(p||q) becomes the negative entropy, further maximizing which will minimize system complexity.To achieve the best harmony, a Ying-Yang alternative procedure is implemented and sketched in Algorithm 1. The cluster number starts from a large enough pectives . By one Yτ)( and Aτ), different initializations BYY-BMF may reach different local optima. To tackle this problem, we implement BYY-BMF at a number of random initializations and select the output with the highest harmony measure. There is a room for more effective implementations.X = TUDU for eigenvectors (the columns of U) and corresponding eigenvalues ; Then, it constructs clusters by selecting top sc%α absolute values of each eigenvector corresponding to large eigenvalues; Finally, it discards the nodes linked to less than sc%β of nodes within a cluster. The obtained clusters depend on the proportion of selection sc%α and the internal connectivity by sc%.βMCL  simulateAs in , the refTo evaluate the accuracy of the predictions, we adopt the following four criteria used in ,4.Sensitivity (Sn) is defined as follows:n and m is the number of reference and predicted complexes respectively, ijT is the number of common proteins in the i-th reference complex and the j-th predicted complex, and iN is the number of proteins in the i-th reference complex. A high nS value implies a good coverage of proteins in the reference complexes.where Positive predictive value (PPV) is defined asWhere Accuracy (Acc) is the geometric average of Sn and PPV,Sn and PPV. Sn increases to 1 for the big cluster of all proteins, while PPV reaches 1 for single-protein clusters.which balances the complementary information provided by Separation (Sep) value is given bySep indicatesa better general correspondence between predicted and reference complexes.Where The authors declare that they have no competing interests.In the additional file, all evaluation results on 42 percentage pairs of random additions and deletions are given. Also, a theoretical analysis on the computational efficiency and performance of the proposed BYY-BMF algorithm is presented.Click here for file


Oryza sativa.Protein-protein interactions play a fundamental role in elucidating the molecular mechanisms of biomolecular function, signal transductions and metabolic pathways of living organisms. Although high-throughput technologies such as yeast two-hybrid system and affinity purification followed by mass spectrometry are widely used in model organisms, the progress of protein-protein interactions detection in plants is rather slow. With this motivation, our work presents a computational approach to predict protein-protein interactions in Oryza sativa, we have developed PRIN, a Predicted Rice Interactome Network. Protein-protein interaction data of PRIN are based on the interologs of six model organisms where large-scale protein-protein interaction experiments have been applied: yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans), fruit fly (Drosophila melanogaster), human (Homo sapiens), Escherichia coli K12 and Arabidopsis thaliana. With certain quality controls, altogether we obtained 76,585 non-redundant rice protein interaction pairs among 5,049 rice proteins. Further analysis showed that the topology properties of predicted rice protein interaction network are more similar to yeast than to the other 5 organisms. This may not be surprising as the interologs based on yeast contribute nearly 74% of total interactions. In addition, GO annotation, subcellular localization information and gene expression data are also mapped to our network for validation. Finally, a user-friendly web interface was developed to offer convenient database search and network visualization.To better understand the interactions of proteins in Oryza sativa. It has greatly extended the current available protein-protein interaction data of rice with a computational approach, which will certainly provide further insights into rice functional genomics and systems biology.PRIN is the first well annotated protein interaction database for the important model plant http://bis.zju.edu.cn/prin/.PRIN is available online at  Proteins seldom perform their biological function independently. Rather, they collaborate with other biological molecules such as nucleic acids and proteins to accomplish complex biological processes. Protein-protein interactions play fundamental roles in almost all biological processes such as signal transduction, internal equilibrium maintenance and organs formation . ConsequSaccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens and Escherichia coli K12 [High-throughput experiments, like Yeast two-hybrid system (Y2H), AP-MS method and Bimolecular fluorescence complementation (BiFC) , have becoli K12 -11. But coli K12 -18.Although protein-protein interactions confirmed in the lab are in a low coverage of the whole proteome, including those of model organisms such as human and fruit fly -9,11, thGenome information is another important source for protein-protein interaction prediction. These types of method usually use genome information  to obtain functional dependence between protein pairs . Gene neSaccharomyces cerevisiae and Drosophila melanogaster with the interolog method, which is then combined with literature mining data to construct the human protein interaction network. The most important trend of interolog methods is taking both the orthologous information and genomic information into consideration to obtain high quality protein-protein interaction networks, such as the approaches applied in MPID [Methods based on evolutionary information such as correlated mutation, interologs ,27, corr in MPID  and AthP in MPID .Arabidopsis thaliana is the only plant species in which a global-goal applicable interactome was computationally constructed [Arabidopsis thaliana protein-protein interaction databases with different approaches: AtPIN [Oryza sativa is still lacking.The complexity of plant materials presents a big obstacle to find analytical protein-protein interactions in plant proteomics research . The genstructed . There as: AtPIN  and AtPIs: AtPIN ,26 with s: AtPIN  and Preds: AtPIN  with ints: AtPIN ,40 with Oryza sativa. Machine learning methods such as SVM and Naïve Bayes Network require both high quality golden-standard experimental data and huge genomic annotations. As a result, interolog method combined with limited rice genome information appears to be a realizable way to construct an unprecedented rice protein interaction network. In this study, we attempt to computationally depict a panorama of rice interactome with interolog method, where genome information such as GO annotations, subcellular localization information and gene expression data are utilised to validate the predicted protein interaction network and at the same time, to extract significant biological network properties.Rare experimental protein interaction data and low level of genomic annotation information are the two main barriers for computational methods to be widely used for Our network construction is divided into two main parts: (i) integration of six reference model organism interactomes and (ii) interologs between rice and the reference organisms, as shown in Figure Arabidopsis thaliana, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens and Escherichia coli K12, whose experimental interactomes are the most complete and reliable. Among these 6 model organisms, Arabidopsis thaliana, a plant species, logically shares the highest evolutionary conservation with rice, while  Saccharomyces cerevisiae has the best coverage of its genome. We derived their protein interaction data from public non-species-specific protein-protein interaction databases: BioGrid [Arabidopsis thaliana. As high-throughput experiments are well-known for their high rate of false positives, there are many noisy records with redundancy and inaccuracy in public protein interaction databases. We either corrected or discarded these errors during the integration process. With their relatively less redundancy and inaccuracy, species-specific databases offer important supplementary data to our integration. We finally integrated 533,927 interactions with 48,152 proteins of the 6 model organisms, which significantly exceeds previous orthologous data used in interolog predictions. As shown in Figure Arabidopsis thaliana, 196,258 interactions with 6,256 proteins of Saccharomyces cerevisiae, 272,246 interactions with 22, 986 proteins of Homo sapiens, 31,036 interactions with 8,064 proteins of Drosophila melanogaster, 9,918 interactions with 4,762 proteins of Caenorhabditis elegans and finally, 17,799 interactions with 3,059 proteins of Escherichia coli K12 , which showed high redundancy and low sensitivity based on our examination. As reported by Huang et al. , true poGene Ontology (GO) is an important bioinformatics tool for genome-scale protein function annotation. GO tries to explain the roles of genes or proteins in eukaryotic cellular process through the establishment of a controlled vocabulary. GO consists of three separate ontologies: cellular component (components of cells or extracellular), molecular function  and biological process . GO uses directed acyclic graph to connect each ontology and renders tree hierarchical relationships between these ontologies. Two proteins involved in the same biological processes have higher possibility to interact than two proteins that are not. Moreover, the more specific a biological process the two proteins are involved in, the likelihood of interaction is higher. Similarly, a more detailed GO annotation will provide a higher chance of interaction. GO mapping can provide an effective measure of the possibility of predicted protein interactions that occur naturally. We mapped the GO annotations derived from Gene Ontology database ,53 and GGO scores were calculated separately, RSScc scores for GO cell component terms, RSSBP scores for GO biological process terms and RSSMF scores for GO molecular function terms. As the RSSGO distribution shown in Figure cc scores of our network mainly fall within 0.6~1; RSSBP scores mainly fall within 0.4~0.8; RSSMF scores mainly fall within 0.4~0.9. The high proportion of RSSMF score 0~0.1 is mainly due to the imperfection of rice GO molecular function terms. All the proportions of score 1.0 in three kinds RSSGO scores are very high: 41.77% for RSScc scores, 19.81% for RSSBP scores and 27.38% for RSSMF scores. This shows that two proteins in PRIN share stronger correlation in GO annotation, indicating a higher possibility to interact with each other.We lead Relative specificity similarity (RSS) scores -58 into Subcellular localization is the specific location a protein or gene product exists in where the cell, such as nuclear, cytoplasm or cell membrane. It plays an important role in understanding cellular organs function and compartmentalization characteristics. Proteins have to fulfill the conditions for space identity during interactions; hence interacting proteins tend to possess the same subcellular localization, known as co-localization. Currently there is no specific subcellular localization database for rice. The rare and scattered rice subcellular localization information presents a difficult task for figures collection. To tackle this, we opted for a computational subcellular localization identifier RSLpred , which iIf proteins exhibit interaction, there are some quantitative requirements which are closely related to gene expression profile. Using microarray based expression data to predict protein-protein interactions has become a trend in computational systems biology. In protein-protein interaction prediction by gene expression, the most common method is the calculation of the Pearson correlation coefficient between two sets of gene expression data. Many predicted protein interactions based on gene expression profile fall into indirect interactions, also known as function related chains, resulting in high level of false positives in the prediction data. Nevertheless, gene co-expression levels still contain important reference value to protein interactions predicted by interologs. This implies that, no gene expression correlation does not entirely mean that the two proteins do not interact, however, if two proteins gene expression correlate, it will greatly increase the possibility of interaction. Especially, if a protein has a significant inhibition or synergistic effect with another protein as shown by gene expression profile, these two proteins should be strongly considered to interact. In our study, rice co-expression data from Rice Array Database  was usedThe web interface of PRIN was developed with JAVA  and was hosted on an Apache web server. The project used MySQL 5.0 as its database management system and Cytoscape Web  to visuaA small data set of experiments determined rice protein-protein interaction including 406 proteins and 430 interactions is integrated from BIND  , compared to random pairs derived from global rice genome. We calculated co-expression rate of all gene pairs appeared in the microarray, and only 2.3% of them display co-expression pattern with Pearson correlation coefficient score cut off 0.5. Therefore, the co-expression quality of our predicted network is highly notable.Arabidopsis thaliana and yeast, implies that little proteins interact individually in the interactome [By loading the predicted rice protein-protein interaction data to Cytoscape , we obtaeractome . An insiOryza sativa has similar interactome connectivity with Caenorhabditis elegans, stronger interactome connectivity than Arabidopsis thaliana and Homo sapiens, and weaker than Saccharomyces cerevisiae, Drosophila melanogaster, Escherichia coli K12. Proteins in our predicted interactome possess an average of 29 neighbours, which is closed to Homo sapiens, less than Saccharomyces cerevisiae, more than Arabidopsis thaliana, Drosophila melanogaster, Caenorhabditis elegans and Escherichia coli K12. This finding may not be surprising since yeast and human are the major interologs source to our data. It has been shown in yeast and human that the average number of neighbours will increase with upgrading of interactome coverage, which indirectly implies a relatively good coverage on our predicted interactome.We made a comparison of network topology properties between the rice interactome and other six species Table . A conneThe node degree of an interactome shows the number of edges a protein is linked to, where self-interaction is also counted. The node degree distribution of our network shows the scale-free network topologies of rice protein-protein interaction network. Apart from self-interacted proteins, proteins in PRIN mainly fall within 2-10 degree hubs, as shown in Figure The shortest path length distribution, as presented in Figure Cn = 2en/(kn (kn-1)), where kn is the number of neighbours of n and en is the number of connected pairs between all neighbours of protein n [The clustering coefficient is a ratio N/M, where N is given as the number of edges between the neighbours of a protein, and M is given as the maximum number of edges that could possibly exist between the neighbours of a protein. It is calculated as rotein n . The averotein n . TherefoThe neighbourhood connectivity of a protein, defined by NetworkAnalyzer, is the average interaction numbers of all neighbours of this protein. As shown in Figure http://bis.zju.edu.cn/prin/. We have found many conserved basic metabolic pathways among species through the interolog process and most excitingly new protein complexes join known pathways. Pathways expansion, metabolic module detection, and protein complex functional annotation based on PRIN will be the most important features for our further comprehensive genomic functional determination in PRIN.PRIN is based on a sophisticated computational method known as interologs, combined with the genomic features of rice. There are certain inner quality controls in our network construction: the huge amount of integrated model organisms' protein-protein interactions, manual proofreading mismatch IDs in database integration, restricted orthologous data with top InParanoid score and manual verification of the resulting network. Genomic feature of rice, such as GO annotations, subcellular location and gene expression data, is mapped to PRIN in order to validate our network and obtain biologically significant results as well. Finally we acquired 76,585 desirable interactions among 5,049 proteins  for proteins in PRIN were obtained from the Gene Ontology database  and Gramed by Wu . RSS scoi and term j) of a given protein, protein A, and α can be defined as:where, α measures specificity between two GO terms  is defined as the correlation strength between A and B [And to a given interacted protein pair, protein A and protein B, terms(A) and terms(B) are all the GO terms corresponding to protein A and B. RSS A and B -58:GO scores were given. With a certain cut off of 0.8, larger RSSBP score indicates that two proteins having stronger correlation in biological processes; larger RSSCC score indicates that two proteins having higher similarity of cell components characteristics; a larger RSSMF score indicates that two proteins are more similar in molecular functions.Three independent RSSRice subcellular localization data was obtained from the prediction of RSLpred. RSLpred is an integrated prediction server for rice subcellular localization based on four kinds of SVM modules: amino acid composition, dipeptide composition, pseudo amino acid (pseAA) composition and evolutionary information of PSI-Blast. RSLpred classified rice proteins into four 4 subcellular locations: chloroplast, cytoplasm, mitochondria and nucleus. The complete rice proteome of EBI and TIGR were predicted by RSLpred with a faster and traditional amino acid composition based module, and these two files were downloaded and combined to get the maximum coverage over our protein interaction network. Considering the transport mechanism of proteins, we did not adopt the winner-takes-all approach used in earlier studies , and alli and Yi are the gene expression level of A and B in time i, the Pearson Correlation Coefficient score (ɣ) can be given as follows:The Pearson correlation coefficients of two rice genes were obtained from the RiceArray Database  calculatm, σx and σy means the standard deviation of gene expression amount during time m. The value of ɣ drops into -1 ~ 1, and -1 means gene expression patterns of A and B are opposite ; 1means that gene expression patterns of A and B are consistent ; 0 means that gene expression patterns of A and B are without any contact. Since interacting proteins may be mutually reinforcing (corresponding to ɣ > 0), may also be inhibited each other (corresponding to ɣ <0), so we use the absolute value of ɣ as a co-expression property between a predicted protein interaction.where, MC conceived this study, PCZ designed the method of this study and analyzed the result. HBG processed the data and constructed the database and web interface. YMJ tested the web server. The manuscript were written by PCZ, reviewed and revised by HBG, YJM and MC. All authors read and approved the final manuscript.Statistics of interolog. This file contains a statistics of interolog data shown in Figure Click here for fileStatistics of GOA GO Slim. This file contains a statistics of GO terms distribution based on standard UniProtKB-GOA GO Slim shown in Figure Click here for file430 experiments detected rice protein interactions. This file contains 430 rice protein-protein interactions, which are integrated from BIND, IntAct, and PlaPID. Proteins are listed in RGAP locus id pairs with their RSSGO score and co-expression PCC score.Click here for fileTopological statistics of PRIN. This file contains detail data resource shown in Figure Click here for file76,585 predicted rice protein interactions in PRIN. This file contains 76,585 rice protein-protein interactions predicted by our interolog method with high confidence. Protein description, RSSGO score, co-expression PCC score, co-localization level and interolog species are also involved in this file.Click here for file


This paper proposes a distributed algorithm for establishing connectivity and location estimation in cluster-based wireless sensor networks. The algorithm exploits the information flow while coping with distributed signal processing and the requirements of network scalability. Once the estimation procedure and communication protocol are performed, sensor clusters can be merged to establish a single global coordinate system without GPS sensors using only distance information. In order to adjust the sensor positions, the refinement schemes and cooperative fusion approaches are applied to reduce the estimation error and improve the measurement accuracy. This paper outlines the technical foundations of the localization techniques and presents the tradeoffs in algorithm design. The feasibility of the proposed schemes is shown to be effective under certain assumptions and the analysis is supported by simulation and numerical studies. Due to the low power, lower cost, and simple configuration requirements of wireless sensor networks, GPS devices and the installation of a base station may be precluded. Hence, robust and distributed internal algorithms are required for sensor positioning problems.Cooperative Hierarchical Positioning Algorithm (CHPA) performs location estimation in four phases: (I) Initial Local Position Estimation; (II) Position Refinement, (III) Relative Global Coordinate System, and (IV) Cooperative Estimation Fusion.It has been shown that cluster architecture guarantees basic performance achievement in ad-hoc networks, since this effective topology control technique conserves limited energy resources, improves energy efficiency, and further provides scalability and robustness for the network. Accordingly, we propose a distributed localization algorithm for cluster-based wireless sensor networks. This paper assumes that a number of sensors are scattered about the landscape. Initially, all of the sensor positions are unknown and must deduce their positions based on the limited information they receive. The basic strategy is to allow groups of nearby sensors to deduce their positions relative to each other in the cluster formation. These clusters are defined by their shared “local” coordinate systems. To this end, the In Phase I, at the local level, sensors exploit the “particle filter” methodology , 2 to camth sensor is located at position k is In Phase II, once a sensor has obtained the initial position estimate, due to the errors occurring in the distance estimation, the sensor needs to implement a refinement mechanism to determine its position. Assume that the In Phase III, a communication protocol allows nearby clusters (these which share “border sensors”) to merge into larger clusters until eventually the complete network is referred to the same coordinate system. The calculations are done in a decentralized manner since the cost of communication (in terms of power consumption) is high.In Phase IV, based on the refined position estimates and the relative coordinate system, when the measurement does not meet the required estimation accuracy, the target sensor may broadcast a fusion message to its neighbors, group nearby two sensors into a measurement system, and trigger the cooperative sensor fusion to resolve conflicts or disagreements, and to complement the observations of the environment. This work introduces the centralized scheme, the progressive scheme, and the distributed scheme for cooperative position estimation. A centralized estimation approach is a process structure that all the neighboring sensors transmit their observations directly to the estimated sensor  where the estimation is performed. The progressive estimation method is a processing structure in which the estimation groups sequentially update the estimation result based on each group’s local observation and partial decision from its previous groups in the sequence without sending data from all sensors to a central processing unit. For the distributed scheme, the target sensor fuses its local estimate and the estimates received from the neighborhood.One of the unique features of our algorithm is the “sharing” of distributional data by the various sensors. This has the obvious intuitive effect of helping to make all the estimates consistent. But it may also have the effect of spreading misinformation if (for instance) a sensor malfunctions. It should be possible to include reliability measures that would effectively discard “bad” information. Hence we explore adding this feature into the basic algorithm to provide extra tolerance to sensor faults, which can be viewed as an attempt to reduce or remove error propagation.The organization of this paper is as follows: Section 2 reviews the current literature on the sensor localization approaches. Section 3 formulates the position estimation problem and derives a hierarchical solution that relies on a cooperative self-localization protocol W3 (31), W4 (32), it can be further expressed by Q = V/(2aeDacD) and the random variable V is defined as in (36). Hence, the density function of Q is given byW4 atQμ, we obtainW4 is approximated by W5 defined as in (33), with Taylor development at μW3 and applying the density function of W3, the density function may be approximated by For the random variable W2 yieldsx coordinate eX is eXμ = μW1 + μW2 and μW1, μW2, and Accordingly, referring to (30) and applying the normal approximation techniques, the density function of the random variable y coordinate eY, it can be rewritten aseX and applying the normal approximations, the density function yieldsW6 yields the same results as described for W1 by substituting aY and cY for aX and cX in (43) and (44), respectively.For the estimated Accordingly, the localization performance can be assessed with the uncertainty in the distance and position information. Moreover, based on the above analysis, the effect of error propagation with imperfect position and distance information may be approximately depicted. Note that this analysis applies normal approximations to describe the probability density functions of the position estimation. The numerical results will be illustrated in Section 5. in order to compare the simulation results and validate the appropriateness of normal approximations.4.2.m is derived to extract information about estimation accuracy.The relationship of mapping between the state and observations is not known precisely because of the measurement errors and the uncertainty associated with the system model. In order to evaluate estimation behavior, the distribution of the measurement term (3) for sensor mZ is the random measurement term, mI is the index set of estimated known sensors, mP is the estimated position of sensor m, Pℓ is the estimated position of sensor ℓ, mℓD̂ denotes the ℓ1-norm ranging measurement obtained from estimated positions,Dmℓ is the ranging measurement between sensors m and ℓ, and mV is the measurement noise. Suppose that mZ, mP, Pℓ, Dmℓ, and mV are assumed to be normal random variables which distributions are To proceed with the analysis, notations and assumptions are introduced to capture the sensible measurement performance. We rewrite the measurement term (3) asmFℓ asGmℓ = D̂mℓ – Dmℓ, which is a normal random variable with the distribution μGmℓ = μD̂m – μDmℓ and variance mFℓ = m|Gℓ|. Therefore, the distribution of the absolute measurement mFℓ is described as the folded normal distribution 44], the 6.We propose a distributed algorithm for the sensor positioning problem in hierarchical wireless sensor networks. By performing the proposed estimation procedures, a single global coordinate system can be established without GPS sensors using only distance information. In order to elevate the estimation accuracy, the Markov chain Monte Carlo (MCMC) steps may be applied to reduce the estimation error such that the propagation error can be suppressed. In the case of poor observations, cooperative estimation fusion schemes are proposed to complement the measurements of the environment and improve the estimation accuracy. Furthermore, the same basic approach can also solve the tracking problem in which the decentralized sensors combine their information to produce improved estimates of the target location. Therefore, one of the strengths of the approach is that  the same algorithm can be used to track targets with unknown positions.There are many other algorithmic questions that would be worthwhile exploring. For example, the resampling method we have used is basic. More advanced techniques may be appropriate, depending on how typical distributions evolve, how many particles should be used, and how does this depend on the number of sensors, their density (in space), etc. Furthermore, we believe there are many ways to improve the performance of the algorithm: (a) by quantifying the trade-offs between amount of communication, speed of computation, and accuracy of the final estimates. (b) by examining alternate ways to “fuse” the received data. For example, once distributional estimates are “shared” between nearby sensors, what are the best ways of incorporating the data? (c) by using a notion of the reliability of the received data. For example, if the “distance” measured between two sensors varies, then this variation suggests an unreliability in the data and hence it should be discounted compared to measurements which are always consistent.For the proposed measurement solution, trade-offs are found between model complexity, energy consumption, estimation accuracy, and sensible model description in real systems. Future plans will involve generalizing the methods to perform actual measurements to evaluate the performance of the proposed positioning system in ubiquitous computing environments.


Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics.Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys ( The scoring or classification of small molecule binding to a receptor or enzyme is an important problem for many areas of biology including drug development for therapeutics However it has proven difficult to accurately predict ligand interactions by any single method Physics-based scoring function approaches record features of ligand-receptor interactions and sum well-established energetic terms such as Van der Waals interactions, charge interactions, hydrogen bonding etc. The AMBER and CHARMm force fields and the MM-PBSA and MM-GBSA methods are examples of this approach knowledge-based group of methods. These extract the probability of specific atomic interactions occurring in observed  complexes and treat these probabilities, following a Boltzmann approach, as reflecting energy of interaction Another group of approaches for determining protein-ligand affinity is the Empirical potential scoring functions follow the physics-based model but add additional, terms for molecular interactions and parameterize the resulting affinity equation. Terms are adjusted by regression of a linear equation describing interactions to train the method to produce observed ligand affinities as in X-score  One factor that makes scoring ligand affinity difficult is that various ligand binding sites may present different types of potential interactions. Also, various ligands may bind a given protein in different modes, using different portions of the binding site. One way to adapt to the variety of different types of ligand binding is to form a consensus amongst methods that might have strengths with one type of complex or another. Consensus methods for scoring protein-ligand binding have found widespread use. An example is the averaging of three hydrophobic terms in X-score Neural networks are an attractive option for creating consensus Virtual screening is the identification of novel ligands that might bind a binding site, using only computation Here we present a method for predicting the relative affinity of ligands bound to protein binding sites. The method is conceptually an empirical potential approach but is nonlinear, with more input factors than the typical empirical method. The extra terms are included to mimic the larger number of factors that are typically observed in consensus methods. The inclusion of a neural network also allows the analysis to robustly work with groups of protein-ligand complexes of diverse characteristics. This feature, robustness with diverse types of binding site, is also typical of consensus methods. Internal consensus analysis works well on many proteins and in a variety of types of protein-ligand interaction studies. Its features could easily be incorporated into other scoring applications.The method has two or three basic steps plus some elaborations. Step 1) involves assaying a protein-ligand complex using 9 factors that include features such as contacts and hydrogen bonds. The structure of the complex and additional information about atom types and charge are used to determine these 9 values. Step 2) involves using the 9 factors to input to a neural network, which in turn outputs a score value. The score values can be used for analysis, e.g. to calculate an AUC value, or directly as a prediction of whether a complex is stable or not. Step 3) is training and is only needed for new types of complexes. Step 3) uses data from Step 1) for a curated set of complexes to determine coefficients that optimize the function of the neural network.Much scoring of protein-ligand binding complexes by consensus methods has been ad hoc and based on combining the output of existing applications For three categories of interaction, different factors for inclusion in scoring protein-ligand interactions were selected. For contact interactions, Van der Waals energies, one factor was constructed to resemble a classic Lennard-Jones function. The other was designed to reward more distant interactions with a conformational ensemble model of protein-ligand complexes in mind  lead to a potentially high number (50) of parameters for the internal consensus model. This, in turn, could weaken analyses or lead to overfitting. Typical consensus methods also have a high number of parameters, though distributed in the component methods used to form the consensus. Correlation of the factors used to form an internal consensus could simplify the internal consensus model. The number of effective parameters in practice Neural networks have special advantages in classification of complex data. In particular they can learn to recognize correct targets embedded in data with various types of patterns. Much effort has been spent in the attempt to create a universal scoring method applicable to all proteins. Others have suggested that superior pragmatic results can be achieved using machine-learning methods that recognize the special features of a group of proteins Good ligands all show high affinity for their binding sites, but the mechanisms for achieving that high affinity vary. Neural networks and consensus methods reflect two different approaches to permit machine learning to represent multiple patterns for ligand binding. Those specific patterns might include ligands that achieve high affinity mostly through hydrogen bonding and charge interactions and ligands that achieve high affinity mostly through hydrophobic interactions. Neural network architecture, used in internal consensus, is ideal for holding these multiple representations in a single model. Consensus models achieve similar results indirectly by combining methods that are strong in analysis of one type of complex or another.Many methods exist to produce a numerical score for ligand-receptor complexes. Often this score is interpreted as related in some way to ΔG of ligand binding Neural network training consists of finding a local error rate minimum after iterative training cycles An internal consensus protocol was performed on a series of native ligand/receptor complexes. In each case native ligands were matched to 5 DUD decoys that have similar sizes and characteristics to the high affinity ligands but are predicted to bind with much lower affinity 2 (coefficient of determination), between true classification and the classification of a method. The r2 value can be interpreted as the fraction of data variability predicted by the analysis method. An r2 value>0.5 indicates significant evidence of correct prediction above the random level. With both ROC AUC and r2 analysis, using the internal consensus strategy produced robust predictions, and was superior to Vina. The internal consensus approach also compared very favorably with surveys of other commonly used methods Receiver operating characteristic (ROC) curves are a widely accepted way to determine accuracy of protein-ligand scoring. ROC analysis  indicateAs a comparison, reduced models were analyzed . The mod2>.65). For HIV protease, VDW, hydrogen bond and one of the hydrophobic terms (r2>0.64) but not the coulombic term contributed. Thus, as is evident as well from examination of the crystal structures, different types of interactions are key for these two classes of protein-ligand complexes. A possible strength of the internal consensus approach is the potential to analyze multiple representations of binding sites rather than reducing that diversity to an average as in regression methods. The high accuracy of prediction produced by internal consensus trained on multiple proteins  and not with any of the other input factors (r2<0.2) suggesting that training regression models (as shown above) on multiple proteins might yield an overly simplified model. Others have suggested that empirical methods often reduce to measurement of VDW terms It is somewhat difficult to deconvolve the processes of a neural network that produce a score in order to understand its predictions. It is simpler, and still relevant, to determine the correlation of factors with ligand binding status. This analysis shows that different input factors contribute to the variance of complex formation for trypsin and HIV protease ligands vs. decoys . For tryScoring functions can be used to identify binding conformations, poses, of ligand that are very close to the native structures identified by x-ray crystallography. Consensus methods have been useful in identifying well-docked complexes The internal consensus method classified 87.9% of 269 complexes correctly into near-native and decoy groups. The HIV-1 protease binding site is especially large and complex, holding higher molecular weight ligands A common use for scoring methods is in the virtual screening of small compound databases to attempt to find lead molecules that might bind a target protein. This process of drug discovery requires a method to recognize target-ligand complexes that may bind tightly. Though it is common to use methods that score ligands, inevitably the use of virtual screening is to produce a short list of compounds for further testing. That is, the methods are used more to rank than to score. Here, the nonlinear function output of internal consensus analysis is used for ranking.To simulate virtual screening, documented ligands or decoys from the DUD database were docked to 39 proteins (about 70 complexes/protein). This approach has been used by a number of groups to benchmark methods for virtual screening Virtual screening methods, crucially, must concentrate binding ligands into a small selected pool that represents only a tiny fraction of the original candidates Therapeutic drug development, molecular probe development and other aspects of biology make use of ligand predictions to identify important molecules. Many methods have been proposed, but none are entirely successful. Here, a new method that suggests improvements on these predictions is presented. Similar to consensus methods that combine several applications, internal consensus uses a model for ligand binding that can analyze diverse ligand binding interactions. Both the use of multiple, overlapping factors and a neural network analysis contribute to the ability of the internal consensus strategy to robustly deal with multiple types of ligand complexes. Some other models may be less complex than the data they are set to analyze. Overall, Vina showed significant ability to recognize protein-ligand complexes, but the internal consensus analysis was superior in most tests to Vina and other non-consensus methods. Internal consensus analyses can overtrain as they learn their input data, but that difficulty was formally prevented in this work. Because the internal consensus approach to scoring complexes is consistent with training on discrete binding/nonbinding data, the quantity of training data available to it may be greater than that available to some other methods, permitting more focused analyses. The internal consensus produced by this strategy may capture key aspects of ligand binding that contribute to free energy of binding. Though not formally a physics-based method, internal consensus is physics-based in spirit, attempting to provide to its neural network factors reflecting known contributions to energy. At the same time, the approach is more realistic about the messiness of real data than many methods. Docked ligands are, at best, near-native in conformation. To be useful for applications such as virtual screening, methods must be able to analyze imperfect complexes. The successes of analyses incorporating internal consensus suggests pragmatic changes in scoring methodology that might improve accuracy while making only modest compromises with existing empirical methods.Factors were selected with an attempt to reflect physics-based interactions known to act in protein-receptor complexes. Protein-receptor or protein-decoy complexes were scored for each of the 9 factors.1 and c1 are constants reflecting the sum of VDW distances for the atom pair to the sixth or eighth power, and r is the observed distance between the atom pair. VDW1 was truncated at 4.5 Angstroms.Two factors were selected to reflect Van der Waals interaction. VDW1 was a Lennard-Jones related function of atomic distances:VDW2 was selected to reflect more distant interactions perhaps interactions not present in the structure under consideration, but present in ensemble structures due to protein flexibility.HBOND takes a value of 1 if a hydrogen bond donor is within 2.5 Angstroms of a hydrogen bond acceptor. Donors and acceptors are defined by MGLtools atom types 2, where Θ is the hydrogen bond angle.HBANG, the hydrogen bond angle term, assumes a value of 0 if a hydrogen bond makes an angle (with hydrogen at its vertex) of less than 90°. Otherwise the value is cos(Θ)HLONG is a term that allows long hydrogen bonds, e.g. those formed via an intervening water molecule. The conditions for permitting an HLONG interaction are the same as those for an HBOND, but the distance cutoff is HLONG<3.3 Angstroms.NHB is a term reflecting number of hydrogen bond donors or acceptors that do not meet the criteria for forming hydrogen bonds of the HBOND or HLONG type.HYDR1 is a term intended to correlate with a hydrophobic environment for the ligand. The value represents the number of receptor hydrophobic residues that lie within 4.5 Angstroms of the ligand HYDR2 represents another hydrophobic term, but one based on an atomic level. The term reflects a count of the number of receptor carbon atoms within 4.5 Angstroms of a ligand carbon atom. Though only an approximation, the value of HYDRO2 may correlate with waters displaced during ligand binding.1q2/r2 where q1 and q2 are the Gasteiger partial charges of the two atoms provided by MGLtools COUL is a term for coulombic charge interactions. COUL = q2), 1000 DUD decoys For calculation of factor correlations . Observed data for protein-ligand complexes was coded as 1  or 0 . Typically, the neural network was trained using data from a single protein. Prediction values were taken as the neural network output node value  or as predicted −ΔG (for Vina). In all cases the sign of the data was such that r would be positive if the method correctly predicted binding. Samples with approximately 60–150 native complexes and 300–750 docked decoys were created and split into training and scoring files. The training subset was used to train the internal consensus neural network. The second non-redundant sample was scored. To determine variance the process was repeated but with different training and scoring sets. In all cases the training and scoring sets did not contain any ligands or decoys in common. Training was either focused on a single target protein ROC curve analysis provided one way to judge accuracy in scoring protein-ligand complexes. For ROC curves, the true positive and false positive rates were compared as the threshold for scoring as positive was varied. ROC AUC is a simple area under the given ROC curve. The AUC value 0.5 results from a method that does not select better than a random level. Another approach to measuring prediction accuracy is the correlation coefficient, To test the functioning of the internal consensus method, elements were combined in different ways. One hybrid model used the 9 input factors and a linear regression least squares training approach. A second model was designed to partially mimic the linear regression model X-score which uses three input factors. Three internal consensus input factors  substituted for X-score VDW, H-bond and hydrophobicity terms. A third model used these three internal consensus factors with a neural network analysis. In each case, coefficients were estimated on one set of data. A second, non-redundant, set of data was scored using the same model that generated the coefficients.i. If Θ is 0, then Θ is reset to 1/(positives+negatives) to avoid calculation problems. For pROC calculations with internal consensus analysis, the neural network output node value was used as a method score.Assessing virtual screening requires assessment of ligand selection. The metric pROC weights the early part of the selection curve as is desirable for screening purposes Enrichment is a key concept in use of scoring methods in virtual screening. Enrichment was calculated at several points at which varying amounts of the database had been discarded because it was below the score threshold. For each point the fraction of the database discarded was determined. Then enrichment was calculated:A group of HIV-1 protease/inhibitor complexes were analyzed by using Vina to generate a library of ligand binding poses. Complexes were analyzed using RMSD, used here as a measure of docked ligand deviation from the crystal structure ligand conformation. Docked complexes were separated into two groups: a well-docked group had an RMSD of <2.0; a decoy group had an RMSD of >3.0. The two groups  were used to train the internal consensus method to distinguish complexes based on RMSD. A non-overlapping group of 269 complexes derived from HIV-1 protease bound to 32 different ligands was then scored for correct classification. To generate a series of single ligand conformations as a potential source for analysis and visualization, the ligand of PDB file 1bv7 was extracted and repeatedly docked to give 45 bound poses. Each docking generated 9 new conformations since each Vina run started its process at a configuration determined by a random seed value. Each pose was evaluated by the internal consensus method. The highest-scoring pose was documented.Virtual screening used the DUD database. Decoys in the database were examined especially to confirm that they were matched in features and size to ligands. DUD database decoys were similar in mean size but had somewhat less size dispersion than ligands. In general, the DUD decoys were well-matched to their ligands. Virtual screening of complexes involved screening groups of 70 or >1000 (mixes of decoys and ligands). The small groups permitted screening of more proteins. The large groups were more realistic virtual screen conditions with few true ligands and many decoys. Internal consensus analysis allows both ligand ranking and scoring. By changing the internal threshold for scoring, only higher ranked molecules are scored as positive.Table S1Efficacy of hybrid methods in classifying native and decoy protein-ligand complexes.(DOCX)Click here for additional data file.


The ability to predict protein-protein binding sites has a wide range of applications, including signal transduction studies, de novo drug design, structure identification and comparison of functional sites. The interface in a complex involves two structurally matched protein subunits, and the binding sites can be predicted by identifying structural matches at protein surfaces.We propose a method which enumerates “all” the configurations (or poses) between two proteins (3D coordinates of the two subunits in a complex) and evaluates each configuration by the interaction between its components using the Atomic Contact Energy function. The enumeration is achieved efficiently by exploring a set of rigid transformations. Our approach incorporates a surface identification technique and a method for avoiding clashes of two subunits when computing rigid transformations. When the optimal transformations according to the Atomic Contact Energy function are identified, the corresponding binding sites are given as predictions. Our results show that this approach consistently performs better than other methods in binding site identification.http://sites.google.com/site/guofeics/dobi for non-commercial use.Our method achieved a success rate higher than other methods, with the prediction quality improved in terms of both accuracy and coverage. Moreover, our method is being able to predict the configurations of two binding proteins, where most of other methods predict only the binding sites. The software package is available at A lower ACE value implies a lower (and hence more favorable) desolvation free energy.A and B denote two protein structures (subunit), and write A = , and B = , where ai, and bj are atoms of structure A and B. Without loss of generality, we assume that n ≥ m. We also assume that we know the 3D coordinates of each atom in both input proteins. We use A[i:j] to denote the subsequence , and refer to a subsequence of atoms as a structural fragment.In this paper, we assume that subunits are rigid. A protein structure consists of a sequence of residues. Each residue consists of a set of atoms. We assume that the atoms in a residue are ordered as a sequence. Hence, the whole protein structure can be represented by a sequence of atoms. In the rest of this subsection, we let B is fixed, and we perform rotations and translations  on A. The method proposed here is modified from the algorithms for structure comparison is to rotate it around the axis through ai and aj. We use a 1° step size; that is, we explore 360 different positions for the remaining atoms through 360 rotations. FigureAssume that two points ai,bi′) and . We can simply enumerate all the atoms pairs as the interaction pair candidate. However, there will be O(n4) such cases, which makes the computer program too slow in practice. This is perhaps one of the reasons that such a method has not been tried. The focus of the following subsection is to identify two pairs  and  which are more likely to be interaction pairs.The method will work well if we know two interaction pairs  only surface fragments can be candidate binding sites for a configuration and (2) there is no clash between the two proteins in such a configuration. Before presenting the details of the method, we define the csc = , and B = , we assume that ai interacts with bi′, and aj interacts with bj′. Using the enumeration method described earlier, we enumerate different configurations for A and B and compute the corresponding ACE score for the atom sets A and B. We do not consider any configuration which causes A and B to clash. In this step, a pair of structural fragment which does not give any configuration with an ACE score below a specified threshold is discarded. In this paper, we define the threshold value as 400, since the ACE scores of actual interface in the docking decoy set from Dockground are all less than 400. After this step, it is unlikely for two protein structures which cannot be bound to have an unfiltered fragment pair.From the fragment pairs obtained in the previous step, a second step is used to further filter out fragment pairs of ACE scores below a threshold. Given two structural fragments ai, bi′) and  in the enumeration, since these are the atoms that are likely to be interacting. Assuming that there are k fragment pairs from the same two proteins left after the filtration of the second step, we will have a maximum of 2k distinct atom pairs to choose. Thus, there is a total of at mostai, bi′) and .In the third step, for each pair of protein structures with at least one remaining fragment pair, we enumerate all the potential configurations for the structures. We want to use the begin and end atoms of the identified fragments for our choice of  (iRMSD) between the experimental structure and the predicted complex is 2.36Å.Besides the identification of binding sites, our program also estimates the orientations and positions of the proteins after binding. FigureIn this experiment, DoBi is compared to ProMate and PINUP. The test data is originally used by ProMate, and consists of 57 non-homologous proteins. The results are reported in TableThe average of the sizes predicted by DoBi, PINUP and ProMate are 23.5 residues, 19.0 residues and 5.4 residues respectively, while the actual average size  is 21.0 residues. The number of residues correctly predicted to be on interface by DoBi, PINUP and ProMate are 12.3 residues, 8.3 residues and 2.7 residues respectively.TableIn this study, we compare DoBi to core-SVM using the same data set of 50 dimers which core-SVM was tested against. The resTableunbound, whereas a protein with a binding partner in a complex is referred to as bound. We test our method in both the bound and the unbound cases. To further evaluate our method, we perform tests on the protein-protein docking benchmark v4.0,33. ThisRunning time""We used a Pentium(R) 4 (CPU of 3.40GHz) to run DoBi. The computation for each of the 176 complexes took 100 seconds on average.The complexes are classified into broad biochemical categories: Enzyme-Inhibitor (52), Antibody-Antigen (25) and Others (99). The average accuracy and coverage of DoBi are 61.8% and 67.9% respectively on the 52 complexes in Enzyme-Inhibitor, 51.6% and 70.1% on the 25 complexes in Antibody-Antigen, and 58.2% and 69.1% on the 99 complexes in Others. A success rate of 77.6% is achieved for the Enzyme-Inhibitor complexes. The details are shown in TableThe pairs of unbound proteins are classified into three categories: 121 rigid-body (easy) cases, 30 medium difficult cases, and 25 difficult cases, according to the magnitude of conformational change after binding[Cα iRMSD between the experimental structure and the predicted complex is 4.12Å.DoBi discovered several good configurations for the medium difficult cases. One of the instances is 1wq1(R:G). Its configuration discovered by DoBi is shown in FigureDoBi is optimized for binding site prediction, but it also can be used to dock two protein structures. We compare DoBi’s poses to the best configurations obtained by ZDOCK and 3D-Dock. ZDOCK uses a fCα iRMSD, F-score and the fraction of native contacts are used to evaluate the results by different methods. The fraction of native contacts is used by 3D-Dock[We calculate the predicted structures by different methods on the complexes in benchmark v4.0 and the targets in CAPRI. CAPRI is a community-wide experiment to assess the capacity of protein docking methods to predict protein-protein interactions. The Cα  3D-Dock. It is cWe compare the docking results of DoBi, ZDOCK and 3D-DOCK on the CAPRI targets. The results are shown in TableEach of DoBi and 3D-Dock produced ten results for each target, and the configurations with smallest iRMSD values among those ten predictions are used for comparison. The average iRMSD values for DoBi and 3D-Dock are 9.2Å and 9.1Å. However, the overall fractions of native contacts for DoBi and 3D-Dock are 29.1% and 26.8%. DoBi’s performance on binding site prediction is better than that of 3D-Dock.The docking results obtained by DoBi and ZDOCK on Benchmark v4.0 are shown in TableCα iRMSD values between the experimental structure and the configurations by DoBi and ZDOCK are 2.97Å and 1.97Å, respectively. DoBi improves F-score value of ligand protein by 2.7%, and that of receptor protein by 0.4%. The configurations produced by methods are shown in FigureWe calculate the docking results of 1i4d. The We notice that DoBi performed badly on a few specific instances. We analyze this performance issue with TableFigureIn this work, we proposed an approach to identify binding sites in protein complexes by docking protein subunits. The method is implemented in a program called DoBi. DoBi consistently and significantly performed better than existing techniques in predicting binding sites in experimental results.We identify a few potential areas for future improvements to our method. The first area to work on is in the energy function used. Currently, DoBi uses a simple score function. As suggested by the experiment results, a better energy function is able to improve the performance of DoBi.A second area for improvement is in our current assumption that protein structures are rigid when binding. In reality, protein structures may vary sightly or even dramatically when they bind. Hence, further studies on this issue are very much in demand.Although our method shows better overall performance, there are some protein complexes where other methods outperformed DoBi. It will be beneficial if we could combine the strengths of these existing programs with DoBi, to come up with a more reliable method.aThe initial two letters from each of the two words, Docking and Binding, were taken.The authors declare that they have no competing interests.FG participated in the design of the study, performed the statistical analysis, and is in charge of the software package development. SL participated in the experiment design and drafted the manuscript. LW conceived of the study, participated in its design, and helped to draft the manuscript. DZ is heavily involved in the computation of the tables. All authors read and approved the final manuscript.


Understanding protein complexes is important for understanding the science of cellular organization and function. Many computational methods have been developed to identify protein complexes from experimentally obtained protein-protein interaction (PPI) networks. However, interaction information obtained experimentally can be unreliable and incomplete. Reconstructing these PPI networks with PPI evidences from other sources can improve protein complex identification.We combined PPI information from 6 different sources and obtained a reconstructed PPI network for yeast through machine learning. Some popular protein complex identification methods were then applied to detect yeast protein complexes using the new PPI networks. Our evaluation indicates that protein complex identification algorithms using the reconstructed PPI network significantly outperform ones on experimentally verified PPI networks.We conclude that incorporating PPI information from other sources can improve the effectiveness of protein complex identification. A protein complex is a group of associated polypeptide chains linked by noncovalent protein-protein interactions (PPIs). Protein complexes have an important role in biological processes and perform independent discrete biological functions, such as DNA transcription, mRNA translation, and signal transduction Computational approaches also can be applied to identify protein complex information by searching densely connected regions in a PPI network noninteracting pairs, especially not on a large scale. Hence, learning from positive and unlabeled data (LPU) n pairs as the predicted PPI pairs. Some popular protein complex identification algorithms COACH (a core-attachment method) http://202.118.75.18∶8080/PPINPredictor/. We also evaluated our LPU model and compared the performances on reconstructed PPI network with ones on the DIP In the current paper, instead of using 1 or 2 sources to predict protein complexes, we applied machine learning to predict PPI pairs from 6 diverse sources and supplemented the reliable PPIs with predicted PPI pairs. Since protein structures provided a strong evidence for Protein-Protein Interactions prediction, we selected positive PPIs based on Domain-Domain interaction (DDI) information and purified PPI datasets Krogan core For a given organism, the proposed protein complex identification approach contains 2 steps . The firThe following sources are considered PPI-related features.t. Let tD be the collection of GO terms that are either t or its descendants. Let A be the occurrence of t annotations given a collection c. The probability of t in c, or p, is defined as:GO 1, t2)CA A protein domain is a conserved part of a given protein sequence and structure that can evolve, function, and exist independently of the rest of the protein chain. Domains often suggest the propensity for the proteins to interact or form a functional unit, such as protein complex. If two proteins have more domain-domain interactions (DDI), they have more possibility to have an interaction. So we used one feature to capture DDI information for a protein pair. As shown in STRING Affinity purification combined with mass spectrometry (AP-MS) is a powerful method for high-throughput PPI identification. Affinity purification consists of first tagging a protein of interest (bait) by genetically inserting a small peptide sequence (tag) onto the recombinant bait protein. The bait protein is affinity purified, together with its interacting partners (preys), which are identified using mass spectrometry. The partners can have either a direct physical interaction with the bait or an indirect physical interaction mediated by a protein complex. AP-MS experiments provide direct information about co-complex relationships among proteins. However, proteins in the same protein complex may be missed in the screen  if they fail to bind tightly enough, whereas other proteins may be copurified if they bind nonspecifically to the bait . Because of these false-negatives, false-positives and the datasets are large, computational methods have been developed to isolate true protein complexes out of the purification results. These computational methods typically convert the co-complex relationships in the AP-MS data into binary PPIs. They proposed different measurements to assigns a reliability score to every protein pair in converting multirelationships into binary interactions, such as socio-affinity index by Gavin GCD-Dist is defined as the proportion of partners that the 2 proteins have in common:G(X) and NG(Y) are the set of neighbors for X and Y. CD-distance, originally proposed by Brun et al Not every interaction pair is present in curated PPI networks. We consider a protein pair to have a higher probability of being a PPI pair if they have many common neighbors in a PPI network. We use the Czekanowski-Dice distance (CD-distance) to capture such information. Given a pair of proteins X and Y in an interaction graph G, n}. We repeated the strategy m times and constructed m×(n+1) classifiers for a chosen machine learning algorithm. Each of the classifiers assigns a score to each of the unlabeled pairs, in which the score measures the possibility or confidence of a pair being positive. We then rank the unlabeled pairs by the sum of the m×(n+1) scores; those ranked high are considered to be predicted PPI pairs.Using machine learning to predict a protein pair to be a PPI pair (or not) requires a training set containing pairs that are annotated as positive or negative. The positive pairs can be obtained from curated knowledge sources with protein structure evidence, whereas a confirmed report of noninteracting pairs is difficult to obtain. We randomly selected unlabeled protein pairs to act as negative pairs, since only one in several hundred potential protein pairs actually contain interacting partners. Thus, over 99% of our random data is indeed noninteracting, which is probably better than the accuracy of most training data. This randomly sampling negatives way is popular applied in LPU (Learning from positive and unlabeled data) model for many researches and got a good performance, such as ref. After acquiring predicted PPI pairs, existing computational methods developed to identify protein complexes from PPI networks can be used. We employed 7 state-of-the-art protein complex identification algorithms here: COACH COACH CMC Adamcsek et al. MCODE algorithm proposed by Bader et al. IPCA Clusterone MCL NA between a predicted cluster p ∈P and a real complex b ∈B, where P is the set of predicted complexes by a computational method and B is the set of real ones in the benchmark.We followed existing approaches p||V is the number of proteins in the predicted complexes and b||V is the number of proteins in the real complex. If NA≥ω, a real complex and a predicted complex are considered to be matching  In NA scores, precision, recall, and F-measure are applied to assess the methods:After all real complexes and predicted clusters have their best match calculated according to their cp is the number of predicted complexes that match at least 1 real complex, and Ncb is the number of real complexes that match at least 1 predicted complex NThere are lots of negative protein-protein interactions in the real world, we prefer to obtain the candidate PPI with high probability to be true. Since the prediction scores of a classifier indicates the probability of being positive in descending order, we chose Lift Saccharomyces Genome Database http://wodaklab.org/cyc2008/) with a total of 408 manually curated heteromeric protein complexes. After filtering out complexes composed of a single or a pair of proteins, the final benchmark set contains a total of 231 protein complexes.We evaluated our approach by performing a yeast protein complex identification task. We downloaded yeast protein interaction data from DIP The GO website was accessed in September 2011 to retrieve GO annotations and GO Slim terms for yeast. A total of 161 microarray data series for yeast (using platform PL90), consisting of 2,015 samples, were downloaded from Gene Expression Omnibus (accessed September 2011). The expression measures were log transformed, and a Pearson correlation coefficient was computed for each protein pair. We retrieved yeast protein domain information from UniProtKB Because many protein pairs do not have PPI evidence from sources other than Gene Expression Omnibus and GO annotations, we used the following heuristics to filter out protein pairs:There is only PPI evidence from Gene Expression Omnibus and GO annotation;For the GO hierarchies BP and CC, Type I GO features have a value of 0 and Type II GO features have a value less than 0.002; andThe Pearson correlation coefficient is less than 0.5.We obtained 4.64 million protein pairs according to the heuristics. Note that many of our features have numeric values. We applied discretization according to the value distribution , which yielded a vector containing 281 elements for every pair.Considering the protein structure is solid evidence, we integrate Domain-Domain interaction information for selecting reliable positive PPIs. As Krogan and Collins provided a purified PPIs and many methods utilized their datasets for protein complexes identification Considering class distribution in the training set can impact the performance of the resulting systems, so we used following two strategies to generate multiple negative examples. One we called group-way, another called fixed-size way. The group-way sampled multiple sets of negatives using different class distributions: the ratios of positives and negatives are set to be {1∶1, 1∶2, 1∶4, …, 1∶2n}. While the fixed-size way sampled them using same class distributions. We divided the positives into five folds and did a five-fold cross-validation for evaluating our LPU model. Each fold is selected as testing data in turn and the other 4 folds are for training. The testing fold is added to Unlabeled PPI data 2 for testing . Then we sampled 5 different size negative datasets based on Unlabeled PPI data 1. As shown in m and n in LPU were set to 5 and 4, respectively, with a total of 25 LPU classifiers constructed. The machine learning algorithm used was Support Vector Machine (SVM) implementation in libSVM 3.0. As shown in http://202.118.75.18∶8080/PPINPredictor/).After evaluating the LPU, we selected group-way for predicting PPI. The parameters We chose seven different popular methods to assess the performance of our methods. COACH, CMC, CFinder, Clusterone, MCODE, MCL and IPCA were implemented on the existed popular PPI networks and our new reconstructed networks respectively. We evaluated their performances on the Krogan, DIP and BioGRID PPI networks and compared them with our reconstructed PPI network built upon our purified reliable PPIs and predicted PPI pairs .For the fixed-size way (as shown in the For evaluating our method, we selected different size reconstructed networks for protein complexes detection and compared their performances with other existed popular PPI networks. Besides the above measurement, it also shows the same improvement in GO annotation analysis. We count the number of clusters with p-value http://202.118.75.18∶8080/PPINPredictor/.We also evaluated our predicted PPIs through statistical analysis based on GO annotation. Since interacting proteins are likely involved in similar biological processes, they are expected to have similar functional annotations in gene ontology. Therefore, we measure the functional relevance between any pair of proteins that are connected by an edge using the semantic similarity between the GO terms annotated with the proteins, using a popular method In summary, our method gets a higher quality network for protein complexes detection. This illustrates that our approach of integrating PPI evidence from multiple sources is effective in protein complexes detection. These sources include different types of information, so it is more comprehensive than the existing methods that only consider GO ontology or DDI information. The integration of PPI information from multiple sources enables us to obtain more PPI pairs and enhance true PPI information. From our selected reliable positive PPI supplementing with predicted PPI, it addresses the false negative and false positive problem in the existing PPI network, hence improves the performance for protein complex identification. We plan to evaluate the contribution of each individual source towards protein complex identification in the future.We have integrated PPI information from multiple sources into protein complex identification. The evaluation of our method indicates that incorporating PPI information sources significantly improves the performance of protein complex identification algorithms. Future work includes evaluation of the contribution of each individual source toward protein complex identification. Additionally, we plan to incorporate additional features such as high-level structure information into the protein complex prediction task.


The development of new anti-malarials remains a priority, and the rational selection of putative targets is a key element of this process. Discovery-2 is an update of the original Discovery in silico resource has been developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein properties used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions. Newly added features include drugability measures from ChEMBL, automated literature relations and links to clinical trial information. Searching by chemical structure is also available.An updated Plasmodium falciparum S-adenosyl-L-homocysteine hydrolase (PfSAHH) protein. A short example of a chemical search with pyrimethamine is also illustrated.The updated functionality of the Discovery-2 resource is presented, together with a detailed case study of the The updated Discovery-2 resource allows researchers to obtain detailed properties of proteins from the malaria genome, which may be of interest in the target selection process, and to perform advanced filtering and selection of proteins based on a relevant range of molecular characteristics. Drug resistance to current anti-parasitic compounds has become widespread and is on the increase, including resistance to newer treatments such as artemisinin ,2. WhilePlasmodium falciparum, it contains information for all proteins from Plasmodium vivax, Plasmodium yoelii, Plasmodium knowlesi, Plasmodium chabaudi and Plasmodium berghei and also for the human and mosquito hosts. Protein information includes sequences and annotations from PlasmoDB, Ensembl and VectorBase, functional predictions, gene ontology terms, orthology information, structural information, metabolic pathways, predicted putative protein-ligand interactions, druggability predictions, and literature links. The resource also contains chemical compounds from the ChEMBL database with chemical search functionality and putative ligand-protein prediction information. Protein searches may be performed using accession numbers, keywords or an advanced multi-parameter filtering interface. Chemical searches may be performed using keywords, SMILES strings or chemical structures.Recent approaches have illustrated the value of predicting the association of chemical compounds with putative protein drug targets, especially when the targets of compounds such as the GSK dataset with known activity against the parasite may be extrapolated using protein-ligand interaction databases such as ChemProt ,9. The DThis new implementation of Discovery (version 2) is a complete rewrite of the original Python-based system using Java and NetBeans with the implementation of automated updates based largely on web services. The previous version included information related to sequence features, orthology, ontology terms, structural information, metabolic pathways and protein ligand interactions. The new functionalities are the addition of expression information, literature information from PubMed abstracts, the implementation of druggability predictions from DrugEBIlity where available, the inclusion of malaria-related data from the clinical trials database and the implementation of Wiki-like user annotations. Specific advantages of Discovery-2 include extensive functionality to identify putative associations of proteins with ligands, an advanced chemical structure search interface encompassing the content of the ChEMBL database and an interactive system for refining putative target selections based on data mining of molecular properties. Additionally, the resource also contains data for the mosquito and human hosts, for easy comparative analysis.P. berghei, P. chabaudi, P. falciparum, P. knowlesi, P. vivax and P. yoelii were downloaded from PlasmoDB (currently version 8.1). The Homo sapiens and Anopheles gambiae proteins were downloaded from Ensembl (currently version 64) ), UniProt:Q8IFP1 , UniProt:O96221 (Sec31p putative), UniProt:Q8IKB6 , UniProt:Q8IIC8 , UniProt:Q8IBL5 , UniProt:Q8IAZ3 (Eukaryotic translation initiation factor 3 subunit G) and UniProt:Q8IJY1 . All identified interactions were determined by the use of a two hybrid system that uses transcriptional activity as a measure of protein-protein interactions with all interactions therefore being demonstrated by physical associations.Mus musculus, Homo sapiens and Thermotoga maritima, all showing good BLAST hits. Domain matching of PfSAHH showed no additional matches compared to the BLAST results for the complete protein sequence. Two hundred and ninety-one chemical compounds were identified which have been tested for activity against PfSAHH and its homologs, 819 respective bioactivities involving those compounds were found.Protein-ligand interactions were identified by a BLAST search against the CHEMBL targets database, and were reported in the targets section with the ligands associated in the ligand section Figure . PfSAHH Burkholderia pseudomallei, strain 1710b) had druggability scores of 48% and 45%, respectively.Druggability data for PfSAHH, showed that the molecules with the most significant BLAST domain matches from DrugEBIlity  are not predicted to be druggable, although tractability is indicated. From the BLAST alignments PfSAHH (UniProt:P50250) had 0% overall druggability whilst the top two homologues UniProt:P60176 (SAHH from Mycobacterium tuberculosis) and UniProt:Q3JY79 -6-ethylpyrimidine-2,4-diamine (pyrimethamine) was retrieved as the only hit. The compound information view provided detail about the molecular mass, hydrogen bond donor count, acceptor count, logP, logD, ring count and rotatable bond count, which are all relevant properties for the selection of druggable compounds. The compound passed the Lipinski rule of five . When a Discovery-2 is an update of the original Discovery resource, and is a complete rewrite of the system utilizing an updated database structure and new software technologies, and underlying functionality to perform automated updates using web services. A series of new features have been added such as relations to targets and compounds in ChEMBL, links to druggability information, automated literature links and relations to compounds in clinical trials.in silico and experimental investigation to assess the suitability of the protein as a putative drug target.The resource allows researchers to perform quick searches based on specific proteins, but to also perform mining and filtering of possible drug targets based on a series of molecular properties. Results provide the researcher with information about the protein’s functional annotations, ontology terms, orthology to the different malaria parasite species and host proteins, structural information, pathway information, protein and ligand interaction information, druggability information and literature links. These result categories were selected to optimally assist researchers in selecting proteins for further detailed in silico selection of proteins from the malaria genome to effectively reduce the data space for further detailed experimentation.It is hoped that this resource enables the malaria community to quickly and easily obtain information related to the suitability of a protein as a drug target, and to perform the http://discovery.bi.up.ac.za.Users can access the Discovery-2 resource at The authors declare that they have no competing interests.FJ is the PI and grant holder. Core functionality was developed by JS. The protein-related functionality was developed by JS and PM. The chemical-related functionality was developed by MS and JS. Web-services and scoring was developed by JS, MLG and PB. All authors contributed to the writing of the manuscript. All authors have read and approved the final manuscript.


Computational Structural Bioinformatics Workshop (CSBW) was held in Philadelphia on October 4, 2012. This issue includes eleven papers selected from the work presented at the CSBW of 2012. In "Four-body atomic potential for modeling protein-ligand binding affinity: application to enzyme-inhibitor binding energy prediction", a predictive model of free-energy was built to evaluate the binding between a protein and a ligand. In "Unbiased, scalable sampling of protein loop conformations from probabilistic priors", a new Markov chain Monte Carlo algorithm was proposed to generate unbiased conformations of closed protein loops from probabilistic priors. In "Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce", authors demonstrate an enhanced method by cutting a long RNA sequence into smaller chunks at strategically selected points, and distributing the tasks to multiple processors. In "A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction", the authors present an evolutionary search algorithm to obtain a discrete representation of the protein energy surface in terms of an ensemble of conformations representing local energy minima. In "Estimating loop length from Cryo-EM images at medium resolutions", the authors developed a computational geometry method to simplify the points along the skeleton to measure loop length in 3D images. The paper "A conservation and rigidity based method for detecting critical protein residues" presents a method that combines the rigidity and the evolutionary conservation in detection of the critical residues. In "A conservation and biophysics guided stochastic approach to refining docked multimeric proteins", the authors introduce a refinement method that accepts complexes consisting of any number of monomeric units. In "Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method," the authors present a robotics-inspired tree-based method to sample energetically-credible conformational pathways connecting diverse functional states in multimodal proteins. The paper "An aggregate analysis of many predicted structures to reduce errors in protein structure comparison caused by conformational flexibility" applies protein structure prediction algorithms to enhance the classification of homologous proteins according to their binding preferences. In "DINC: A new AutoDock-based protocol for docking large ligands", an enhanced method was demonstrated for docking large ligands. The paper "Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods" presents an efficient approach involving robotics concepts.The rapid accumulation of macromolecular structures presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of macromolecular structures and interactions. The 6th The authors declare that they have no competing interests.