Structural information about these reactions is important for studying the molecular functions of enzymes, however a large number of the 3D interactions are still unknown. Therefore, in order to complement such missing information, we predicted protein–ligand complex structures, and constructed a database of the 3D interactions in reactions. NLDB provides three different types of data resources; the natural complexes are experimentally determined protein–ligand complex structures in PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand, and the ab initio complexes are predicted by docking simulations. In addition, NLDB shows the known polymorphisms found in human genome on protein structures. The database has a flexible search function based on various types of keywords, and an enrichment analysis function based on a set of KEGG compound IDs. NLDB will be a valuable resource for experimental biologists studying protein–ligand interactions in specific reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions.NLDB (Natural Ligand DataBase; URL:  Protein–ligand interactions play key roles in almost all biological processes, ranging from enzyme catalysis to signal transduction. The molecular recognition of a ligand by its host protein requires non-covalent interactions, such as hydrogen and hydrophobic bonding, between molecules. Thus, characteristics of these interactions, including the ligand-binding modes and binding affinities, are valuable information to facilitate the elucidation of molecular mechanisms of ligand recognition to understand molecular functions in vivo.The large-scale structural information for protein–ligand interactions is currently available in the Protein Data Bank , and haThe information about various metabolic pathways and their related reactions has been manually curated and stored in the Kyoto Encyclopedia of Genes and Genomes . In thehttp://nldb.hgc.jp).Due to the importance of the information about 3D protein–ligand interactions for studying the molecular functions of enzymes, it would be valuable to organize and complement the missing structural information of the metabolic pathways with computational approaches. There are some high-quality databases of biologically relevant ligands bound to proteins, such as Binding MOAD  and BioLnatural, analog, and ab initio complexes  prediction of analog complex structures, and (4) prediction of ab initio complex structures  preparation of a list of protein–ligand interactions in enzymatic reactions (2) collection of  preparatAll the reactions in the KEGG REACTION database along with the KEGG IDs for the metabolic pathways, enzymes and compounds related to each reaction were obtained using the KEGG API , 8. In Kfkcombu program [clustalw2 program [The following two clustering processes were then carried out. The first clustering for a set of chemical compounds was done by the chemical similarity scores (Tanimoto coefficients) between two different compounds, calculated by the connected maximum common substructure (C-MCS) search using the  program . In C-MC program . The sec program  with the program , and theapo-protein structures. To make the list (III), proteins with a ratio of missing residues  of more than 10 % were removed from the list (III), and a representative protein structure with the largest sequence length, which precedes structures with the highest resolution, was selected for each reaction on the same list.The two data sets were then merged together, to form three different types of ligand–protein complex lists: (I) a list of known protein–ligand complex structures, (II) a list of known protein-analog complex structures, and (III) a list of known natural complexes were defined as the complex experimentally determined and registered in PDB. We used the word ‘natural’ to distinguish compounds naturally found in vivo from compounds artificially generated in vitro. Note that NLDB deals with only the compounds found in the KEGG REACTION database. According to the list (I), the coordinates of the ligands in complex with proteins were extracted from the PDB files.The analog complexes were protein–ligand complex structures, predicted based on the structures of protein-analog interactions according to the list (II). Firstly, the ideal coordinates of compounds, computed with the CORINA program, which automatically generates high-quality and low-energy 3D coordinates for a small molecule [fkcombu program, which flexibly transforms a target molecule onto a reference molecule and is bundled in the KCOMBU package program for comparison and modelling of chemical structures [The molecule , were domolecule . Secondlructures , 11 . The quality for ab initio complexes is largely dependent on the accuracy of the predictions of the binding sites and the initial conformations of ligands. Thus, we used two different flows to construct complex structures. When homolog information about the ligand-binding sites was available, the target compound was docked onto the binding sites on the target protein using VINA. When homolog information was not available, the target-specific ligand-binding sites and the energetically favorable conformations of a target ligand were predicted using BUMBLE, which predicts them based on known fragment–fragment interactions observed in PDB  Fig. 2II2II-A. BPDB entry ID, molecule name, organism, compound name, KEGG reaction ID, EC number, UniProt accession number, chemical component ID, rs number, OMIM ID and so on, as well as combinations of these keywords. In this function, partial matching is selected by default, so that, for example, the keyword of ‘GMP’ matches not only a chemical ID but also protein names. Perfect matching can be achieved as an option, in this case, for example, the keyword of ‘GMP’ matches only a chemical ID. According to the keywords inputted in the search box of the NLDB top page, the following three result pages will be provided: (1) The first result page is a list of reactions with the data counts of three types of complexes, natural, analog, and ab initio  The third page shows the detailed information of a specific protein–ligand complex structure  Thew’ Fig. C. The daure Fig. . On this UniProt . Other dp-values. In addition, a list of the reactions in each enriched pathway is also shown, in the same table format as that in the first result page of the keyword search.NLDB also provides an enrichment analysis of a set of KEGG compounds. This function enables users to retrieve enriched KEGG pathways with expected NLDB can be used to complement the missing complex structures in chemical reactions as in the following example.bisphosphoglycerate mutase’ in NLDB . In particular, BPGM in the complex with 1,3-bisphosphoglycerate  in R01662, which is the main function of BPGM, to convert X15 to DG2, is unknown in PDB. This missing complex structure would be strongly required, for clarifying the binding mode of the ligand and also for identifying key residues in the reaction. In the case of BPGM with X15, there are no analog complexes in which a ligand similar to X15 binds. In addition, the chemical similarity score between X15 and DG2, based on C-MCS, is 0.43. Thus, the ab initio complex structure was predicted, according to our data construction procedure  is an erythrocyte-specific enzyme, and its main function is to regulate the oxygen affinity of hemoglobin by controlling the synthesis of 2.3-bisphosphoglycerate , which is an allosteric effector of hemoglobin, via a phosphoryl transfer reaction , 20. TheNLDB is a unique, up-to-date database that collects 3D protein–ligand interactions from known structures, and also automatically predicts missing complex structures using reliable, state-of-the-art software programs, in the reactions of the KEGG REACTION database. As far as we know, there are no comparable databases focused on protein–ligand complex structures involved in these reactions.natural, 28,441 analog and 64,204 ab initio complexes, for 3248 KEGG reactions in which 1654 enzymes are involved, and also registers 4379 KEGG reactions in which 3291 enzymes without structural information are involved (As of July 2016). In total, 7627 reactions have been registered in NLDB. Furthermore, 1679 and 2131 entries with variant residues in their binding sites linked to rs number and OMIM ID, respectively, are registered and viewed in the ‘Variants’ page. The former entries are associated with 89 pathways and 367 reactions, and the latter entries are associated with 103 pathways and 354 reactions.NLDB registers 68,551 natural complexes, cover only 19.09 % (1456/7627) and 21.66 % (1652/7627) of all registered reactions, which have at least one known protein structure in a complex with a substrate and a product, respectively, the current coverage of 3D protein–ligand interactions in NLDB is 35.23 % (2687/7627) and 21.19 % (2885/7627) of these reactions, which have at least one known or predicted protein structure in a complex with a substrate and a product, respectively (Table Even though experimentally determined complex structures; i.e., http://nldb.hgc.jp, and will be regularly updated every 3 months.In particular, we believe that these predicted structures with lower binding affinity can provide some insights for experimental biologists studying protein–ligand interactions in specific chemical reactions, in which the 3D structures of the interaction are as yet unknown, and will facilitate breakthroughs in understanding or verifying chemical reactivities at specific ligand-binding sites, as shown in the above example. Furthermore, NLDB will be a starting point for theoretical researchers wishing to undertake more accurate simulations of the ligand-binding affinity and stability in the predicted conformation of a complex, by considering the effects of protein flexibility and water solvation. Therefore, NLDB will be continually improved, for the prediction of more accurate structures involved in reactions and for web interface usability. NLDB is freely accessible at


Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs.Long non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting protein-protein similarity networks (PPSNs). Concretely, we first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods.In this paper, we propose to boost the prediction of lncRPIs by fusing multiple Fusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs. Up to dRNA-protein interactions (RPIs) by using Random Forest (RF) and Support Vector Machine (SVM) classifiers trained with features of protein and RNA sequences. Bellucci et al. [In the literature, there are more and more works that employ machine learning methods to predict the interactions between RNAs/ncRNAs/lncRNAs and proteins. For example, Muppirala et al.  proposedi et al.  proposedi et al.  developei et al.  developeCheng et al.  proposedIn addition, there are some works that identify lncRPIs from the perspective of network. That is, to construct networks by using known lncRPIs and PPIs as well as lncRNA-lncRNA interactions. For example, Yang et al.  first cosimilarity network fusion (SNF) algorithm [In this paper, to boost the performance of lncRPI prediction we propose to fuse multiple protein-protein similarity networks (PPSNs), and integrate the fused PPSN with known lncRPIs to construct a more informative heterogeneous network, on which new lncRPIs are inferred. Concretely, we first use protein sequences, protein domains, GO terms and the STRING database respectively to construct four PPSNs, then employ the lgorithm  to combiIn this section, we introduce the lncRPI data used in our study and present the details of our method.We extracted Homo sapiens ncRNA-protein interactions from NPInter (v2.0)  and HomoThe pipeline of our method is shown in Fig. As most lncRNAs do not show the same pattern of high interspecies conservation as protein-coding genes . To avoiprotein-protein similarity networks (PPSNs).Many sources are available to evaluate the similarity between proteins. In this paper, we calculate protein-protein similarity by using protein sequences, protein domains, protein functional annotations (or GO terms), and PPI confidence data from the STRING database v10.0 . As protpi and pj is sw means the original Smith-Waterman score. By applying this operation to any protein pair of pi and pj, we can obtain their sequence similarity as SS=+nsw)/2.Protein sequences are obtained from the UniProt database . We compti and tj of proteins pi and pj is defined as pi and pj to the total number of terms of pi and pj, which is used as the functional annotation semantic similarity FS of proteins pi and pj.GO annotations are downloaded from the GO database . Semantipi and pj with their domain fingerprints as their similarity DS.Protein domains are extracted from the Pfam database . Each prpi and pj, their similarity is String = confidence_score/999.STRING is a database of known and predicted interactions which currently covers 9643763 proteins from 2031 organisms [As each protein-protein similarity matrix (network) computed above may contain noise, here we fuse these four matrices (network) to get a more informative and reliable matrix (or network). The similarity network fusion (SNF) algorithm  is emploG= where V={ v1,v2,⋯,vn} corresponds to the set of proteins in the network and E corresponds to the set of edges, each of which has a similarity weight. We denote the corresponding similarity matrix as W where W is the similarity between proteins vi and vj. To compute the fused matrix (network) from the four protein similarity matrices, we define a full and sparse kernel on each matrix. The full kernel is a normalized weight matrix P=D−1W, D is a diagonal matrix and P involves self-similarities on the diagonal entries of W, a better normalization is as follows [A PPSN can be represented as a graph  follows : 1\docuvi’s neighbours as Ni and use k nearest neighbors (kNN) to measure the local affinity as follows: We denote protein P carries all information of the protein-protein similarity network and S carries local similarity information of the network. Then, we can do iterative computation as follows: We think that the similarities between a protein and its neighbours are more reliable than the similarities between the protein with remote ones. Through graph diffusion, the similarities can be disseminated to remote proteins. Matrix ith similarity matrix (network) after t (≥0) iterations, Si)( is the kNN matrix of the ith similarity matrix (network). m is the number of PPSNs used, here m=4. As S is the kNN neighbour matrix of P, it contains the most important information of P and also alleviates the noise effect in P. At each iteration, each similarity matrix (network) can get reliable information from the other similarity matrices (networks) and also updates itself with the other similarity matrices (networks). After t iterations, the fused matrix (network) is computed as follows: where Pt after each iteration to ensure the matrix is full rank and each protein is more similar to itself than the other proteins.Note that we normalize matrix With the known lncRPIs and the fused protein-protein similarity network, we build a lncRNA-protein heterogenous network, on which a random walk model HeteSim  is emploS= where A is a set of object types and R is a set of relationships. A lncRNA-protein network is defined as a directed graph G= with an object-type mapping function ϕ:V→A and a edge-relationship mapping function ψ:E→R. Each object v∈V belongs to one particular object type ϕ(v)∈A, and each link e∈E belongs to a particular relationship ψ(e)∈R. The schema S depicts the object types and the relationships existing among object types. For example, a relationship existing from type A to type B, denoted as A and B are termed the source type and target type of relationship R. In this paper, there are two object types: lncRNA and protein, and three possible relationships: lncRNA-protein, protein-protein, and lncRNA-lncRNA. Here, we consider only the former two relationships. An object may be a concrete protein or lncRNA, and two objects can be connected via different paths that have different meanings.Given a schema relevance path along a sequence of object types A1,A2,⋯,Al+1 can be denoted as composite relationship between A1 and Al+1 is denoted as R=R1∘R2∘⋯∘Rl where ∘ denotes the relationship between two object types. For two objects o1 and o2 with a composite relationship R=R1∘R2∘⋯∘Rl, HeteSim iteratively evaluates the relevance score between them as follows: In the heterogeneous network, a O(o1|R1) is the out-neighbours of o1 based on relationship R1, I(o2|Rl) is the in-neighbours of o2 based on relationship Rl, Oi(o1|R1) / Ij(o2|Rl) indicate the ith / jth object in O(o1|R1) / I(o2|Rl), |·| means the size of a set.where lncRNA-protein relationship (lp in short) and protein-protein relationship (pp in short), so we have As we consider only UAB is a normalized adjacent matrix along the row vector between type A and type B based on relationship R. What is more, VAB is the normalized matrix along the column vector, which is the transition probability matrix of R−1. So, we can get VBA. Given a relevance path P=A1A2⋯Al+1. The reachable probability matrix PM for path P is defined as PM in short). PM represents the probability of object i∈A1 reaching object j∈Al+1 based on path P. So the relevance between objects in A1 and Al+1 based on the relevance path P is: For relationship VAB=UBA′ , where \M is the middle position node type of A1 and Al+1. So above equation shows the inner product of matrices of two probability distributions that A1 reaches M and Al+1 reaches M.where o1 and o2 of type A1 and type Al+1, we can get their normalized relevance score: For two instances o1 lies in the matrix where ent}PMPL .Binary, and denote our method under different settings by the setting names, such Seqs, Seqs+Pfam, Seqs+Pfam+Go, Seqs+Pfam+Go+String, etc. Actually, the String case of our method is roughly similar to the LPIHM method [lncRNA-protein-protein (LPP) as their relevance path and achieved better performance than other pathes. In our work, we also choose it as the relevance path.In our experiments, the leave-one-out cross validation (LOOCV) is used to evaluate the proposed method. The baseline is the method proposed by Yang et al.  where PPM method . So esseM method  and the M method . Becausereceiver operating characteristic (ROC) curve is generated for each experimental setting, and AUC (the area under the ROC curve) is calculated, which is widely used in assessing prediction performance and its value falls between 0 and 1. The maximum value 1 means a perfect prediction, and 0.5 means a random guess.To evaluate the prediction performance, the Binary, the results are presented in Fig.Binary and the other colored lines are the ROC curves of our method with different similarity matrices. In Fig.String is better than that of Binary, which shows that weighted PPI network is more helpful than binary PPI network. Moreover, the results of Go, Pfam and Seqs are all better than that of String. This may be because String is less reliable than the other similarity networks.We first compare our method with only one similarity matrix to the baseline method We then compare the performance of our method under different experimental settings, the results are shown in Figs. First, we consider the cases of fusing two different similarity matrices, their corresponding ROC curves are presented in Fig. Then, we check the cases that fuse three different similarity matrices, their ROC curves are shown in Fig. Finally, we consider the case that fuses all four similarity matrices, and present its ROC curve in Fig. Binary and our method under different settings, we give all of their AUC values in Fig. To more clearly compare the baseline method In this paper, we proposed a new approach to predicting lncRPIs by fusing four protein-protein similarity networks, which were computed with protein sequences, protein domains, protein functional annotations of GO, and the PPI confidence scores from the STRING database. The similarity network fusion (SNF) algorithm and the random walk on heterogeneous network model HeteSim were employed. Our experimental results show that the proposed method outperforms the existing method and those cases when using only one protein-protein similarity network. For future work, on the one hand, we will explore other advanced network fusion methods to fuse more available data sources for further boosting the performance of lncRPI prediction; On the other hand, we will include the lncRNA-lncRNA interactions into the prediction procedure.


Trypanosoma brucei genome. Localisations of over 2000 proteins are already available viahttp://tryptag.org. This will be a transformative resource for enabling sophisticated analysis of conserved eukaryotic and parasite specific cell biology.TrypTag is a major resource which will contain the localisation of every protein encoded in the Trypanosoma brucei several years ago TrypTag is a new community resource with the aim of providing the localisation of every protein encoded in the trypanosome genome. This resource grew out of projects in the Gull Lab where we needed to determine hundreds of protein localisations using fluorescent protein tagging to validate proteomic analyses T. brucei, have an exquisitely structured shape with a precisely defined and polarised architecture. Many major organelles  are present in a single copy at a consistent location, and divide through a precise series of morphogenetic events. This means that, in trypanosomes, protein localisation indicates precisely which organelle a protein is associated with, and can be extremely informative for function . Forty percent of T. brucei protein-coding genes are annotated as hypothetical with minimal functional information, and analysis of individual proteins has lagged behind scaled highly parallel technologies like RNAseq, mass spectrometry, etc.Trypanosomes, including T. brucei biology is informative for two main reasons: these parasites are among the most divergent eukaryotes; conserved structures in T. brucei therefore represent eukaryote-wide core biology. Non-conserved structures represent divergent biology, candidates for understanding unique important features for pathogenicity.Understanding TrypTag.org. The resource is currently based on T. brucei TREU927 genome version 5.1 (gene models from August 2013), but will incorporate new genome releases and published gene model changes. Proteins with an annotated signal peptide have been tagged at only the C terminus; all other proteins will be tagged at the N and C termini. No other signal sequences or post-translational modifications  have been used as criteria to exclude tagging of a terminus. This gave a final set of 7168 and 8129 cell lines to generate, respectively . Given our current throughput, we expect this resource to be complete in 2019.Once complete, the resource will include localisation data for all protein-coding genes, excluding variant surface glycoprotein (VSG) genes (the surface protein for antigenic variation) and genes not assigned to a chromosome. Whether a gene is scheduled to be tagged can be viewed on The TrypTag resource comprises image data of more than 200 cells per cell line  and human interpretation and annotation of protein localisation and cellular structures . Cell liThe data are accessible as a community resource in three ways. Firstly, an example field of cells for each cell line  is shown on the TrypTag website, and includes annotation and primer sequence information. Secondly, example cells will be shown on TriTrypDB http://tryptag.org were announced at the BSP Trypanosomiasis and Leishmaniasis Seminar 2016 . Phased additions of data will appear regularly. We anticipate that TrypTag will be a transformative resource for the community. It will allow researchers to rapidly identify or validate localisations of proteins of interest. More importantly, this resource will enable fundamentally new types of analysis. The analysis of large numbers or cohorts of proteins will allow, for example, identification of new signals that target proteins to cellular structures, signals responsible for cell-cycle-dependent localisation and stability, and organelle subcompartments using protein and gene sequence motif analysis tools. Definition of localisation profiles of gene families  and proteins that have been post-translationally modified  will allow determination of their function in spatial terms. Enabling researchers to move from organelle-based proteomics will stimulate development of more advanced analyses such as interactomics. The proteins identified by TrypTag will enable the generation of reporter cell lines for sophisticated functional assays. We are keen to collaborate with the community for these types of analyses. However, we are happy for researchers wanting to use the localisation data for one or two proteins to simply cite the TrypTag website and this publication.The data for the 2000 protein localisations available now at


MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric (k‐cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease‐associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP (http://proteincomplexes.org) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease.Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu. One major strategy to understand this relationship is to study the physical interactions of the proteins responsible for carrying out the core functions of cells, since interacting proteins tend to be linked to similar phenotypes and genetic diseases. Accurate maps of protein complexes are thus critical to understanding many human diseases . These strategies resulted in 23,744 and 26,642 protein interactions for BioPlex and Hein et al, respectively. In contrast, the CF‐MS experiments sampled endogenous proteins in their native state without genetic manipulation, but with only partial purification, relying instead on repeat observation of co‐eluting proteins across samples and separations to increase confidence in the interactions. The resulting 16,655 protein interactions reflect the biases expected for well‐observed proteins, tending toward more abundant, soluble proteins. Additionally, the Wan et al interactome required all interactions to have evidence in at least two sampled metazoan species; thus, only evolutionarily conserved human proteins are represented. As a consequence, none of these three datasets is individually comprehensive; nonetheless, we expect them to present highly complementary, potentially overlapping views of the network of core human protein complexes. There is thus an opportunity to integrate these over 9,000 published mass spectrometry experiments in order to create a single, more comprehensive map of human protein complexes.Over the past year, three large‐scale mass spectrometry‐based protein interaction mapping efforts in particular have greatly expanded the set of known human protein interactions, namely BioPlex . This reinterpretation of AP‐MS experiments using a weighted matrix model substantially increased the amount of overlap among the three interaction networks, which rose to between 10 and 15%, as plotted in Fig To further probe the reason for the limited observed overlap, we next considered whether the spoke model interpretation of the AP‐MS experiments was partly responsible. By only considering interactions between bait proteins and their preys, spoke models are heavily reliant on the baits selected for experimentation, and also ignore evidence for repeated precipitation of intact complexes across baits. Traditionally, spoke models have shown higher accuracy when compared to the alternative full “matrix” model interpretation Fig C Bader . HoweverBased on the notion that considering this large and diverse set of experiments jointly should increase the ability to discriminate between true and false protein interactions, we next asked whether integrating all three large‐scale datasets would outperform the individual networks in terms of identifying true human protein interactions. We employed a formal machine learning framework to combine evidence from the thousands of individual mass spectrometry experiments in the three large‐scale datasets. Our approach was specifically designed to address the limited network overlap described above, using the weighted matrix model to increase interaction coverage while preserving accuracy. We expected the orthogonal techniques employed, CF‐MS and AP‐MS, to complement each other, where CF‐MS captures stable interactions among endogenous proteins in diverse cells and tissues, while AP‐MS captures a large collection of interactions with differing biophysical characteristics. The three datasets also sample very different portions of the human interactome in terms of cell type and bait selection, which we similarly expected to contribute to a more comprehensive map.et al, and Wan et al as well as the new weighted matrix model features, in the form of a negative log hypergeometric P‐value capturing the specificity and extent to which pairs of proteins co‐precipitated across many AP‐MS baits. Rows in the feature matrix represented pairs of proteins and columns represented measured numerical estimates of protein pairs’ interaction potentials based on the different experiments. All protein interaction features were calculated from raw experimental data, and to avoid any circularity, no features trained on our gold standard were used  were included in training. Specifically, Wan et al  includedl et al,  featureset al, et al, et al, k‐cliques as we describe in the A hallmark of protein complexes is that their component proteins should frequently be co‐purified in independent separations and affinity purifications. This trend manifests as densely connected regions of the interaction network, which we sought to identify by applying a two‐stage clustering procedure. In the first stage of clustering, we applied the ClusterOne algorithm . We also observe in Fig Next, during the course of identifying protein interactions and complexes, we withheld a leave‐out set of literature‐curated complexes to serve as a final, fully independent test set. We compared these data to the derived map and to previously published complex maps, using two different comparison measures Fig D and E. et al, et al, Finally, in order to assess the quality of the final map independently of both the test and training set complexes, we further evaluated our complex map with several of the largest remaining available human protein interaction datasets. We observed highly significant overlap with protein interactions from different experimental methods, including yeast two‐hybrid assays , 66 are found in complexes in hu.MAP, but only 12 are found in the synaptic bouton complex. This low recovery of synaptic vesicle proteins suggests that the synaptic bouton complex is not exclusively vesicle‐bound components.We next wanted to consider the possibility that the set of synaptic bouton complex proteins were co‐precipitating due to their enclosure in a membrane‐bound organelle rather than making physical co‐complex interactions. We therefore looked at the expected number of proteins we would find given the purification of a synaptic vesicle. Out of the 131 proteins annotated with the GO term et al, et al, et al, et al, Rather surprisingly, the AP‐MS experiments that support this complex were all performed with HEK293T cells. HEK293T cells were first reported to be derived from human embryonic kidney tissue   for each complex for both hu.MAP and the shuffled complexes. Figure We next evaluated the fraction of complexes with significantly enriched annotations [FDR‐corrected hypergeometric test; g:Profiler . hu.MAP shows substantially higher coverage than the other networks for nearly all high‐level DO terms, covering ~46% of the annotated human disease‐associated proteins.Knowledge that a protein interacts with a disease‐associated protein greatly increases the probability that the first protein is linked to the same disease  , so we determined the subcellular localization of tagged versions of these proteins as a direct experimental test of the map's prediction. We expressed proteins in this complex as GFP fusions in multi‐ciliated cells (MCCs) of embryos of the frog et al, et al, et al, et al, We next focused on the IFT complexes, which link cargos to microtubule motors for transport along ciliary axonemes  protein interactions by removing interactions from the training protein interaction sets that were shared in the test protein interaction sets, such that the sets were fully disjoint. Additionally, 112 pairs in the positive test set were not co‐complex prior to our merge step and were removed.et al, et al, For the analyses of protein complexes, in order to ensure that the test and training sets of complexes were disjoint, we removed entire complexes from the training set which shared any edge with a complex in the test set. In comparing the size distributions (the number of subunits per complex) between the training and test sets, we noticed a skew of larger complexes in the test set likely a result of our conservative approach of removing complexes from the training set. In order to better balance the training and test complex set size distributions, we first randomly split the test set into two and combined one half with the training complexes. We again applied our redundancy removal procedure, removing complexes from the training set which shared any edge with a complex in the test set. Similar to what has been done previously (Havugimana http://proteincomplexes.org).The final pairwise protein interaction training/test sets consisted of 27,665/15,575 and 2,543,855/2,867,914 positive and negative interactions, respectively. The final protein complex training/test sets consisted of 406/264 complexes. The complete lists of training/test interactions and complexes are available at the supporting web site . We excluded HS‐LC (human literature‐curated) and HS‐CC (human co‐citation) evidence codes to remove circularity in the training process. HumanNet features were only included for pairs of proteins that had substantial co‐fractionation evidence, specifically co‐fractionation measure greater than 0.5 in at least two species. This resulted in 35,028 pairs with HumanNet evidence . The additional AP‐MS fly feature, HGSCore value, was downloaded from supplemental table S3 in Guruharsha et al  of two proteins being observed together at random across all AP‐MS experiments, as calculated using the hypergeometric distribution as previously described  as well as a −1*log to form the final estimate of protein complexes.To objectively optimize the choice of clustering parameters, we performed the two‐stage clustering process for each combination of parameters, varying Guiding and assessing the accuracy of the reconstructed complexes requires comparison with a gold standard set of known complexes. However, comparing sets of complexes to known complexes , is ill defined due to the problem of first deciding which sets should be compared and second, to the incomparable nature of specific matches. For instance, given two non‐overlapping complexes, one of size 3 and one of size 20, it is difficult to assess whether an exact match of the complex of size 3 should be given more weight than a partial match of the complex of size 20 . Many complex–complex comparison metrics  through n, where n is the size of the largest predicted complex. The approach allows for precision and recall values to be calculated unambiguously (because a clique is either present or absent in a given set) for each clique size, k, and averaged to determine a single performance metric . An important feature of the k‐clique approach is that it focuses the evaluation on protein interactions, rather than the proteins themselves, providing a unique perspective on set comparisons. In addition, while other approaches suffer from evaluating each complex individually and often require a cluster reduction step in which similar clusters are combined to avoid potential skew, for example, as caused by prediction of sub‐complexes of larger complexes, the k‐clique approach compares complexes on a global level and naturally deals with potential skew by only evaluating on the unique set of cliques for all predicted complexes. Finally, there is no need to determine a unique mapping between each predicted and benchmark complex, thus avoiding mapping‐induced ambiguity.In order to more systematically address these issues, we invented a new class of similarity metrics, C be a set of predicted complexes {c1, c2,…, cn} and D be a set of gold standard complexes {d1, d2,…, dm}, where ci and dj are an individual predicted complex and gold standard complex, respectively. Let QD be the set of protein identifiers in D  and Pk represents the powerset of a given size . Ak , false positives (FPk), and false negatives (FNk), respectively, for a given clique size k.Definitions of Pk) and recall (Rk), and equation F‐measure (Fk) as the harmonic mean of Pk and Rk.Equations F‐measure   was amplified from a Xenopus cDNA library and subcloned into the vector pCS10R (derived from pCS107 expression vector) fused with C‐terminal GFP. The human CLUAP1 open reading frame was obtained from the Human ORFeome collection V7.1 and subcloned into the pCS10R‐mCherry vector. Capped mRNAs were synthesized using mMESSAGE mMACHINE (Ambion). mRNAs and MOs were injected into two ventral blastomeres or two dorsal blastomeres at the 4‐cell stage to target the epidermis or the neural tissues, respectively. We used each mRNA or MO at the following dosages: ANKRD55 MO , JBTS17 MO (20 ng), IFT52 MO (40 ng), ANKRD55‐GFP mRNA (75 pg), ANKRD55 mRNA , membrane RFP mRNA (50 pg), and mCherry‐CLUAP1 (100 pg).Morpholino antisense oligonucleotides (MOs) were purchased from Gene Tools. The Xenopus embryos injected with ANKRD55‐GFP and mCherry‐CLUAP1 mRNA were anaesthetized with 0.005% benzocaine at stage 26. High‐speed in vivo imaging was acquired on a Nikon Eclipse Ti confocal microscope with a 63×/1.4 oil immersion objective at 0.267 s per frame. Kymographs were calculated using Fiji , and KIAA1328, WDR90, TTLL5 cDNAs were PCR‐amplified from the library using the following primers:WDR90F: caccATGGCTGGAGTCTGGCAGWDR90R: TGAATTCTGAATGTCCCACACTTLL5F: caccATGCCCGAAATGTTGCCTTLL5R: TTTTCTTTGCCCTTTACTGTCGAKIAA1328F: caccATGGATTTACAGAGGCAGCAAGKIAA1328R: ACAAATGAAGAAGATCTCCTCTAACATCXenopus embryos at the 4‐cell stage and imaged at stage 27. Sample sizes were chosen to obtain representative images.PCR products were subcloned into Gateway ENTRY clones . Destination vectors were modified from destination vector Pcsegfpdest (a gift from the Lawson laboratory) by inserting the α‐tubulin promoter between the SalI and BamHI sites. Fluorescence protein‐tagged expression plasmids were constructed using the LR reaction on entry clones and destination vectors with the Gateway LR Clonase II Enzyme mix (Life Technologies). Expression plasmids (40 pg) with centrin‐BFP mRNA (100 pg) were co‐injected into the ventral blastomeres of Xenopus embryo manipulations and injections were carried out using standard protocols. All experiments were performed following animal ethics guidelines of the University of Texas at Austin, protocol number AUP‐2015‐00160.https://thebiogrid.org/dataset/marcotte2017) and can be searched interactively or downloaded at http://proteincomplexes.org. Supporting computer code for the full protein interaction mapping and analysis pipeline is available at: https://github.com/marcottelab/protein_complex_maps_public.Protein interactions and complexes have been deposited in the BioGRID database Stark  (https:/KD and EMM designed project. KD developed code and performed data analysis. CL, RLH, FT, YM, and JBW designed and performed validation experiments. BB contributed code. CDM performed analysis. KD, JBW, and EMM drafted manuscript. All authors discussed results and contributed edits.The authors declare that they have no conflict of interest.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Table EV4Click here for additional data file.Table EV5Click here for additional data file.Movie EV1Click here for additional data file.Movie EV2Click here for additional data file.Movie EV3Click here for additional data file.Review Process FileClick here for additional data file.


H) of HNF1A hinder binding to the DNA, thereby leading to a dysfunctional protein. Missense mutations of the HNF1A were retrieved from public databases and subjected to a three-step computational mutational analysis to identify the underlying mechanism. First, the pathogenicity and stability of the mutations were analyzed to determine whether they alter protein structure and function. Second, the sequence conservation and DNA-binding sites of the mutant positions were assessed; as HNF1A protein is a transcription factor. Finally, the biochemical properties of the biological system were validated using molecular dynamic simulations in Gromacs 4.6.3 package. Two arginine residues (131 and 203) in the HNF1A protein are highly conserved residues and contribute to the function of the protein. Furthermore, the R131W, R131Q, and R203C mutations were predicted to be highly deleterious by in silico tools and showed lower binding affinity with DNA when compared to the native protein using the molecular docking analysis. Triplicate runs of molecular dynamic (MD) simulations (50ns) revealed smaller changes in patterns of deviation, fluctuation, and compactness, in complexes containing the R131Q and R131W mutations, compared to complexes containing the R203C mutant complex. We observed reduction in the number of intermolecular hydrogen bonds, compactness, and electrostatic potential, as well as the loss of salt bridges, in the R203C mutant complex. Substitution of arginine with cysteine at position 203 decreases the affinity of the protein for DNA, thereby destabilizing the protein. Based on our current findings, the MD approach is an important tool for elucidating the impact and affinity of mutations in DNA-protein interactions and understanding their function.Maturity-onset diabetes of the young type 3 (MODY3) is a non-ketotic form of diabetes associated with poor insulin secretion. Over the past years, several studies have reported the association of missense mutations in the Hepatocyte Nuclear Factor 1 Alpha (HNF1A) with MODY3. Missense mutations in the POU homeodomain (POU GCK and HNF1A genes , 14HNF1Aromosome q24.2, spn and DNA.http://www.hgmd.org/), the majority of the mutations detected in individuals with MODY3 were missense, 39 missense and five nonsense mutations that are located in the DNA-binding domain (POUH). Most of the mutations were observed in exons 1, 4, 6 and 10.Generally, DNA-protein interactions have pivotal roles in important cellular mechanisms, such as transcription and gene regulation . DNA-binin silico prediction methods. The computational workflow described here for studying the structural and functional impacts of HNF1A missense mutations on DNA-protein interaction sites can be easily implemented in a pipeline for any other DNA-protein interactions  to DNA, such as the ChIP (chromatin immuno precipitation) assay , EMSA  as deleterious , predicted four missense mutations, R131Q, R131W, R159W, and R203C, having destabilizing effects on the protein  predicted to be the most deleterious  by all t protein .H- R203C, R203H, K205Q, R263C, R272C, R272H, and K273E) were described as DNA-binding sites by both programs. As a next step, all 13 DNAbinding sites were cross checked according to their potential deleteriousness, i.e., pathogenicity, and destabilizing effects reported by the various in silico prediction methods , we observed a similar number of salt bridges formation in R131W and R131Q mutant complexes and the native complex. However, in the R203C mutant complex, only 5 salt bridges formation was observed .Triplicate runs of 50ns MD simulations using Gromacs were performed to understand the stability, functionality, and folding variations between the native and mutant complexes at an atomic level. Trajectory files obtained from the triplicate runs of the native complex were utilized to construct a Block-based root-mean-square deviation (RMSD) plot. This analysis allows us to compare and identify the most highly converged trajectory of the native complex for further analysis . Among tThe radius of gyration (Rg) was utilized to measure and understand the compactness of the protein complexes . The lowEssential dynamics analyses were elucidated to measure and understand the differences in motion between the native and mutant complexes. The g_anaeig and g_covar utilities from Gromacs were used to perform a PC analysis to reveal the changes in the motion patterns of the protein complexes . A porcuThe folding pattern of a protein is crucial for its proper function. FEL analyses helped us to determine whether the protein favors a folded or unfolded state, based on the obtained Gibbs free energy. Proteins with higher Gibbs free energy, which is shown as blue spots, favor an unfolded state, and a decrease in the Gibbs free energy, which is shown as red spots, favors a folded state in the FEL analysis. Among the three mutant complexes, R203C showed more blue spots (which correspond to increased Gibbs free energy) compared with the native protein ,whereas Most of the molecular interactions depend on the basic phenomenon of electrostatic potential. A comparative analysis of the electrostatic potential was conducted using CCP4mg. This visualization allowed us to understand the different electrostatic points potentials observed in the native and mutant complexes. In Missense mutations have long been known to inhibit protein activity, and tend to occur in DNA-binding regions of the protein affecting the DNA-binding affinity –20. HNF1in silico prediction methods in prioritizing deleterious mutations associated with simple and complex diseases , a higher RMSD is associated with reduced stability, consistent with our observation that the R203C mutant complex exhibited the largest deviation pattern among the three mutant complexes  with cysteine (C) at position 203 decreases the affinity of the proteinfor DNA. Although the destabilizing effect of this mutant on the DNA-protein complex has been previously discussed , 71, 72,activity . Substitactivity –78. In aactivity , 79, 80.Transcription factors play pivotal roles in various cellular mechanisms, including regulation of cell function, growth, and differentiation, The detailed molecular structure analysis of the HNF1A presented here emphasizes the importance of the two arginine positions R131 and R203 for the HNF1A stability and function, the mutants R131Q and R203C were shown to be deleterious for the protein function and compromise its binding affinity, however our data suggested that the R203C mutant was the most deleterious leading to the loss of the protein binding affinity. The understanding of the detailed molecular structure of the mutations in HNF1A that cause MODY3 is expected to serve as a platform for developing therapeutic approaches for patients with MODY3 and drug discovery for treating diabetes, and maycreate a path toward personalized medicine for diabetic patients.HNF1A gene were retrieved from the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/), UniProt (http://www.uniprot.org/), and HGMD databases (http://www.hgmd.org/) to understand their functional annotation. Further information about the impact of the mutations and their disease associations was collected based on in vivo and in vitro experiments recorded in OMIM (http://www.ncbi.nlm.nih.gov/omim/), PubMed (http://www.ncbi.nlm.nih.gov/PubMed/), and UniProtKB (http://www.uniprot.org/).The mutations associated with the in silico tools was used to predict the pathogenicity of each mutation collected from the above mentioned databases. SIFT (Sorting Intolerant from Tolerant) (http://sift.bii.a-star.edu.sg/) [http://snps-and-go.biocomp.unibo.it/snps-and-go/) predict the impacts of coding mutations on protein function [http://genetics.bwh.harvard.edu/pph2/) predicts the influence of a substitution on the structure and function of a protein using its physical properties. [http://www.cdfd.org.in/HANSA/) [http://snps.biofold.org/phd-snp/) [http://fathmm.biocompute.org.uk/) is an HMM (Hidden Markov Model) algorithm-based tool that is capable of predicting the functional consequences of coding missense mutations [http://www.pantherdb.org/about.jsp) [http://agvgd.iarc.fr/agvgd_input.php) [http://www.broad.mit.edu/mpg/snap/) [http://structure.bmc.lu.se/PON-P2/) tool is a machine learning approach that predicts the harmful effects of mutations by utilizing the evolutionary sequences and the biochemical and physical properties of the protein [A set of 10 edu.sg/)  and SNPsfunction . PolyPheperties. . HANSA (/HANSA/)  and PhD-hd-snp/)  are supputations . PANTHERout.jsp)  actuallyput.php) . SNAP isg/snap/) . The PON protein .in silico prediction tools. I-Mutant 2.0 (http://folding.biofold.org/i-mutant/i-mutant2.0.html) [www.igb.uci.edu/servers/servers.html) [http://bleoberis.bioc.cam.ac.uk/duet/stability) [The relationships between the mutations and their effects on protein stability were calculated using three .0.html) , based ors.html) . In addiability) . This sehttp://bioinfo.ggc.org/bindn+/) is an SVM-based approach that utilizes three features of the sequence, namely, the side chain pKa, hydrophobicity index, and molecular mass of the given amino acid, to make its prediction. The input for the tool is in FASTA format, and the output is depicted as positive (+) and negative (-). The positive (+) sign represents the presence of binding sites, and the negative (-) sign represents the absence of binding sites [http://www.biochem.ucl.ac.uk/bsm/pdbsum) aims to provide a summary of the molecules that are confined in the PDB entry [Transcription factors are the set of proteins that bind to a specific region of the DNA to up-regulate or down-regulate a specific transcription process. The binding of the protein to the DNA depends on highly specific regions or residues present in the protein. The BindN+ tool was used to identify the protein-DNA interaction. BindN+ (ng sites . The obtDB entry .http://consurf.tau.ac.il/). This web-based algorithm predicts the functionally important regions of a protein by estimating the degree of conservation of amino acid sites based on homology [The presence of conserved regions among the homologous sequences of the protein was investigated using the ConSurf web server (homology . The grahttp://www.uniprot.org/) database describes the protein and provides a detailed explanation and the FASTA sequence of the protein. The sequence of the human HNF1A (P20823) protein with a length of 631 amino acids was retrieved. The transcript with the ref sequence NP_001293108 was used in the study (http://www.ncbi.nlm.nih.gov/protein/NP_001293108.1). The X-ray structure (PDB: 1IC8) with a resolution of 2.8 Å was obtained from the PDB database [The UniProtKB . Along with hydrogen bond analysis, we also predicted the number of salt bridges that were formed in the molecules using the ESBRI server (http://bioinformatica.isa.cnr.it/ESBRI/). The server works on a CGI script written in Perl language that elucidates the interactions present between the oppositely charged groups and recognizes at least one Asp or Glu side-chain carboxyl oxygen atom and one side-chain nitrogen atom of Arg, Lys or His within a distance of 4.0 Å [CoCoMaps (bioCOmplexes Contact MAPS) visualizes the interface between the chains as presented in the X-ray or NMR structures . The inpof 4.0 Å .MD simulations and energy minimization were conducted using the Gromacs 4.6.3 software package with the force field CHARMM 27 . The natS1 Fig(A) Workflow explaining the different databases used to collect the protein and mutation information. (B)Workflow explaining the process used to select the deleterious mutations (‘N’ denotes number of missense mutations).(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 FigColor scheme: native complex (black), R131W complex (red), R 131Q complex (green), and R203C complex (blue).(TIF)Click here for additional data file.S4 FigColor scheme: native complex (black), R131W complex (red), R131Q complex (green), and R203C complex (blue).(TIF)Click here for additional data file.S5 Fig(A) native, (B) R131W, (C) R131Q, and (D) R203C mutant complexes.(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development. Accurately predicting how a protein folds, functions and interacts with other molecules, remains one of the most critical problems in bioinformatics. An entirely algorithmic solution to elucidate protein folding would be ideal, and a folding process of some form must exist in nature as postulated in Levinthal’s paradox . HoweverDetermining protein structures and their function from sequence data is an essential part of modern molecular biology and leads onto the problem of determining the protein interacting partners and quaternary structural state. A thorough review of scientific literature and progressively reliable computational predictions have resulted in the formation of massive databases of protein interaction data. These databases have become an essential resource for new predictive methods . The preVarious rapid bioinformatics tools are available for the inherently complex process of PPI prediction, yet experimental methods are still considered more reliable due to the current accuracy limitations of computational methodologies. However, determining the full atom coordinates for a protein complex or quaternary structure, for example using X-ray crystallography, is very costly and time-consuming and the crystallisation process itself can be hit and miss. Alternative experimental techniques for PPI detection are more reliable, but they do not provide detailed atomic information of quaternary structures. Therefore, there is a growing need for techniques that aim to fuse sparse experimental data with predictive data to aid with the modelling of complexes.As per the statistics from IntAct , an openTandem affinity purification (TAP) is a biochemical PPI detection method, which accounts for 14.1% of the experimental methods as per IntAct . TAP is If antibodies against the target protein are available, Co-immunoprecipitation (Co-IP) is considered to be a simple technique to study PPIs in vivo . In thisYeast two-hybrid is a high throughput PPI detection method, which accounts for 9.2% of the PPI detection methods submitted in IntAct . In thisThe pull-down method, which involves the use of affinity purification, is similar to CoIP in methodology, i.e., uses affinity ligand to capture interacting proteins. This method uses “bait”, purified and tagged protein in place of immobilised antibodies in CoIP . InteracIn protein chip technology, expressed purified and screened proteins are printed onto a chip using a microarrayer as discrete spots. A solution containing labelled proteins is incubated with the chip, which is then washed and the position of the labels indicates the interaction between proteins (protein on the chip and protein from the solution) . High sid > ~100 μM) and hence is a method preferred to study PPI in excellent detail [Even though X-ray crystallography is the most popular method of protein structure determination, NMR spectroscopy has played a vital role where protein complexes have disorders and when challenging to obtain a crystal. NMR is handy in weak detection PPIs , which involves simultaneous analysis of ECs, derived from multiple sequence alignments (MSAs), with NMR chemical shift, nuclear Overhauser effect (NOE) and residual dipolar coupling (RDC) data. The results with this EC-NMR method were said to be more accurate and provided complete structural information compared to the ones obtained with sparse NMR data or ECs alone .In 2007, Latek, Ekonomiuk, and Kolinski combined de novo modelling with limited experimental data and demonstrated that limited experimental data, such as chemical shifts, are sufficient for three-dimensional structure determination if the data are accurate. It was also suggested that weak quality chemical shift data can be improved by the application of additional sparse experimental data .Recently, an integrative modelling approach was used to determine the 3D model of the entire Mediator complex. Robinson et al. combined data from X-ray crystallography, homology modelling, and cryo-electron microscopy with the results from chemical cross-linking and mass spectrometry to produce the model. All Mediator subunits, subunit interfaces and some secondary structural elements were revealed in the elucidated model .Cross-linking is a biochemical PPI detection method, which is suitable for detecting weak interactions. As the reagent used in this technique detects interactions which are not in direct contact, the interactions detected with this method need to be assessed independently. Combined with mass spectrometry, chemical cross-linking can be used to study PPIs. In purified protein complexes they are used to study spatial relationships. Both transient and stable interactions can be studied using the information from the controlled arrays produced using new classes of photo-activated reagents ,77.The requirement of only a small amount of sample and the lack of a requirement for crystallisation has made cryo-electron microscopy (cryo-EM) a method preferable over other techniques ,79. StatExperimental methods have proven invaluable to further our understanding of PPIs. However, each method has its disadvantages. Also, the relatively high cost, time and expertise requirements of in vitro techniques suggest that in silico methods will need to play an increasingly important role for understanding protein–protein interactions at the atomic level.There are two primary categories of methods for modelling protein–protein complexes: (1) homology or template-based modelling and 2) ab initio docking, or template-free modelling. Template-based modelling relies on the existence of appropriate templates in protein databases, and docking approaches can be broadly split into rigid body and flexible solutions. Docking guided by restraints obtained from experimental procedures could be considered a third category and is often referred to as “hybrid modelling”. However, the computational pipelines used in any integrative approach would usually comprise either template modelling or docking, or both. For protein structure prediction, ab initio methods have gained importance because the number of quaternary structural models available in the Protein Data Bank (PDB)  limits t ab initiThe increase in the rate of sequencing of biological macromolecules means that ab initio methods for determining structures and interactions of proteins are becoming more relevant. Protein docking is the computational determination of protein complex structure from individual protein structures. When using docking software, there are two classes of problems: bound docking, where the complex structure is known and the receptor and ligand are pulled apart then reassembled, and unbound docking, where individually determined proteins structures are used. Docking is computationally expensive, and the accuracy can decrease rapidly when the protein chains undergo significant conformational changes upon binding. A brief description of the variety of different methods utilized in homology modelling and docking follows, the methods have been chosen to be representative of some common successful methods as well as some less common approaches.RosettaDock is a soft body type of protein interaction prediction software and considers backbone or side-chain flexibility, which is advantageous regarding obtaining structures of protein complexes with high-resolution accuracy. The protein docking server predicts the structure of protein complexes given the compositions of the individual components and a relative binding orientation. The server identifies low-energy conformations of a protein–protein interaction near a given starting configuration by optimising rigid-body orientation and side-chain structures . RosettaZDOCK, which is presently one of the most common rigid-body docking engines, uses a scoring function that includes shape complementarity, electrostatics and a heuristic potential called atomic contact energy ,89,90. TRigid body docking as a method produces a large number of docked conformations with favorable surface complementarity usually followed by ranking using energy scoring. The fast Fourier transform (FFT) algorithm uses a correlation approach  and systMEGADOCK  uses a KFRODOCK projects the interaction terms of a potential protein complex into 3D grid-based potentials using spherical harmonics approximations to accelerate the search; this is itself an extension of the FFT alogrithm. The binding energy of the complex as it is formed is approximated as a correlation function composed of van der Waals, electrostatics and desolvation energy terms  and thisM-ZDOCK was developed for predicting the structure of cyclically symmetric multimers based on the construction of an unbound  monomer. Using a grid-based FFT approach purely symmetrical multimers are generated and searched for the highest quality structure rather than creating predicted structures with ZDOCK ,89 and fThe ClusPro  docking PatchDock  and LZerThe LZerD  softwareQuality assessment is vital to structure prediction as it allows us to reduce a potentially considerable number of predicted complexes to a smaller subset for further ranking or examination. Energy based scoring of protein complexes is often carried out by docking programs to filter predictions and takes into account several binding terms: van der Waals energy and shape complementarity; desolvation energy and hydrophobicity; electrostatic interaction energy; translational, rotational and vibrational free energy changes. The assumption made by energy scoring is that proteins will find the lowest energy configuration when forming complexes. However, this is not always true, as some complexes observed by experimental methods have not taken up the lowest possible energy pose. Furthermore, these energy functions provide relative accuracy estimates, with only moderate power in adequately ranking models. Further, when one tries to compare models obtained from different methods, their associated energy scores are often not directly comparable. Therefore, accurate quality estimation methods that are independent of energy scoring are essential for protein structure prediction tools to fulfil their potential as useful techniques for biologists .Consensus clustering can be used to determine the quality of predicted protein complexes. Docking predictions for pairs or groups of protein chains are clustered based on a scoring system that compares the similarity of the predictions using either root mean square deviation (RMSD) of the distance between the atoms of the complex in angstroms or the TM-score  of deterAlthough many different forms of tertiary model evaluation exist; such as those implemented in ModFOLD6 , ProQ3 ,115 and The development of methods for the prediction of protein-ligand binding sites and function prediction has been supported in recent years as a direct result of community-wide prediction experiments, such as CASP , CAPRI  and CAMECASP1, the first large-scale experiment to assess protein structure prediction methods, was held in 1994. It consisted of three parts: the collection of targets for prediction from the experimental community, the group of forecasts from the modelling community, and the assessment and discussion of the results. Information was solicited from X-ray crystallographers and NMR spectroscopists on structures about to be solved. Protein–protein interaction prediction was not added to CASP until CASP11 in 2014, however, in the years since, the area has proliferated and was a much more significant part of CASP12 in 2016. CASP has a large number of target sequences for prediction and runs over several months, culminating in a conference to review the experiment. In In the CASP 12 experiment the McGuffin group predicted tertiary structures using IntFOLD ,122,123 The ultimate goal of structure prediction is to provide insights into biological functions. However, it is difficult to quantify and benchmark the utility of protein structure prediction for functional inference . The bioDuring CASP11, it was seen that the model accuracy was improved when using NMR simulated sparse data. Dramatic improvement was seen in GDT_TS score  for all data assisted models, where GDT_TS was better by >17 GDT_TS units on average compared with the CNS  results (those models unassisted by data) .CAMEO continuously applies quality assessment criteria established by the protein structure prediction community. Since the accuracy requirements for different scientific applications vary, there is no “one fits all” score. CAMEO, therefore, offers a variety of scores—assessing different aspects of a prediction  to reflect these requirements . CAMEO oCAPRI is a blind prediction experiment, and its targets are unpublished crystal or NMR structures and are submitted to CAPRI by their authors in a confidential process to maintain the validity of the experiment. Contributor predictor groups are given the atomic coordinates of two proteins that are known to have relevant biological interactions. The target interactions are modelled with the help of the coordinates and other publicly available data and then submit sets of ten models for assessment. Also, the predictors are invited to upload more massive sets that are communicated to scorer groups who evaluate and rank them and make a separate ten-model submission. After the prediction round is finalised, the CAPRI assessors equate the submissions to the experimental structure and evaluate the models on criteria that depend on the geometry and biological importance of the predicted interactions . CAPRI hA large number of high throughput experimental techniques and computational programmes are now available for both PPI detection and modelling quaternary structures. Integrating this information together with sparse experimental data, or hybrid modelling, will help biologists to elucidate the complex networks of PPIs, which are intrinsic to every cellular process. A plethora of methods are being explored for in silico quaternary structure prediction and the refinement and quality assessment of such predictions. In docking, methods based on the FFT algorithms are currently prevalent although the field is developing alternative methods. Energy scoring is common to the majority of methods as the main ranking criteria and clustering techniques are also sometimes utilized. However, there is a clear need for the development of generic quality assessment methods for evaluating predicted quaternary structures, if models are to be more seriously adopted by the wider biology community. The combination of predictive and experimental methods and the continued focus of the CASP, CAPRI and CAMEO on quaternary structures, will be essential for driving our continued progress.


Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. On the other hand, the number of solved protein structures is rapidly increasing while the number of unique protein folds has reached a steady number, suggesting more structural information is being accumulated on each fold. Deep learning neural network is a powerful method to learn such big data set and has shown superior performance in many machine learning fields. In this study, we applied the deep learning neural network approach to computational protein design for predicting the probability of 20 natural amino acids on each residue in a protein. A large set of protein structures was collected and a multi-layer neural network was constructed. A number of structural properties were extracted as input features and the best network achieved an accuracy of 38.3%. Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta. Moreover, the predictions from our network show ~3% higher sequence identity than a previous method. Results from this study may benefit further development of computational protein design methods. Over the past three decades, remarkable successes have been achieved in protein design, in which some of the designs were guided by computational methods. Examples of some recent successful computational protein designs include novel folds2, novel enzymes4, vaccines6, antibodies8, novel protein assemblies13, ligand-binding proteins15, and membrane proteins18. Comprehensive coverage of the protein designs until 2014 is provided by Samish19, and more recent ones are reviewed elsewhere23. In general, the input of computational protein design is the backbone structure of a target protein (or part of a target protein). Through computational sampling and optimization, sequences that are likely to fold into the desired structure are generated for experimental verification. The scoring function usually contains physics-based terms such as van der Waals and electrostatic energy as well as knowledge-based terms such as sidechain rotamer24 and backbone dihedral preference obtained from statistics of protein structures26. In many cases, the sequences from computational design are subject to further filtering by considering various factors such as shape complementarity9 and in silico folding free energy landscape28, where human experience and familiarity with the designed protein play an important role, indicating that there is a gap between current design methods and fully automatic designs.Proteins perform a vast number of functions in cells including signal transduction, DNA replication, catalyzing reactions, etc. Engineering and designing proteins for specific structure and function not only deepen our understanding of the protein sequence-structure relationship, but also have wide applications in chemistry, biology and medicine29 with a yearly increase of ~10,000, but the number of unique folds has not changed in the past few years, suggesting more data are accumulated on each fold, and therefore statistical learning and utilizing the existing structures are likely able to improve the design methods31. Recently, two statistical potentials for protein design have been developed33, and the ABACUS potential34 has been successfully used in designing proteins35. While these statistical potentials have a physical basis, machine learning especially deep-learning neural network has recently become a popular method to analyze big data sets, extract complex features, and make accurate predictions36.On the other hand, while the number of known protein structures is increasing rapidly, the number of unique protein folds is saturating. As of July 2017, there are ~132,000 structures in the protein data bank (PDB)37, language processing38, and game playing39. Particularly in computational biology/chemistry, it has been used in protein-ligand scoring42, protein-protein interaction prediction43, protein secondary structure prediction49, protein contact map prediction52, and compound toxicity54 and liver injury prediction55, among others56. In many cases it shows better performance than other machine learning methods. The advantage of using deep neural network is that it can learn high-order features from simple input data such as atom coordinates and types. The technical details such as network architecture, data representations vary from application to application, but the fundamental requirement of applying deep neural network is the availability of a large amount of data. With the aforementioned rich protein structure data available, it is promising to apply deep neural network in computational protein design. Zhou and coworkers have used the neural network approach to tackle this problem and developed the SPIN method to predict the sequence profile of a protein given the backbone structure57. The input features of SPIN include φ, ψ dihedrals of the target residue, sequence profile of 5-residue fragment derived from similar structures (the target residue and four subsequent residues), and a rotamer-based energy profile of the target residue using the DFIRE potential58. SPIN was trained on 1532 non-redundant proteins and reaches a sequence identity of 30.3% on a test set containing 500 proteins. This sequence identity is at the lower boundary of homologous protein59 and is not sufficient to improve protein design significantly.Deep-learning neural network, as a machine learning technique, is becoming increasingly powerful with the development of new algorithms and computer hardware, and has been applied to learning massive data sets in a variety of fields such as image recognitionIn this study, we applied deep-learning neural networks in computational protein design using new structural features, new network architecture, and a larger protein structure data set, with the aim of improving the accuracy in protein design. Instead of taking the whole input structure into account, we use a sliding widow method that has been used in protein secondary structure prediction, and predict the residue identity of each position one by one. We consider the target residue and its neighboring residues in three-dimensional spaces, with the assumption that the identity of the target residue should be compatible with its surrounding residues. We collected a large set of high-resolution protein structures and extracted the coordinates of each residue and its environment. The performance of the neural network on different input setups was compared, and application of the network outputs in protein design was investigated.α-Cα distance, and feed their input features to a neural network that consists of an input layer, several hidden layers and a softmax layer as output. The output dimension of the softmax layer is set to 20 so that the 20 output numbers that sum to one can be interpreted as the probabilities of 20 residue types of the target residue. This network is named residue probability network hereinafter . Instead of predicting the residue types of all positions in the input protein simultaneously, we consider each target residue and its neighbor residues . In the simplest case, we consider a target position and its closest neighboring residue determined by Cter Fig. . Such a ter Fig. . The outter Fig. , which cα-Cα distance, cos and sin values of backbone dihedrals φ, ψ and ω, relative location of the neighbor residue to the target residue determined by a unit vector from the Cα atom of the target residue to the Cα atom of the neighbor residue, three-type secondary structures, number of backbone-backbone hydrogen-bonds, and solvent accessible surface area of backbone atoms (see Methods). To train the neural network, we collected high-resolution protein structures from PDB using filtering conditions including structure determination method, resolution, chain length, and sequence identity (see Methods). Briefly, three data sets are prepared based on three sequence identity cutoffs  to remove homologous proteins. For each of these data sets, each residue and its N  closest neighbor residues based on Cα-Cα distance are extracted as a cluster. These clusters are randomly split into five sets for five-fold cross-validation , and precision is the percent of predictions that are correct. Pro and Gly have higher recall and precision than other residues with Pro achieves 92.1% recall and 62.7% precision Fig. , althougmap Fig. . The ami-K accuracy: if the native amino acid is within the top-K predictions (K amino acids that have the highest probabilities), the prediction is considered correct. The top-2, 3, 5, and 10 accuracy of the network trained on the SI90N15 dataset reaches 54.3%, 64.0%, 76.3%, and 91.7% respectively, suggesting the native amino acids are enriched in the first half of the predictions , an all-β protein (PDB ID 1HOE61), and a mixed αβ protein . As fixbb uses a stochastic design algorithm, we generated 500 sequences for each protein and calculated the average sequence identity to the native proteins .Because the output of the neural network is the probability of 20 amino acids at a target position, in addition to the overall accuracy mentioned above, it is also possible to calculate the top57. SPIN was trained on 1532 non-redundant proteins and reaches a sequence identity of 30.3% on a test set containing 500 proteins. The training and test set were collected using a sequence identity cutoff of 30%. SPIN was also evaluated on 50 proteins from the 500 test proteins for comparison with Rosetta62. As the structure ID of these 50 proteins are known . The top 1 prediction from SPIN shows an identity of 0.250, while the top 1 predictions from the SI30N15, SI50N15, and SI90N15 network have identities of 0.283, 0.304, and 0.402.Nonetheless, the networks trained on the larger data set in this study could still be beneficial to computational protein design. As in real applications, amino acid probability learned on larger data set could be more useful, as long as it is not biased. As an example, we tested both methods on the 63. The PSSMs of the 50 test proteins were obtained by running PSI-BLAST against the non-redundant protein sequences database available at ftp://ftp.ncbi.nlm.nih.gov/blast/db/, and converted to pseudo probability matrixes of 20 amino acids at each residue. The root mean square error (RMSE) of the matrixes to those predicted by our network and SPIN were calculated. Our network and SPIN show very similar RMSE values (0.139 for our network and 0.141 for SPIN). It should be noted that SPIN was trained on PSSMs from PSI-BLAST for predicting sequence profiles whereas our network was trained on protein sequences only.In addition to comparing sequence identities, we also compared out predictions with the position-specific scoring matrix (PSSM) from PSI-BLAST59. In a DNA repair enzyme 3-methyladenine DNA glycosylase, the probability that a random mutation can inactivate the protein was found to be 34% ± 6%, indicating a large proportion of mutations can be tolerated in this protein64. Moreover, residues at active sites are subject to functional restraints and are not necessary the most stable ones65. Our neural network approach is similar to a structural comparison method that extracts and integrates similar local structures from known structures. Therefore, its accuracy is limited by the average number of tolerable amino acids at each residue in the training set. Fortunately, the native amino acid is concentrated in the top predictions (top-5 and 10 accuracies are 76.3% and 91.7%). By integration the network output with molecular-mechanics scoring functions, it should be possible to identify the correct amino acid from the top predictions and further improve the accuracy. Particularly, the network preforms well on Gly and Pro, due to its ability to learn distinct structural features, but less satisfying on hydrophobic residues that are likely more important for the correct folding of a protein. Including solvation energy in the molecular mechanics scoring functions is probably a promising way for future development.In this study, we have developed deep-learning neural networks for computational protein design. The networks achieve an accuracy of 38.3% on the dataset with 90% sequence identity cutoff when 15 neighboring residues are included. This accuracy is limited not only by our neural network approach but also by the nature of protein structures. It is known that two proteins with a low sequence identity (~30%) can fold into similar structuresN closest residues based on Cα-Cα distances. This method may exclude some residues that have important interactions with the target residue. To quantitatively characterize this, we calculated the distance rank  of neighbor residues that have contacts (heavy atom distance <4.5 Å) with the target residue in our dataset, and found that 96.2% contacting residues have M ≤ 20, and 98.9% contacting residues have M ≤ 30, which means 3.8% and 1.1% of the contacting residues are not included in the environment if N = 20 and 30, respectively. Moreover, for terminal residues that are highly exposed, 20 neighbors may contain residues that do not have contacts with the target residue, which will generate noises in the inputs. Using distance cutoff instead of residue number cutoff may solve this problem. However, the distance cutoff method requires the input size to be highly flexible from several residues to tens of residues, which should be carefully considered during network construction.In our approach, the environment of a target residue is simply considered using the Knowing the possible amino acids with good confidence at the designing positions may reduce the search space significantly and increase the chance to make a successful design. Our test of Rosetta design on three proteins shows that it is possible to improve the sequence identity by using the output from our neural network as residue-type restraints. However, the optimal number of amino acids to be used as restraints is system dependent. More importantly, in our neural network, the prediction on each residue is independent from each other. For real designs, it is important to simultaneously consider the identities of the neighbor residues by using molecular-mechanics-based or statistical scoring functions like the ones in Rosetta. In this regard, the predicted probability of each amino acid should be explicitly taken into account. As the prediction of a trained neural network on a protein structure only takes several seconds, we expect our approach to pave the way for further development of computational protein design methods.29 using the following criteria: (1) the structure is determined with x-ray crystallography, (2) the resolution is better than 2 Å, (3) the chain length is longer than 50, and (4) the structure does not have any DNA/RNA molecules. To investigate the effects of sequence homology on prediction accuracy, the structures that satisfy these conditions were retrieved with 30%, 50%, and 90% sequence identities. The resulting entries were cross-referenced with the OPM database66 to remove membrane proteins. Structures that have D-amino acids were also discarded. The resulting structure dataset consists of 10173 (30% sequence identity), 14064 (50% sequence identity), and 17607 structures (90% sequence identity). To remove the bias from non-biological interface in the crystal asymmetric unit, the biological assembly provided by PDB was used. If multiple biological assemblies exist for one structure, the first one was used. For each of these structures, non-protein residues such as water, ion, and ligand were removed, and each protein residue and its N closest  neighboring residues were extracted as a structural cluster. Clusters that have any atoms with an occupancy <1 or missing backbone atoms were discarded. Protein oligomeric state was also considered during cluster extraction so that if a structure contains several identical subunits, only one of the subunits was used. Each cluster was then translated and orientated so that the Cα, N, and C atoms of the target residue are located at the origin, the –x axis, and the z = 0 plane, respectively.The training set was collected from PDBcos and sin values of backbone dihedrals φ, ψ and ω, total solvent accessible surface area (SASA) of backbone atoms , and three-type  secondary structure; (2) for the neighbor residues: cos and sin values of backbone dihedrals φ, ψ, and ω, total SASA of backbone atoms, Cα-Cα distance to the central residue, unit vector from the Cα atom of the central residue to the Cα atom of the neighbor residue, Cα-N unit vector of the neighbor residue, Cα-C unit vector of the neighbor residue, three-type secondary structure, and number of backbone-backbone hydrogen bonds between the central residue and the neighbor residue. The Cα-Cα distance, Cα-Cα, Cα-N, and Cα-C unit vectors were used to define the exact position and orientation of the neighbor residue with respect to the central residue. cos and sin values of the dihedrals were used because the dihedrals that range from −180 to 180 are not continuous at −180 and 180. The SASA value was calculated using the Naccess program67 on the whole protein structure  with the sidechain atom removed, because during protein design, the identity of a residue and thus its sidechain atoms are unknown. Secondary structure was assigned with Stride68. All other features were calculated with an in-house program.The input features for the neural networks are (1) for the central residues: http://keras.io) with rectified linear unit (ReLU) as the activation function for all layers. Training was performed using the categorical cross entropy as the loss function and the stochastic gradient descent method for optimization with a learning rate of 0.01, a Nesterov momentum of 0.9, and a batch size of 40,000. To account for the different abundance of each residue type in the training set, the training samples were weighted as: Wi = Nmax/Ni, where Nmax is the maximal number of samples of all 20 residue types, and Ni is the number of samples of residue type i. This bias would force the neural network to learn more from the residue types that are underrepresented in the training set. The output of the neural-network is the probability of 20 amino acids for the central residue of a cluster.The neural network was constructed using the Keras library (fixbb program and talaris2014 score in Rosetta 3.762. The crystal structures of the design targets were used as inputs without any prior minimization. 500 designs were performed for each protein with and without residue-type restraints, which were incorporated using the “-resfile” option.Rosetta design was carried out with the The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.Supporting Information