Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA–protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA–interacting proteins, by making full use of the known lncRNA–protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA–interacting proteins.As one large class of non-coding RNAs (ncRNAs), long ncRNAs ( Recently, several computational methods have been reported for predicting potential lncRNA–protein interactions. For instance, Bellucci et al. developed catRAPID in 2011 All the aforementioned methods are based on the biological characteristics of ncRNAs and proteins. CatRAPID and lncPro combined sequence and structural features of lncRNAs and proteins. RPISeq was based on sequence features. RPI-Pred used the high-order structure features of lncRNAs and proteins. However some studies show that lncRNAs generally exhibit low sequence conservation ip and jl can be defined as: ip and kp. Here, we used cosine vector similarity to measure the similarity of proteins: d(i) and d(k) are the degrees of proteins i and k, respectively.We compared the performance of LPBNI with RWR Data collection and preprocessing” for detail). In LPBNI, for one node, at least two interactions are required to perform LOOCV. Therefore, the nodes that have only one link are not considered in the performance evaluation, so we further get 4796 lncRNA–protein interactions which match that condition, and this dataset is taken as ‘gold standard’ data in the LOOCV test. The receiver operating characteristic (ROC) curves and the area under the curve (AUC) obtained using these methods are shown in We extracted 4870 lncRNA–protein interactions from NPInter 2.0 N/2/nN represents the number of candidate proteins, and n is the ranking of the tested protein among the candidate proteins for the evaluation. Based on the formula, the average fold enrichments are 4.007, 3.590, and 1.653 for LPBNI, RWR, and ProCF, respectively. These data suggest that LPBNI outperforms the other methods in identifying lncRNA–related proteins with a higher rank. The fold enrichment is also used to evaluate the performance of the proposed method, which can be defined as: Furthermore, 10-fold cross validation was applied, in order to conduct a comprehensive performance evaluation of LPBNI in predicting lncRNA–protein interactions. All lncRNA–protein interactions (4796) are randomly divided into 10 equal portions. Each portion in turn was left out as a test sample, while the remaining ones were treated as training sets. During cross validation, some nodes are separated into the test sample and the corresponding links cannot be predicted by LPBIN; therefore, those links were not considered in the process of evaluation. The ROC curves for LPBNI, RWR, and ProCF are shown in In order to further evaluate the performance of the proposed method in predicting lncRNA–protein interactions, we compared LPBNI with lncPro As aforementioned, there are 69 lncRNA–protein interaction pairs for these six lncRNAs. We next examined the numbers of lncRNA–protein interactions that were correctly recovered with respect to different percentiles Following the validation of the superior performance of LPBNI using LOOCV, we applied LPBNI onto the 4796 known lncRNA–protein interactions downloaded from NPInter Furthermore, we extracted the predictions confirmed by starBase and compared their ranks predicted by LPBNI, lncPro, RPISeq-RF, and RPI-SVM . The resIn this study, we proposed and tested a novel computational method, LPBNI, for the prediction of potential lncRNA–protein interactions. We constructed an lncRNA–protein bipartite network, using the information about lncRNA–protein interactions, lncRNA and proteins are connected if they were known to interact with each other. Following this, two-step propagation was carried out in the bipartite network to score and rank candidate proteins for each lncRNA. The proposed method has some important features. Firstly, LPBNI uses only the network constructed based on the known lncRNA–protein interactions to perform this prediction. Secondly, with an increasing degree of a node, less information is assigned to its direct neighbors. Finally, the propagation matrix is not symmetrical. The results of comparisons between LPBNI and other network-based methods show that LPBNI has higher AUC, compared to RWR and ProCF. In order to further evaluate the performance of the proposed method, we compare LPBNI with the existing methods for lncRNA–protein pair prediction and obtain consistently higher ROC curves using LPBNI in relative to lncPro, RPISeq-RF, and RPISeq-SVM for the six lncRNAs tested. All the comparisons show that our method can effectively predict interactions between lncRNAs and proteins, largely by taking advantage of lncRNA–protein interaction network. The case study shows further that LPBNI is powerful not only for the recovery of known lncRNA–protein interactions, but also for the prediction of potential candidate proteins. This suggests that LPBNI may be a useful tool for predicting candidate lncRNA–interacting proteins that could be subjected to further experimental investigation for potential functional studies.Despite the efficiency of LPBNI in the prediction of the candidate proteins for interacting with lncRNAs, some limitations exist. Firstly, LPBNI can only be implemented for a bipartite network, in which each node has at least two links. Since LPBNI only uses the prior information about the known lncRNA–protein interactions, LPBNI cannot predict candidate proteins if there is no information about the lncRNAs in the training set. This limitation may be addressed by extending the bipartite network to a bipartite network model based on lncRNA/protein functional domains or by adding the expression profile of lncRNAs The prediction of lncRNA–protein interactions is extremely important for the studies of the complex function of lncRNAs. Existing methods are using the sequence information of lncRNAs and proteins, but in this study, we introduce a network-based method, LPBNI, which takes full advantage of the information about the known lncRNA–protein interactions. We performed the evaluation and case study of this method, which further demonstrate its superior performance.http://www.bioinfo.org/NPInter/) in November, 2013, with the restriction of type “NONCODE” and organism “Homo sapiens”. Furthermore, we extracted 2380 lncRNAs from a human lncRNA dataset downloaded from NONCODE 4.0 database 7576 ncRNA–protein interactions were downloaded from the NPInter 2.0 database G, in which L = {1l,2l,…,nl} is defined as lncRNA set, P = {1p,2p,…,mp} is defined as the protein set, and E = {i,j|Li∈L,Pj∈Pe} is the edge set, where i,je represents the edge connecting the nodes ip and jl. A = {i,j|i∈P,j∈La} represents the adjacent matrix, where ija = 1 if ip interacts with jl, otherwise ija = 0. For lncRNA jl, positive samples referred to the proteins that are known to interact with jl, and the remaining proteins were considered negative samples. A simple illustration of lncRNA–protein bipartite network construction is shown in The lncRNA–protein interaction network can be described as a graph P and L as aforementioned and only the connections between different sets were allowed. LPBNI procedure is illustrated in vice versa. The propagation of information after each step is shown in The propagation process of LPBNI was derived from the recommendation algorithms proposed by Zhou et al. Here, 3 × 3 matrix represents the propagation matrix. Following the two-step propagation on the bipartite network, the final information of these three proteins becomes 5/4, 5/8, and 1/8, respectively.G, W is defined as the propagation matrix, where ikw represents the information transferred from kp to ip node, and can be interpreted as the importance of ip for node kp. For an lncRNA jl, we define S0(i) = ijs, i∈{1,2…m} as the initial information of protein P, ijs = 1 if ip interacts with jl, otherwise ijs = 0. LS(jl), j∈{1,2…n} represents the score on jl after the first step of information propagation, which can be calculated as:i.pFor the lncRNA–protein interaction bipartite network L propagates back to P. FS(ip) is defined as the final information of protein ip, representing the interaction score of protein ip with jl. FS can be defined asjl.In the second step, all the information in FS can be defined in the matrix form asS0, and The final information jl by the final score FS. All of the candidate proteins are listed in a descending order, and highly-ranked proteins are considered to interact with lncRNA jl. The data and source code are freely available at https://github.com/USTC-HILAB.Following the calculations, the proteins were ranked for A to 0. The performance of LPBNI was estimated by the success rate it achieves in recovering the known lncRNA–protein interactions. In order to assess the performance of LPBNI, we plotted the ROC curves, and compared the AUC values obtained using LPBNI, RWR, and ProCF. Additionally, we computed Sp, Sn, Acc, Pre, and MCC values. The propagation matrix W presented in this paper relies on the adjacent matrix A of the bipartite network. When LOOCV was implemented, we obtained different W values, due to the change of A values in each step of LOOCV. Therefore, W value was recalculated for each lncRNA–protein pair that was left out as test sample. Furthermore, during LOOCV process, no information was propagated on the nodes with less than two links, and these nodes were not considered during the performance evaluation.LOOCV was performed on the lncRNA–protein interaction network for performance evaluation of the proposed method. In this process, each lncRNA–protein pair was left out in turn as a test sample, by setting the corresponding value in the adjacent matrix MG participated in the downloading and preprocessing of the datasets, carried out the design and performance evaluation of LPBNI in predicting lncRNA–protein interactions. AL conceived of the project and helped with the study design. MW was involved in data analysis. MG drafted the manuscript with the help of AL and MW. All authors read and approved the final manuscript.The authors have declared no competing interests.


Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before. Further, it is not simply the case that any given gene has a (singular) function. The protein prescribed by its gene sequence has many functions [loci into function space is both surjective and injective). While many of these issues have been addressed in recent [Although protein structure determination by biophysical techniques such as X-ray crystallography, cryoelectron microscopy and NMR has become highly automated, there will, for several reasons, continue to be interest in pursuing theoretical predictions of protein structure. Despite the high productivity of the mentioned experimental methods, the rate at which genomics and proteomics data are generated still outstrips the rate at which structures can be determined experimentally. Performing mutant studies for planning protein engineering experiments or screening for proteomic therapeutics (for example: immunotherapeutics) are most rapidly done unctions ,2. This unctions ,2 this in recent  and earlTheoretical/computational protein folding studies have undergone a steady series of developments in recent years. These have included significant accomplishments in protein dynamics –5, methoAs an extension of the CMA idea, studies of patterns of sequence variability (VAR) and Shannon entropy (ENT) ,16 allowIn this paper, the focus will be restricted to protein folding, or more specifically, the folding of individual domains. The extent to which KOL, and its antecedents ,16 VAR/EBefore the VRN and KOL measurements can be made it is important to decide the range of values that give results that are relevant to the type of contact being studied. This question has been studied earlier for VRN ,16 and KThe data to produce VRN and KOL: were determined using previously published methods  for dealSVB: previously published  contact http://ignmtest.ccbb.pitt.edu/p2pdocs/p2p_doc.htmlP2P: http://gremlin.bakerlab.org/sub.phpCMA: For all of the above methods, comparisons were made between “hits” identified by the method and those in the 2D contact map for each target protein .http://www.r-project.org/).Statistical correlations between CMA, KOL, VRN, P2P and SVB (corrected for RND) displayed  as a priThe data arising out of this work can be used to construct 2D contact maps from which 3D structures can, in principle, always be derived using distance geometry –26. Therhttp://www.ccp4.ac.uk/html/contact.html) range for short contacts is 2.0Å-3.66Å while a considerably wider range, 6Å-12Å, is considered significant in order to cater for all contact types (http://en.wikipedia.org/wiki/Protein_contact_map). Given that there is such a wide spread of distances which are involved in defining a “contact” it now becomes interesting and, as it turns out, important to ask the question: in each of the algorithms for contact prediction listed above and summarised in Once these juxtaposed results have been presented, the next step is to decide how best to combine the 2D contact predictions from SVB, P2P, VRN, KOL and CMA in such a way as to provide the best consensus set, “best” being here defined as leading to rapid convergence towards a structure for the protein that corresponds to the crystal structure for this protein. As stated, there has been widespread interest in trying to predict intra-protein (as well as inter-protein) contacts. But very little is ever said about the nature of those contacts. The definition of “contact” can be very vague or ambiguous, often referring to “hydrogen bond” or “Van der Waals” contacts. Neither of these include the possibility of and maybe even need for long range effects that are not contacts as such. A standard .From de novo prediction of protein domain structures. Together with the efforts of the present author, this seems to be a valid and useful way forward. To this end, future extensions of this work will give further consideration to these other protein functions [One of the missing items in much published work is a clear definition of what is meant by “contact”. A mention of this has been made  which amunctions  that areunctions  is highlab initio predictions of domain folds. Five methods were compared and it was found that they all make predictions in different ways. Different in respect to which interatomic distance or displacement that they best predict. CMA, VRN and KOL emerge as being the most useful methods for predicting “contacts”. The first two are already well established, while the Kolmogorov approach [This paper has dealt with the question of which inputs to use when conducting approach , 32 reprAs for the interatomic contacts themselves, no account has been taken here of the nature of the atom types involved . Here, a standard “CA-CA” proximity metric is assumed as a definition for all “contacts”. But the issue is an important one. Depending on the chemical nature of the participating atom types the problem  of finding matching pairs amounts is a case of the mathematically well defined “marriage problem” . This is applicable to “+/- type” interactions or wherever there is a duality or asymmetry in the interaction. But there are also interactions of a more neutral or symmetric kind such as “hydrophobic” interactions typical of the way that aliphatic, and to some extent aromatic, sidechains interact. These have more the character of the “stable-roommate problem” . It is intended that these distinctions will form the basis of yet another extension of this work.S1 Figpredcon.f) which calculates the MCC scores from the input data is provided and users are strongly advised to peruse this file before use making note of the comments and the default names of the input files. Examples of the input files are provided for the purposes of getting the correct format. The program should be run once for each cutoff value and it is advisable to rename the output files using a filename that incorporates that value A shellscript (renumber) is provided to help with that and an awkscript (awkward) is provided to enable the different measurements  to be corrected for the random (RND) scores. The correct command for compilation of predcon.f under Linux is:The identities of the proteins is stated in columns 1 and 2 of gfortran -Wall -O3 -ffixed-line-length-132 -o predcon.exe predcon.f(GZ)Click here for additional data file.S1 SoftwareIt goes without saying that no commercial use may be made of these programs or scripts in whole or in part without express permission of the author.(GZ)Click here for additional data file.


The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. Few proteins carry out their tasks in isolation. Instead, proteins combine with each other in complicated ways that can be affected by either the natural genetic variation that occurs among people or by disease causing mutations such as those that occur in cancer or in genetic disorders. To understand how these mutations affect our health, it is necessary to understand how mutations can affect the strength of the interactions that bind proteins together. This is a difficult task to do in a laboratory on a large scale and scientists are increasingly turning to computational methods to predict these effects in advance. We show that by looking at the multiple alignments of similar protein-protein complex structures at the interface regions, new constraints based on the evolution of the three dimensional structures of proteins can be made to predict which mutations are compatible with two proteins interacting and which are not. PLOS Computational Biology Methods paperThis is a The formation of protein-protein complexes plays an essential role in the regulation of various biological processes. Mutations play fundamental roles in evolution by introducing diversity into genomes that can either be selectively advantageous or cause a change in protein affinity that can result in malfunction of the protein interaction network , 2. The Most of these efforts involve the prediction of the effect of a mutation upon the Gibbs free energy change of protein-protein binding (ΔΔG) on a large scale. Quantitatively, ΔΔG values for protein interactions may be measured experimentally by a variety of biophysical techniques , 5. HoweAs a result, scientists have increasingly turned to computational methods to predict ΔΔG values. For a rigid protein, the ΔΔG of folding or protein binding can be determined relatively accurately from a full-atomic description of the protein structure or complex, using either potentials based on molecular mechanics that attempt to quantify the interactions in physically meaningful terms , statistAs such, alternative methods have been proposed that either use reduced representations of the protein structure or simplified interaction schemes  , 14 or oThis limitation can be overcome if new and more accurate predictors are available for ΔΔG prediction. Because physics based features often share the same limitations, attempts have been made to predict ΔΔG using alternate scoring methods. Based on their success in the prediction of ΔΔG values for protein stability , 18, seqHowever, in many cases there are other driving forces for selection besides protein-protein binding affinity such as binding specificity , 21, 22,http://zhanglab.ccmb.med.umich.edu/BindProf.Using this approach, we show an interface binding profile score, called BindProf, formed from an aligned ensemble of structurally similar interfaces has accuracy as a standalone feature similar to, or in most cases, better than many composite all-atom potentials. Unlike the all-atom energies, it can be calculated very rapidly once the profile is constructed. The on-line server of the BindProf program is freely available at To predict the free-energy change of protein-protein interactions, ΔΔG, BindProf adopts a multi-scale approach shown in F reflects the log odds likelihood of an amino acid (A) being found at a particular position (p) in an aligned ensemble of structurally similar proteins [g is the Henikoff weighted frequency of the amino acid a appearing at the pth position in a multiple sequence alignment (MSA) with exactly redundant interface sequences removed; M is the BLOSUM substitution matrix with a varying for 20 amino acids, which is used to account for missing structures in the PDB. Experimental ΔΔG values are therefore hypothesized to be proportional to the mutant profile score defined as the difference between the profile scores of the wild type (WT) and mutant (Mut) amino acids at position p in the interface:Since the most distinctive feature of our approach is the use of structurally similar interfaces of protein complexes in the PDB to score the effect of a mutation, we first consider the most accurate way to predict ΔΔG of protein binding using only this information. The amino acid distribution of structurally similar complexes can be analyzed quantitatively by the use of structural profile scores. Similar to a position specific scoring matrix, a structural profile score proteins F=∑The profile therefore depends on both the cutoff level for defining a similar complex and the measure of similarity used. The definition of “similar” is less straightforward in regards to interfaces than it is with overall protein structure. Similarity of protein structures can be defined by a normalized, length independent measure of structural difference, TM-score, which has been shown to have a close relationship with fold classification , 32. ForIn The relationship between cutoff value and ΔΔG prediction for the interface alignment methods  is more complex reflecting a more complex underlying distribution. In each case, the accuracy of ΔΔG prediction is at least bimodal with the cutoff value. Like the monomeric structure profile, the accuracy rises at strict cutoff values. As the cutoff is reduced it levels off as an adequate representation of closely related complexes is built. However, unlike the monomeric structure profile, the accuracy rises again at lower cutoff values, eventually reaching a higher accuracy than can be achieved by profiles constructed from closely related complexes. Closer inspection of the actual origin of this effect is the inclusion of sequences at lower cutoff values that can be aligned accurately to a region within the interface but with relatively poor overall global alignment. From the viewpoint of applications which rely on global properties like the recognition of convergently evolved similar interfaces for function annotation –38, thesTaken individually, sequences with higher interface similarity should be more predictive of ΔΔG then sequences with lower interface similarity. However, the accuracy of the interface profile score is highly dependent on the number of sequences that can be aligned at the site of the mutation. A representative example is shown in n sequences are added until the second, looser cutoff is reached. n for the optimal high and low interface similarity cutoff values (IScore = 0.25 and 0.19). n reaches a shallow maximum around 80 sequences. The adaptive profile shows a significant improvement over the profile formed from a high similarity cutoff and a smaller improvement over the profile formed from a high similarity cutoff.We therefore considered an adaptive procedure to form a more accurate profile. The sequences are first sorted by descending interface similarity. All sequences with an interface similarity above a strict cutoff are added to the profile and up to β based statistical potentials . The difference in c between the adaptive interface profile score and the all-atomic docking potential PISA is also statistically insignificant . The adaptive interface profile score is superior in accuracy to all other potentials tested at high statistical significance .The Pearson’s correlation coefficient c although it is statistically indistinguishable with BindProf and PISA. However, this value could be biased somewhat by the fact that the side-chains of the mutant have been reconstructed using the FoldX force field. A mismatch between the force field used to optimize the side-chain rotamers and the scoring potential can result in a degradation of the performance. In our early trials, the Talaris2013 Rosetta force field generally showed similar performance to FoldX values if the side-chains were reconstructed using the Talaris2013 forcefield.From c is still relatively low (below 0.5). However, one of the key features of BindProf is that it works on a fundamentally different basis then the other methods that are currently in use. This complementarity should be of important help for improving the overall recognition accuracy of multiscale potentials when combined with other sources of potentials as demonstrated below.We note that although the BindProf score compares favorably with other individual potentials, the Pearson correlation coefficient c between experimental and calculated ΔΔG values restricting the dataset to the entries with experimental ΔΔG values within the appropriate range.In many applications it is desirable to know the accuracy of ΔΔG prediction across different categories of experimental ΔΔG values. For example, the accuracy of predicting destabilizing mutations is significantly less important in protein design than the accuracy of predicting favorable mutations, as strongly destabilizing mutants are rejected during the design process. Any inaccuracy in prediction therefore only matters to the extent they are misclassified as favorable or neutral mutations. On the other hand, favorable mutations should be enriched during the design process and accurate ΔΔG prediction is essential for these mutations. We therefore recalculated the Pearson’s correlation coefficient c = 0.5) at predicting favorable mutations , similar to previous observations which also included a degree of backbone flexibility by incorporating a short relaxation before the calculation of FoldX energies [Interface profile scores show exceptional performance relative to other predictors (energies . Likewisc = 0.31) while it is not predictive of favorable mutations (c = -0.13). All methods were inaccurate in determining the subtle differences between neutral mutations  (c <25%). Core residues are typically hydrophobic with a composition strongly divergent from the composition of the remainder of the protein surface [We next sought to see if the accuracy of interface profile scores could be predicted from the characteristics of the profile. Interface residues play different roles in protein-protein interactions and display both different conservation patterns and different types of interactions depending on their relative position within the interface . Since tc) . Followi surface . Core re surface . The hyd surface , 54, andc) buried upon complex formation. The rim residues have a composition more similar to the surface of the protein away from the interface [“Rim” residues surround the core residues and are also exposed in the monomeric protein. But unlike the core residues, the rim residues become only partially  and “interior” (rASAc <25% and rASA <25%) consist of residues that make no contacts with the binding partner. Mutations within these regions only influence complex formation indirectly by influencing conformational changes, by destabilizing protein folding [The final two categories of “surface”  and the adaptive interface profile along with 10 additional features reflecting the quality of the high and low interface similarity profiles. These cutoff levels were selected on the basis of validation on a separate testing dataset comprised of 20% of the data not used in validating the final result.For the high and low interface similarity profiles we calculated additional features, includingThe average RMSD at the mutation site.The average fraction of preserved contacts after alignment relative to the number of contacts in the native complex at the mutation site.The total number of aligned residues at the mutation site within the profile.The relative sequence entropy at the mutation site defined by the Jenson-Shannon divergence within the profile of the amino acid distribution at the mutation site from the background amino acid distribution found in proteins.The Z-score of the Jensen-Shannon Entropy relative to other interface sites.The first two features report on the relative quality of the alignment of the structural profile; whether the ensemble of aligned structures actually resembles the protein complex under question or not. The last three features measure the information content within the profile and reflect whether the profile is sufficiently diverse to fully reconstruct the mutational landscape of the interaction. A random forest algorithm was then used to predict ΔΔG with these features using repeated 10 fold cross-validation .c = 0.71±0.07 . Although the dominant feature in terms of determining relative error is the Cβ statistical potential RF_CB, the most important term in terms of node purity is the low interface similarity profile score and the other profile based features are also important features in the approach both in terms of relative error and node purity (c = 0.83±0.05) above the residue-level potential model  and reduces the computational speed and the range of applications . In this work, we developed a novel approach, BindProf, aiming to overcome some of these limitations by introducing an interface structure profile based scoring function built on the multiple sequence alignments of analogous protein-protein interactions collected from the PDB.Interface profile scores constructed in this manner can be used as either as a predictor of the Gibbs free energy change of protein-protein binding (ΔΔG) in their own right or combined with other features in a machine learning approach. Considered as a standalone feature, the adaptive interface profile score created by BindProf has an accuracy similar to the best all-atom potentials . HoweverThe effectiveness of interface profile scoring in predicting binding affinity changes has implications beyond the prediction of ΔΔG values for protein affinity changes. First, the fact that such a method can be constructed at all is independent confirmation of the results of Gao and Skolnick  that theExperimental ΔΔG values were derived from the SKEMPI database that consists of experimental protein affinity changes upon mutation for protein-protein complexes in which a crystal structure of the WT complex are available . A subseFor random forest machine learning, three separate training, testing, and validation datasets were constructed. The training set (60% of the data) was used to construct the model, while the testing set (15% of the data) was used to tune the number of variables attempted in each split. The final model was evaluated by 10 fold cross-validation repeated three times on the validation set (25% of the data).Crystal structures were first downloaded from the PDB and stripped of water and all non-protein ligands. A short optimization of the structure of the WT protein complex was then performed to eliminate small clashes and other undesirable features by the RepairPDB function within FoldX . StructuE is the relevant energy function. For the docking functions PIE, PISA, DCOMPLEX, FoldX and the Rosetta Energy function Talaris2013, this calculation is performed internally and ΔΔG is directly proportional to the difference between the energies of the two complexes:For all the sequence and physics based energies except the docking functions PIE , PISA 4, and DCO Rosetta .Interface structural alignment was performed using the COTH complex library of non-redundant dimeric structures. To create this library, higher order complexes in DOCKGROUND  are firsInterface alignment was performed by either Ialign  or PCaliLQ is the total number of residues in the interface, Na is the number of aligned residues, di is the distance between the Cα atoms of residues at ith aligned residue pair, and d0 is an empirical scaling factor dependent on LQ to ensure the length invariance of the final score [The equation for the interface similarity metric iTM-score is a direct analogue of the scoring matrix for TM-score  except tal score .fi reflecting the fraction of conserved contacts, i.e.fi = (ci/ai + ci/bi)/2, where ai and bi are the numbers of interfacial contacts of ith aligned residue pair for the template and query complex, respectively, and ci is the number of overlapped contacts. A contact is defined as being overlapped if the residues forming these contacts are aligned in the two pairs of chains.The Iscore is defined similarly except for the addition of a contact overlap factor Ii of ith residue pair:fc is the ratio of common contacts between two sets of aligned interfacial residues. Ii equals to 1 if the ith pair of aligned residues are in the same chemical type, or 0 otherwise. To define the chemical equivalency, the amino acids are split into non-overlapping groups of positively charged , negatively charged , mixed hydrogen bond donor/acceptors , aromatic , hydrophobic and mixed donor/acceptor or aromatic .The PCscore is defined analogously to the Iscore with the addition of chemical similarity measure S1 Fig(PDF)Click here for additional data file.


Motivation: The interpretation of cancer-related single-nucleotide variants (SNVs) considering the protein features they affect, such as known functional sites, protein–protein interfaces, or relation with already annotated mutations, might complement the annotation of genetic variants in the analysis of NGS data. Current tools that annotate mutations fall short on several aspects, including the ability to use protein structure information or the interpretation of mutations in protein complexes.Results: We present the Structure–PPi system for the comprehensive analysis of coding SNVs based on 3D protein structures of protein complexes. The 3D repository used, Interactome3D, includes experimental and modeled structures for proteins and protein–protein complexes. Structure–PPi annotates SNVs with features extracted from UniProt, InterPro, APPRIS, dbNSFP and COSMIC databases. We illustrate the usefulness of Structure–PPi with the interpretation of 1 027 122 non-synonymous SNVs from COSMIC and the 1000G Project that provides a collection of ∼172 700 SNVs mapped onto the protein 3D structure of 8726 human proteins (43.2% of the 20 214 SwissProt-curated proteins in UniProtKB release 2014_06) and protein–protein interfaces with potential functional implications.Availability and implementation: Structure–PPi, along with a user manual and examples, isavailable at http://structureppi.bioinfo.cnio.es/Structure, the code for local installations at https://github.com/Rbbt-WorkflowsContact:tpons@cnio.esSupplementary Information:Supplementary data are available at Bioinformatics online. Predicting how single-nucleotide variant (SNV) alters the function of protein products is a topic of growing interest in genomics and bioinformatics (reviewed in Supplementary Table S1).Indeed, only a few methods, e.g. PMut  ability to map mutations onto 3D structures of protein–protein complexes , (ii) description of functional residues around the SNVs in protein–protein interfaces, additionally Structure–PPi implements a full annotation schema annotating post-translational modification sites, catalytic sites, binding sites residues, Pfam domains and prediction of damaging effects from state-of-the-art methods. Besides, the system selects a single reference sequence for each protein-coding gene , and provides information about cancer somatic mutations, and their corresponding tumor origin and histology. Since this work was submitted two papers dealing with the analysis of disease mutations at protein-protein interfaces have appeared  and the maximum frequency observed in the 1000G project  and somatic mutations extracted from: UniProt , InterPrStructure–PPi maps SNVs onto the protein 3D structure for 8726 human proteins (43.2% of the 20214 SwissProt-curated proteins in UniProtKB release 2014_06). This value of 43.2% is well above the 18% coverage reported by MuPIT  for standard users. Structure–PPi includes a pair-wise alignment (Smith–Waterman) step to resolve any potential inconsistency between the isoform sequence and the sequence in the 3D structure, or differences between the isoform sequence and the reference UniProt. Depending on the database used, the throughput on a single process is around hundreds or thousands per second, with less than 500 MB of memory use. We will continue to develop the Structure–PPi, in particular its method for parallelizing file archival and retrieval, and software portability, to further facilitating inclusion into extended genome annotation workflows. We have pre-computed annotations for all coding nsSNVs in COSMIC v69 (∼741 276), and in 1000G Project (∼285 846). The results are available through the website, and some summary statistics and discussion of variants at protein interfaces can be found in Supplementary Table S4. This preliminary analysis might identify disruption of important interactions and improve our understanding about human diseases. Structure–PPi is currently used in different projects, including the ICGC-CLL analysis.Structure–PPi is an independent component of the Rbbt-framework  are located in functional domain/sites or they affect amino acid residues that have been shown to be important by 3D protein structure. Furthermore, the system provides information about known functional-residues in close physical proximity to query SNVs. Thus, Structure–PPi can provide both mechanistic and biological insights into the role of SNVs in a given cancer.


Recognizing the correct structural fold among known template protein structures for a target protein (i.e. fold recognition) is essential for template-based protein structure modeling. Since the fold recognition problem can be defined as a binary classification problem of predicting whether or not the unknown fold of a target protein is similar to an already known template protein structure in a library, machine learning methods have been effectively applied to tackle this problem. In our work, we developed RF-Fold that uses random forest - one of the most powerful and scalable machine learning classification methods - to recognize protein folds.RF-Fold consists of hundreds of decision trees that can be trained efficiently on very large datasets to make accurate predictions on a highly imbalanced dataset. We evaluated RF-Fold on the standard Lindahl's benchmark dataset comprised of 976 × 975 target-template protein pairs through cross-validation. Compared with 17 different fold recognition methods, the performance of RF-Fold is generally comparable to the best performance in fold recognition of different difficulty ranging from the easiest family level, the medium-hard superfamily level, and to the hardest fold level. Based on the top-one template protein ranked by RF-Fold, the correct recognition rate is 84.5%, 63.4%, and 40.8% at family, superfamily, and fold levels, respectively. Based on the top-five template protein folds ranked by RF-Fold, the correct recognition rate increases to 91.5%, 79.3% and 58.3% at family, superfamily, and fold levels.The good performance achieved by the RF-Fold demonstrates the random forest's effectiveness for protein fold recognition. Proteins are the fundamental functional units in living systems. Protein tertiary  structures at the molecular level are necessary to understand the functions of proteins. However, due to the significant cost of experimentally determining the tertiary structures of proteins, the number of known 3D protein structures is about 200 times smaller than the number of known protein sequences ,2. ThereSince the number of unique protein structures appears to be limited  according to the structural analysis on all the tertiary protein structures in the Protein Data Bank (PDB) , it is pGiven a number of features describing the pairwise similarity between two proteins , the objective of the classification is to predict if the two proteins share a similar tertiary structure (fold). The problem can often be divided into three difficulty levels that range from the easiest family level (i.e. two protein belonging to the same family), to the superfamily level, and to the hardest fold level. This roughly corresponds to the decrease in sequence identity between two proteins. Proteins sharing similar structures have a relatively high sequence similarity if they are in the same family, moderate or little sequence similarity if in the same superfamily, and almost no sequence similarity if in the same fold.Random forest is one of the most powerful machine learning methods known for its good interpretability and its efficiency in handling very large training datasets . Random In this work, we applied the random forest method (i.e. RF-Fold) to address the fold recognition problem and evaluated its performance on the standard Lindahl's dataset , on whicThe decision tree method for classification had been widely used in many domains due to its simplicity and good interpretability after Leo Breiman et al. introduced it in 1984 . HoweverIn our study, each decision tree in the random forest was trained to predict if two proteins share a similar structural fold or not from a list of input features describing similarity between the two proteins (see Section 2.2 for the description of the training data and features). A number of features used to construct each decision tree were randomly selected from the total 84 features. The random forest method was implemented by using the randomForest R package . The dechttp://supfam.org/PRC) were used to align target and template profiles to obtain profile-profile alignment scores. For structural features, based on the global profile-profile alignments obtained with LOBSTER, structural features of query proteins predicted using the SCRATCH suite [We trained and tested RF-Fold on the FOLDpro dataset . The FOLCH suite -49 were The small portion of pairs belonging to the same protein family, superfamily, or fold was labelled as positive examples because they shared the same structural folds. The vast majority of protein pairs that did not have structural similarities were labelled as negative examples.We divided all protein pairs into 10 equal-size subsets for 10-fold cross validation purposes. We put all the target-template pairs associated with the same target protein into the same subset. Nine subsets were used for training and the remaining subset was used for validation. We removed all the pairs in the training dataset that used targets in the test dataset as templates. This procedure was repeated 10 times and the sensitivity and specificity of fold recognition were computed across the 10 trials. We also compared RF-Fold with 17 other methods by fold recognition rates for top-one ranked templates and for top-five ranked templates as in ,32. UsinWe compared the random forest consisting of 500 decision trees to a single decision tree in terms of the error rate (i.e. percent of incorrectly classified protein pairs). The error rate of the random forest classification was 0.566%, which was lower than 1.135% of a single tree , how to evaluate the performance on the benchmark data set , the datasets used to do cross-validation, and the scripts used to generate 84 pairwise features for a pair of proteins (32 Perl scripts in scripts_feature_generation sub-directory). Based on the document and programs, any user can create his/her own training and test datasets and train / test his/her own random forest classifier for protein fold recognition from scratch. The software, source code and data are released under the GNU General Public License. Anyone can freely reuse the software and source code for any purpose . Any technical problems may be addressed to the email box of the corresponding authors. Based on users' feedback, additional documents, utility programs, test examples, and data will be added in order to facilitate the development of random forest methods for protein fold recognition.In order to facilitate the reuse and implementation of RF-Fold method, the online web service for fold recognition, the source code of the programs of random forest learning and classification, the scripts of generating pairwise features for a pair of proteins, the scripts of evaluating the fold recognition results, and the training and test datasets are released at In this study, we developed a random forest method (RF-Fold) to recognize protein folds. The method was systematically validated by varying the input features and the class distribution of training datasets on a standard fold recognition dataset. The random forest consisting of 500 decision trees yielded a low error rate than a single decision tree on a highly imbalanced dataset. The random forest also delivered a good, steady performance regardless of the different ratios of negative and positive examples. Compared with 17 other different fold recognition methods, the performance of the RF-Fold is generally comparable to the best performance. The results achieved by the RF-Fold demonstrated the effectiveness of using the random forest algorithm in protein fold recognition. In the future, we plan to further evaluate the performance of RF-Fold on a standard protein homology detection dataset , and to The authors declare that they have no competing interests.TJ implemented the algorithms and carried out the experiments. TJ and JC analyzed the data, wrote and edited the manuscript. TJ and JC approved the manuscript.


Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations.LocSigDB (Database URL: http://genome.unmc.edu/LocSigDB/ Proteins are synthesized in the cytoplasm and on ribosomes bound to the endoplasmic reticulum (ER), with a few proteins synthesized in mitochondria or chloroplasts (as in plant cells). However, the nucleus-encoded proteins are targeted to different subcellular locations to carry out defined functions. Aberrant mislocalization of proteins to unintended locations can interfere with numerous cellular processes, often leading to many diseases . ProteinMany computational methods have been developed for the prediction of protein subcellular localization ; howeverOver the past decade, we have developed a variety of tools for predicting protein subcellular localization . This haLocSigDB is a database dedicated to protein subcellular localization signals and the proteins that harbor them, along with the research articles that have experimentally confirmed these signals. The main goal of this database is to collect and organize the information on localization signals and present it in a user-friendly and searchable format to facilitate easy retrieval of information. Our second goal is to ensure that the information in this database is up-to-date. In addition to monitoring the new literature, we also provide a signal submission form on our website for users to submit experimentally characterized sorting signals. We will review this information for authenticity before adding it to our database. We anticipate LocSigDB will serve as a comprehensive resource of protein targeting signals to the scientific community.Over 1000 published articles related to protein-targeting signals were collected and reviewed to extract experimentally determined subcellular localization signals across eight major cellular compartments. Various keywords and their combinations were used in PubMed searches to retrieve appropriate literature; some examples are ‘Nuclear localization signal’, ‘NLS’, ‘Nuclear localization sequence’, ‘Mitochondrial targeting signal’, ‘Lysosome sorting signal’, and so forth. The returned literature was reviewed with a focus on the following criteria for selecting a signal as a valid sorting signal: (i) if a particular signal is able to target a non-resident protein to its own specific organelle , or (ii) by deleting or mutating some amino acids from the signal prevents import of the protein into its native subcellular location. Based on these criteria, only 518 articles were selected out of the 1000 articles that contained relevant information (some articles had information on more than one protein). Note that some researchers report a part of the protein sequence as a signal while others report rather a specific peptide sequence or a sequence pattern with a set of conserved residues or non-specific residues allowed in the patterns. Such signal patterns were translated into regular expressions in our database to facilitate pattern-based querying.Protein(s): The protein(s) in which the experimental localization signal was reported in the literature. (ii) Localization: The exclusive subcellular location where the protein containing the targeting signal is found. (iii) Reference(s): Published primary literature on the experimental localization signals. This field has URLs that cross-link to PubMed citations. (iv) Coordinate(s): The start and end positions of the signals found is a given sequence. This parameter is displayed only during search by ‘Protein ID' or ‘Protein Sequence'. (v) UniProt Accession(s): UniProt accessions of all proteins that contain the same amino acid pattern as that of a given signal. Preferably SwissProt accessions for the protein are presented but in few cases the signals could not be mapped onto SwissProt annotated proteins; there the TrEMBL accessions have been reported. This helps achieve complete integration with the UniProt. (vi) Organisms: The organism(s) where each signal is found and this information is retrieved from the UniProt as it corresponds to the UniProt accessions and the respective organism representing the same. But the organism field is unique such that each organism is reported only once although more than one protein sequences (accessions) from the same organism may contain the signal of interest.Each entry in the LocSigDB annotates a localization signal with five descriptors: (i) LocSigDB contains 533 experimentally validated localization signals. http://genome.unmc.edu/LocSigDB/. At this time, the database contains 533 experimentally identified protein localization signals, the proteins that harbour such signals and corresponding literature associated with each signal. LocSigDB provides three search functions to retrieve information pertaining to localization signals. Users can search the database by submitting (i) a defined localization signal or a motif pattern (using wild card characters), (ii) a protein identifier (NCBI’s RefSeq ID or UniProt’s protein accession or ID) or (iii) a protein sequence itself (in FASTA format). The web interface system is built using LAMP  architecture. The data is stored in a MySQL database and is made available using a Perl/CGI backed user interface. When a user queries for a signal, the server searches for the signal in the LocSigDB and displays the results. The results page also contains links to further probe the information on signal motifs and PubMed citations. If the user wants to search using an NCBI protein identifier, the server automatically retrieves the sequence from the NCBI database using NCBI E-utilities and displays the results for the retrieved sequence. Similarly, a search can be made using UniProt/SwissProt IDs. In the third search option, a user can provide the full or a partial protein sequence in FASTA format. Certain sequence patterns (such as short repeats or common motifs) occur more frequently in protein sequences than others; thus, have a higher chance of randomly matching as substrings to a query pattern and generating false positives. Hence, we have made the substring search as ‘optional’ below the search window and the substring matches are displayed only if chosen by the user. Additionally, avoiding the use of wild character (*) or lax regex patterns in the queries will reduce the false positive matches. An overview of the query and result interface is shown in LocSigDB is freely accessible on the web at To encourage users to submit experimentally validated new localization signals/motifs, we have provided a ‘Submit signal’ link on the left panel. This link leads to a preformatted form for submission of signals on the LocSigDB webpage. We will review these signals and the literature for accuracy before including in our database. We intend to maintain and frequently update LocSigDB.We present LocSigDB, a comprehensive database and web interface to search and explore localization signals for eight distinct subcellular locations in eukaryotic and bacterial cells. LocSigDB has been designed to help better understand the signal-dependent transport of various proteins; and as a result it is intended to help the development of therapies based on the interception of cellular signals in diseased cells , 44. A n


Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and visualization of the hidden dynamics within protein interaction networks. in vivo protein interaction datasets as 3-dimensional virtual protein complexes. By using this method to approximate the diffusion of complex components, we can predict transient conformational states and estimate their abundance in living cells. Our method offers biologists a framework to correlate experimental phenotypes with predicted complex dynamics such as short or long-range effects of a single perturbation to the function of the whole ensemble.Cells are complex dynamic systems, and a central challenge in modern cell biology is to capture information about interactions between the molecules underlying cellular processes. Proteins rarely act alone; more often they form functional partnerships that can specify the timing and/or location of activity. These partnerships are subject to dynamic changes, and thus protein interactions within complexes undergo continuous transitions. Genetic and biochemical evidence suggest that regulation or depletion of a single protein can alter the stability and activity of an entire protein complex. Experimental approaches that detect interactions within living cells provide critical information for the dynamical system that protein complexes represent; yet complexes are often depicted as static 2-dimensional networks. We have built a system that projects  The living cell is a dynamic, out of equilibrium system in which the interactions among components of multi-protein complexes undergo continuous diffusion and exchange. One of the central challenges in biology is to discover the relationship between the components contained within the complex and the function it performs. There are many experimental techniques, such as Tandem Affinity Purification Mass Spectrometry, and Yeast Two-Hybrid that seek to identify protein-protein interactions. The result usually is the addition of a novel edge to an already complicated network of protein-protein interactions. However, understanding the role of each individual protein within a protein complex has remained a challenge until recently.pca) have successfully obtained structural insights from protein-interaction data pca implements two complementing fragments of dihydrofolate reductase (DHFR) in a plate-based growth assay and reports interactions based on growth in the presence of the drug methotrexate. Methotrexate resistance is conferred to cells when two complementing DHFR fragments, each fused to the carboxyl terminus of a bait or prey protein, encounter each other through direct and indirect interaction of the bait and prey proteins. The formation of the functional DHFR enzyme is a reversible process that occurs when the bait and the prey proteins are within 8 nm of each other. It has been proposed in pca dataset can be used to refine the results generated by the Integrative Modeling Platform (IMP) pca dataset can be used to refine high-quality models generated by the IMP, the set of constraints obtained from pca can also be used to generate a coarse-grained model containing conformations that represent the spatial configuration of a single or several conformational states detected in vivo. Tools are needed for coarse-grained modeling that can be used to visualize and to interpret protein-protein interaction data, especially for protein complexes for which structural information is incomplete or unavailable. To our knowledge, this type of modeling algorithm does not exist.New experimental techniques based on Protein-fragment Complementation Assays  procedure to generate an ensemble of possible conformations for a given clique obtained from the high quality DHFR-pca derived interactome generated by experimentally testing pair-wise interactions amongst 5756 yeast proteins In this article, we introduce MCMC-PCApca. To extract this information and gain greater understanding of the dynamic behavior of protein complexes, we designed an in silico method for predicting diffusion and exchange of proteins resident in multi-protein complexes. Our method for model generation implements a MCMC approach to generate a representative sample of conformations based on experimental data for protein complex collected using DHFR-pca and the more recently developed OyCD-pca method 2 software can be downloaded at http://aguada.biol.mcgill.ca/software.html.Dynamic information is embedded within networks of protein interactions detected using 2 algorithm requires the set of interactions amongst the proteins of interest and the shape of those proteins of interest to generate structural conformations. We generated a binary interaction matrix for each protein complex of interest derived from the DHFR-pca dataset and generated the radius of gyration from modeling each protein of the complex as a sphere.The MCMC-PCATo simplify the protein complex model generation, each protein within a given complex was modeled as a uniform sphere. To confirm the validity of this approach for the yeast protein dataset, we evaluated the relationship between the chain length (number of residues) and the volume occupied by the α-carbons. We evaluated all monomeric yeast proteins deposited in the Protein Data Bank  0.39), with 448 of the 477 proteins falling within the 95% confidence interval , Py(i), Pz(i)). The set of spheres P = {P(1),P(2),…,P(n)}, where n is the number of proteins in the network being simulated, constitutes a possible conformation for the protein complex. Although proteins in living cells contact each other and “overlap” , we implemented a constraint that allows contact but prevents proteins from overlapping.We define a conformation of a protein complex as a connected network such that each protein in 3-dimensional space occupies a sphere of size RTo generate the conformational states of a protein complex, we applied a modified Markov Chain Monte Carlo simulation . During pca method that detects well-defined, pair-wise protein-protein interactions. For all interaction pairs, a positive outcome implies that the two proteins of interest come together such that the mutant DHFR enzyme forms and can overcome the inhibition of methatrexate. The spatial constraint of the DHFR-pca was estimated using the RNA polymerase II crystal structure as a benchmark to reference detected interactions against the distance between carboxyl termini of protein pairs. Positive pca interactions were predominantly composed of protein pairs within 8 nm of each other with an upper bound of ∼ 11 nm Our algorithm relies on the precise, binary nature of the pca method is ∼2 fold greater than the average diameter of a folded protein pca method. Moreover, the 8 nm resolution of the assay implies pairs of interacting proteins have significant diffusional space in which to assume conformational states.The estimated 8 nm resolution of the DHFR-pca method into consideration when modeling the surface-to-surface distance between interacting and non-interacting proteins.The spatial constraint implicit in the interaction dataset represents a physical barrier that is overcome if an interaction is detected, and must be factored in to the knowledge-based potential. Proteins that are more than 8 nm apart are much less likely to be detected as interaction pca could only detect interactions between proteins for which the C-termini are separated in distance by no more than approximately 8 nm pca assay. We also consider the flexible linker (GGGGS)2) between the protein of interest and the DHFR fragment is predicted by Pep-fold to be ∼1.2 nm in length when fully relaxed and able to stretch up to ∼2.4 nm pca assay could detect binary protein-protein interactions at up to about 9.2 nm if both linkers are fully stretched. It would be rare for the linkers to be consistently stretched to full length during the detection of an interaction. Based on this, the probability of detecting an interaction should rapidly decrease as the distance between the proteins increase since the stretching of the linker would be thermodynamically unfavorable. This creates a bias in the distribution of the distance at which a protein-protein interaction is detected as a result of the linker not completely stretching. Therefore, in our simulations, we model the surface-to-surface distance between two proteins as a Weibull distribution.As designed, the DHFR-λ) and shape (k). The scale parameter was set to 4 nm  from our input dataset. All conformations are first translated such that the center of mass (COM) of protein 0 is at the zero co-ordinate. Next, each conformation is rotated such that the COM protein X is on the positive x-axis then rotated around the x-axis such that the COM of protein Y has no z-value and its y-value is positive. Based on the COM of protein X and Y, the positive z-direction is defined such that the vectors defined from the origin to the COM of protein X and Y are a right-handed coordinate system. Based on this new coordinate system, we verify if protein Z is on the positive z-axis. If it is not, we use the mirror image around the X-Y plane such that it becomes on the positive z-axis.i and project it by applying Kabsch's algorithm j that is most similar to it. We define similarity between conformations based on the root mean squared distance (RMSD) between the projected conformation i and conformation j. The conformation j that is most similar to conformation i has the lowest RMSD. Since the position of each conformation being projected will change upon each iteration, we reapply the Kabsch algorithm until the sum of the positional difference before and after running the algorithm is less than 1.To minimize rotational misalignment, we take a conformation Next we applied PCA on the Z-score transformed COM, where the distribution of the COM is transformed into a distribution of Z-scores. The Z-score transform is a necessary step because nodes with many edges are expected to diffuse less than nodes with few edges; the latter are expected to occupy a larger diffusional space in comparison. Therefore, without this Z-score transformation, it is expected that the first few principal components would correspond to proteins that moved the most. However, the goal is to identify a distinct change in the spatial occupation of a few key proteins, such as regulators or linchpin proteins that lead to the formation of distinct clusters. The Z-score transformation allows for such spatial changes to be identified. The ensemble of all protein complexes was then projected on to the two first principal components allowing the de-convolution of clusters representing dominant conformations.pca method in particular reveals the modular and interconnected nature of protein interaction networks (PINs). As the number of components in a PIN increases, the need for in silico modeling and visualization of complex dynamics and diffusional space also increases. Detection of interaction pairs using DHFR-pca enabled the construction of a large-scale protein interaction network that contains a number of sub-networks, each containing 10s of proteins.The composition and dynamic properties of multi-protein complexes can be studied experimentally using many techniques, yet the DHFR-An example of a multi-protein complex is the evolutionarily conserved Arp2/3 complex, which is one of a number of protein complexes that contribute to the formation of actin filaments by decreasing the energy barrier required for nucleation pca, as the maximum distance between carboxyl-termini is <10 nm. The distance between pairs of carboxyl termini was calculated from the PDB structure 1K8K using UCSF Chimera pca is in agreement with the number of predicted interactions between proteins with carboxyl termini < 8 nm apart, as well as three interactions between pairs >8 nm apart; Arp2-Arc19 (8.49 nm), Arc40-Arc18 (9.14 nm) and Arc40-Arc19  on the aligned conformations. These aligned conformations were then projected onto the first two principal components . PCA allThe PCA analysis revealed interleaved yet distinct clusters, and indicates a continuous transition from one conformation to another with at least one major conformational state and one minor conformational state . We inveGuided by the biplot, we next investigated the two major clusters of the PCA landscape by plotting the center of mass of each protein as a dot, which creates a visual representation of complex conformations. We highlighted proteins that the PCA analysis determined to be contributing the most to the formation of that cluster  and Rvs161/Rvs167 (required for scission during endocytosis) By using MCMC simulations to generate conformation states, our conformational states represent an ensemble of conformations with similar scores, close to a local minimum. Embedded within these conformational states are conformations that are undergoing diffusion and exchange relative to the local maximum scoring state. The diffusional search space for each protein within a given conformational state is determined by a set of attractors (interacting proteins) and repressors (non-interacting proteins). Therefore, it is expected that proteins are continuously undergoing biased motion within a simulation. By determining the direction of the biased motion, we create a directed network of proteins moving in relation to one another. This information is useful for generating hypotheses regarding the dynamic outcomes of regulatory inputs to a macromolecular complex.We estimated the motion of protein ensembles (i.e. ensemble of positions sampled by the MCMC for a specific protein) by fitting a Gaussian distribution to each ensemble, and quantify the bias by calculating the skewness of the distribution. First, we discretized the conformational space into f motion .To determine the orientation of the motion, we projected the center of mass of proteins (PCOM) on this axis and estimated the asymmetry of the distribution by calculating the skewness value. The sign of this value was used to determine the orientation of the motion . Then, tActivation of the Arp2/3 complex by WASp/Las17 has been studied at the level of conformational changes in the seven-protein core structure. Reconstituted Arp2/3 complexes composed of budding yeast proteins revealed strong similarity to the mammalian 1K8K structure pca dataset represent complexes that are active in actin polymerization and branching.A third criterion for Arp2/3 activation by Las17 is its interaction with Sla1. Sla1 inhibits Las17 and restrains actin polymerization during critical steps in membrane scission pca edges detected in pca interactions between Arc15 and other core Arp2/3 proteins is a reflection of its interactions with highly diffusive accessory proteins.Arc19 and Arc35 are core components of the Arp2/3 complex that acts as scaffolds that maintain the core structure, while Arc15 contributes to the association of Arp2 and Arc40 alone 2 is a useful method for extracting protein complex conformations representing subpopulations of protein complexes embedded within pca-derived datasets. We applied MCMC-PCA2 to the Arp2/3 complex, and analyzed the results of our simulations. As with any modeling approach, interpretations of the data are open for debate and represent hypotheses rather than conclusions. Finally, the quality/completeness of the input data influences the predicted diffusional behavior of the complex as well as the predictive power of the method. The user must remember that the model predictions for the behavior of the complex are limited to the available input data, and as with any PIN should not be assumed to be complete or accurately represent the complex as it exists in a living cell. Nonetheless, in the absence of experimental data, computational methods such as Coev2Net In summary, our analysis suggests that MCMC-PCApcapca has been used as a molecular ruler through the implementation of several linker lengths with an extended length of 2, 4 or 12 nm (Gly4-Ser)N, where N is 1,2 or 6) pca linkers with a combined extended length > 7.3 nm will consistently detect the interaction. Implementation of the (Gly4-Ser)6 linker (24 nm) resulted in the detection of ∼100% of EpoR. The (Gly4-Ser)2 linker (8 nm) detected a fraction of EpoR and ∼100% of EpoR bound to ligand 2 model. It would also be theoretically possible to integrate in our framework a force field modeling the physical interactions between the proteins in the complex. Although it is out of the scope of this study, such extension has the potential to improve the accuracy of the model prediction.The upper bound for interaction between surfaces of interacting proteins is a physical constraint that has been experimentally determined for the DHFR-2 can be a powerful hypothesis generation tool, as the ability to visualize and explore conformations gives the user insight into the conformations that protein complexes adopt in vivo, even when there is little to no structural information. Structural information is indeed important, and future development will include structural information. This method could also be applied to the analysis of PINs built using pair-wise interactions detected by other spatially defined and dynamic methods such as split-ubiqutin pca and OyCD-pca approaches for building PINs by providing a means to predict meta-states and to visualize and interrogate diffusional space among ensembles of proteins in living cells.MCMC-PCADataset S1PIN edges for the extended Arp2/3 complex. Binary interaction matrix for the 18-member extended Arp2/3 complex used in the study. A 2-dimensional representation of the matrix is shown in (XLS)Click here for additional data file.Dataset S2Contact frequency; relates to (XLS)Click here for additional data file.Dataset S3Protein bias, relates to (XLS)Click here for additional data file.Figure S1Applying the knowledge-based potential. Toy model: Assume A interacts with B, B interacts with C, while A and C do not interact. An interaction occurs when the surface-to-surface distance between two proteins is less than 8 nm. A-H: The weighing scheme used to assign probabilities to the surface-to-surface between two interacting proteins . The number of interactions of a protein determines the mean of the distribution. As the number of interactions increases, the mean of the distribution is shifted towards the 8 nm limit. This weighting allows proteins with a large number of interactions (i.e. hubs) to displace larger sub-complexes and also sample conformations with smaller sub-complexes. I-J: For non-interacting protein pairs, we allow for a small number of experimental false negatives in our simulations by lightly penalizing protein-pairs that are within the experimental resolution of the Protein-fragment Complementation Assay.(TIF)Click here for additional data file.Text S1MCMC proof.(PDF)Click here for additional data file.Text S2Posterior sampling method (Metropolis-Hastings selection).(PDF)Click here for additional data file.


Bioactive cyclic peptides derived from natural sources are well studied, particularly those derived from non-ribosomal synthetases in fungi or bacteria. Ribosomally synthesised bioactive disulphide-bonded loops represent a large, naturally enriched library of potential bioactive compounds, worthy of systematic investigation.We examined the distribution of short cyclic loops on the surface of a large number of proteins, especially membrane or extracellular proteins. Available three-dimensional structures highlighted a number of disulphide-bonded loops responsible for the majority of the likely binding interactions in a variety of protein complexes, due to their location at protein-protein interfaces. We find that disulphide-bonded loops at protein-protein interfaces may, but do not necessarily, show biological activity independent of their parent protein. Examining the conservation of short disulphide bonded loops in proteins, we find a small but significant increase in conservation inside these loops compared to surrounding residues. We identify a subset of these loops that exhibit a high relative conservation, particularly among peptide hormones.We conclude that short disulphide-bonded loops are found in a wide variety of biological interactions. They may retain biological activity outside their parent proteins. Such structurally independent peptides may be useful as biologically active templates for the development of novel modulators of protein-protein interactions.The online version of this article (doi:10.1186/1471-2105-15-305) contains supplementary material, which is available to authorized users. Cyclic peptides are macrocyclic peptides which possess where linear peptide side chains or termini are covalently bonded to shape the peptide into a ring. Macrocyclic compounds such as cyclic peptides have been a renewed focus of drug discovery in recent years, and ideMany examples of bioactive and therapeutic natural peptides are known, including antibiotics, naturalα, β, γ, δ and π turns)[β-hairpin turn, which connects two antiparallel β-sheets. These regions are known to be important in protein-protein contacts[The biological activity of naturally occurring ribosomally synthesised peptides have not been subject to any systematic surveys. Disulphide bonds are known to play a key structural role in proteins, stabilising the protein tertiary structure on a large scale, and can also influence quaternary structure via interchain disulphide bonds. However, disulphide bonds can also have a local effect in constraining a much smaller component of the structure. A protein loop is a general term for a protein secondary structural element which is not a helix or sheet, and generally exhibit a lack of hydrogen bonding and high flexibility, and often served to join other secondary structural elements. Protein turns are specific types of loops where the polypeptide chain reverses its overall direction and can be between 1 and 5 residues long (π turns). A specicontacts, and shocontacts, formingcontacts.The idea of finding "self-inhibitory" peptides, where a peptide derived from a protein-protein interface inhibits the formation of that interface, has been previously explored, and it has been found that many of globular protein interactions are dominated by linear peptide segments. Among oβ-turn motif found in numerous proteins, and small macrocyclic peptides containing this motif have been shown to inhibit integrin αVβ3 activity, which plays an important role in tumour metastasis.The use of small macrocyclic peptides to mimic protein loops has also been exploited for the RGD peptides. The RGDTraditionally, protein-protein interaction inhibitors are discovered by screening compounds against a particular known "target" interaction of interest in a biochemical pathway. Our motivation in this study is to harness the vast amount of protein sequence and structural data available to develop a bioinformatic approach to identifying candidate bioactive small macrocyclic peptides from disulphide-bonded protein loops. In contrast to screening compounds against a single target, this method of analysis allows simultaneous identification of modulators against a variety of protein-protein interactions that have been evolutionarily selected for. This type of bioinformatics approach has been previously successfully used to identify peptides from signalling rich juxtamembrane regions that have the ability to modulate platelet function. In thisTo identify short disulphide bonded loops that play a crucial role at Protein-Protein Interfaces, we set out to find known three-dimensional structures of protein complexes mediated by a disulphide bonded loops. We defined short disulphide bonded loops as those equal to or less than eleven residues in length , and excluding cysteine-knot like regions of overlapping disulphide bridges.2 of solvent exposed surface area), and interface residues (those within 3Å of another protein chain) were counted using PyMol[2,380 Protein Data Bank (PDB) structures correspong PyMol. After rng PyMol server, The proteins containing short disulphide-bonded loops shown in TableAlcaligenes sp., E. coli), plants , and animals . They also cover a variety of protein types, including snake venoms, proteinase inhibitors, collagen, and extracellular proteins involved in growth and cell adhesion.Tablede-novo peptide structure prediction webserver. These models were generated from the sequence of the disulphide loop alone. Five PEP-FOLD model structures were generated for each disulphide bonded loop in Tableα atoms, and calculate an RMSD between the crystal structure and model. The complete results are shown in Additional fileIn order to explore the structural independence of short disulphide bonded loops when removed from their parent protein, structural models of the short disulphide loops described in TableThe mean RMSD of the lowest energy PEP-FOLD model to each corresponding crystal structure was 2.468 ±0.767Å. As a point of reference, the ongoing Critical Assessment of Techniques of Protein Structure Prediction (CASP) experiment describes the generation of a homology structure with an accuracy of better than 6.5 Å as "not trivial", and models with an accuracy of 1.5 Å as "high-resolution". Models with an RMSD of 4 Å can be considered as having a broadly correct fold. Using tFor many of the heterodimeric interfaces listed in TableFor the Lymphocyte antigen 96 and Toll-like receptor 4 interaction, a disulphide-bonded loop CHGHDDDYSFC sits on the conserved "A" patch of Lymphocyte antigen 96. Mutations of five separate amino acids in this peptide have been shown to disrupt binding, including both cysteine residues, implying that the cyclic nature of the peptide is important for complex formation.A CSYYQSC disulphide-bonded loop contained within Vitronectin is located at the interface of two protein-protein complexes, those of Vitronectin with Plasminogen activator inhibitor-1 and Vitronectin with Urokinase plasminogen activator receptor. The relevance of the short disulphide-bonded loop to the interaction of Vitronectin and Plasminogen activator inhibitor-1 has been experimentally verified. Alanine scanning shows that deletion of Asp22, Glu23, Leu24, Tyr27 and Tyr28 significantly reduces binding affinity. The cyclic region of the interface covers the Cys25 to Cys31 region, including 2 of the 5 critical residues. The samThe Epidermal growth factor disulphide-bonded loop CVVGYIGERC interacts with domain I and III of the Epidermal growth factor receptor. The posα-1(IV) homodimer, Palmitoyl-protein thioesterase homodimer).Along with their role at the binding interfaces between different proteins, it can be seen from TableFor the cases discussed above , the disulphide-bonded loop contains all or part of the protein region responsible for the interaction, and it would be interesting to determine if the disulphide-bonded loop portion of the interface alone is sufficient to modulate the protein-protein interaction.For one of the interfaces in TableThe inhibitory abilities of the disulphide-bonded loop alone has been demonstrated by Domingo et al., who desTo investigate if human disulphide-bonded loops at PDB interfaces can have biological activity when extracted from their parent protein, we tested whether the EGF disulphide-bonded loop identified in TableThese loop-derived small macrocyclic peptides were synthesised separately and tested for their ability to 1) activate the EGF receptor and 2) competitively inhibit the EGF receptor in the presence of native EGF. Western blotting was used to assess the amount of phosphorylated EGFR after treatment with EGF, small macrocyclic peptide, or EGF following incubation with small macrocyclic peptide, as a proxy for activation. However none of the selected peptides demonstrated any ability to either activate or inhibit the EGF receptor . The EGFR protein comprises three structural domains . EGF activates EGFR by binding to a cavity between EGFR domain I and III, with binding sites existing on both domain I and III. The CVVIVO. The The cyclic-peptide mediated interfaces above represent an interesting set of compounds, but it is also of interest to see if disulphide-bonded loops represent a widely used natural strategy to influence protein-protein interactions, by examining evolutionary conservation of short disulphide-bonded loops in proteins.A dataset of short disulphide-bonded loop containing proteins was assembled from the SwissProt database of manually annotated proteins. Searching for all SwissProt proteins containing short disulphide bonded loops  revealed 8607 annotated short disulphide-bonded loops in 5989 proteins  which haConsidering a small disulphide-bonded loop as a surface-exposed loop pinned in place by a pair of cysteines, the structure of this loop could be relatively independent of the sequence of the larger protein, and mostly determined by the amino acids between the cysteines, due to the conformational constraint imposed by the disulphide bond, and the solvent exposed nature of a surface loop. Thus, a disulphide-bonded loop known to be at a protein-protein interface could on its own, potentially maintain the same binding activity as the parent protein. Since cyclic peptides are known to be more "drug-like" than linear peptides, this approach could provide a promising set of biologically-optimised lead-like molecules to attack the difficult problem of modulating protein-protein interactions. We haveA disadvantage of re-purposing disulphide bonded loops as small macrocyclic peptides is that disulphide bonds are more easily broken than the amide bonds present in a traditional head-tail cyclic peptides. Nyugen et al. have comDespite being mainly restricted to extracellular or vesicular compartments, short disulphide-bonded loops are relatively widespread with 720 out of a total of 20,252 human proteins in the SwissProt database of manually curated proteins contain annotated disulphide-bonded loops. Disulphide-bonded loop residues are more conserved than non-cyclic residues in the same set of proteins, and are also more conserved than residues located directly beside short disulphide-bonded loops. While the conservation is modest, there is a subset of peptides which show marked conservation  structures containing short disulphide-bonded loops were identified by searching UNIPROT for manually curated proteins containing a non-overlapping disulphide loop with 2-9 internal residues, with a listed entry in the PDB. The biological assembly format (.pdb1) of each PDB structure was downloaded. Choosing the biological assembly format ensures that the downloaded structure is the biologically relevant form of the structure, as opposed to the crystallographic asymmetric unit, which are not always the same thing. A PyMol script wμM small macrocyclic peptide or 10 nM EGF for 5 minutes. For EGF/small macrocyclic peptide competition assays, the cells were incubated with 100 μM small macrocyclic peptide for 5 minutes before incubating for another 5 minutes with 1 nM EGF. EGF Receptor activation was then measured by western blotting for phosphorylated Tyrosine 1173 on EGFR, along with blotting for total EGFR and total Actin.MCF10A immortalised breast epithelial cells were serum starved for 3 hours before all experiments. For activation experiments, cells were incubated with either 100 β sheet or an α helix, and 4) had an average solvent accessibility of the region that was predicted to be more exposed than buried.Likely short disulphide-bonded loop locations were predicted based on sequence, secondary structure and solvent accessibility information. Secondary structure and solvent accessibility were predicted by the Porter and PaleAle servers from the Distill suite ofThe similarity of two disulphide-bonded loop sequences was found by aligning the disulphide-bonded loop sequences, excluding the flanking cysteine residues, using the Bio.pairwise2.align function from the BioPython package,Human and virus proteins containing SwissProt annotated short disulphide-bonded loop regions were identified or predicted using DISTILL as described above. To identify similar viral proteins, we set out to find 1) disulphide-bonded loops with similar sequences in human and viral proteins 2) where the viral proteins had known human interactor proteins 3) where the human proteins interacting with the viral proteins containing a short disulphide-bonded loop also interacted with the original human proteins containing the similar short disulphide-bonded loop.Uniprot accession numbers were used to uniquely identify proteins, and pairs of Uniprot accessions parsed from the interaction files were used to identify interactions. Similarity between human and virus short disulphide-bonded loop sequences was calculated by aligning and scoring the peptides using the BLOSUM62 substitution matrix to identify similar short disulphide-bonded loops in host and virus. Virus and human short disulphide-bonded loops with a similarity of greater than 0.50 were identified, and checked for shared interactors.To find which human and virus proteins shared interactors, the sets of known human protein interactions (including human-virus interactions) were downloaded as PSI-MITAB format text files. A text file of human binary protein interactions was downloaded from the MINT databaseSupplementary material. Contains supplementary tables and figures referred to in the article. (PDF 1 MB)Additional file 1: