Protein-protein interactions are crucial for normal biological processes and to regulate cellular reactions that affect gene expression and function. Several previous studies have emphasized the roles of residues at the interface of protein-protein complexes in conferring stability and specificity to the complex. Interface residues in a protein are well known for their interactions with sidechain and main chain atoms with the interacting protein. However, the extent of intra-protein interactions involving interface residues in a protein-protein complex and their relative contribution in comparison to inter-protein interactions are not clearly understood. This paper probes this feature using a dataset of protein-protein complexes of known 3-D structure.We have analysed a dataset of 45 transient protein-protein complex structures with at least one of the interacting proteins with a known structure available also in the unbound form. We observe that a large proportion of interface residues  are involved in intra and inter-protein interactions simultaneously. The amino acid propensities of such interfacial residues involved in bifurcated interactions are found to be highly similar to the general propensities to occur at protein-protein interfaces. Finally, we observe that a majority (83%) of intra-protein interactions of interface residues with bifurcated interactions, are also observed in the protein uncomplexed form.We have shown, to the best of our knowledge for the first time, that a vast majority of the protein-protein interface residues are involved in extensive intra-protein interactions apart from inter-protein interactions. For a majority of such interface residues the microenvironment in the tertiary structure is pre-formed and retained upon complex formation with its cognate partner during transient interactions.This article was reviewed by Arumay Pal and Mallur Madhusudhan.The online version of this article (10.1186/s13062-019-0232-2) contains supplementary material, which is available to authorized users. Association between two or more proteins is central to many cellular processes . These aIn transient protein-protein complexes, the strength of association between proteins is also an important feature that must be maintained at precise levels depending upon the proteins involved and desired time of association between proteins before they disassociate . Indeed,Obviously, residues at the protein-protein interface play a crucial role in conferring right level of stability of the complex, as well as in conferring specificity for the association between proteins and their precise 3-D structure. Mutation of such residues can compromise on the stability and/or specificity of the proteins concerned and their complex leading to disease states  and otheHowever, it is currently not clear, what is the contribution of the interfacial residues in their interactions within the protein? For example, an interfacial residue, like Arginine, may form simultaneous hydrogen bonds within the protein and with the associated protein.The present work analyzes known 3-D structures of protein-protein complexes, with a view to understand the extent of interaction of interfacial residues within the protein (intra-protein interactions), apart from interacting with residues in the interface of associated protein (inter-protein interactions).According to the earlier literature, at least 176 transient protein-protein complexes are known to have structural information available in both bound and unbound forms , 10. ForA pair of atoms, one from each of the two proteins in a protein-protein complex, that are involved in interactions are considered as interfacial atoms. Interacting atoms were identified using PIC server  (see belBoth inter-protein and intra-protein interactions were identified using PIC server . If an iThe propensities of amino acids to be present at the interface and engaged in bifurcated interactions (both intra- and inter-protein) were measured as per the standard Chou-Fasman  type proα -Cα distance are considered and energies were calculated considering the nature of interaction. Energy is measured as the sum of van der Waals, electrostatics and hydrogen bond interactions. The energy contributions of these types of interactions are as per enthalpic calculations. Additionally, a distance-dependent dielectric has been used and hydrogen bonds are analysed after fixing hydrogen atoms.PPCheck was used to identify and quantify interactions in protein-protein interfaces . ResidueIn order to measure the strength of interactions harboured by interface residues which are involved in bifurcated interactions, intra-protein interactions were initially identified using the ‘intra-protein interaction’ option in PIC server. For each interface residue with bifurcated interactions, the micro-environments of all interacting intra-protein residues were alone calculated for PPCheck calculations for intra-protein interactions. Where multiple interactions are observed between two residues, the total energy of all the interactions between the residues is associated with the residue pair.In the current analysis, we have used a dataset of 45 protein-protein complexes of known crystal structure (determined at 2 Å or better resolution) with 3-D structure of at least one of the proteins in every complex available in the uncomplexed form  except for Gln and Lys. While the tendency of Lys not to be involved in intra-protein interaction could be due tothe localized nature of -NHPropensities of each of the 20 residue types to occur in the interface have been reported in several previous publications. In this work, propensities of each of the 20 residue types to form simultaneous inter and intra-protein interactions have been calculated. In order to maintain the consistency, propensities to occur in the interface have also been calculated to facilitate a convenient comparison Fig. . All theThe residue types low propensity (< 1) to occur in protein-protein interface also show low propensity (< 1) to form simultaneous intra- and inter-protein interactions. Therefore, formation of simultaneous intra- and inter-protein interactions is a general feature of interfacial residues almost irrespective of residue types.Figures Energy values associated with interactions made by the sidechain atoms of interfacial residues, in the protein-protein complex structure, with surrounding atoms were calculated using PPCheck . The totFrom Fig. This learning is strengthened further by the scatter plot shown in Fig. While it is evident that residue types which prefer to be at protein-protein interfaces are also involved in intra-protein interactions, it is not clear how far individual interfacial residues contribute towards inter and intra-protein interaction energies.Figure Interestingly, in the examples of protein-protein complex structures, illustrated in Figs. In this paper, we demonstrate and highlight the fact that residues at the protein-protein interfaces contribute substantially to the stability of the complex, not only by inter-protein interactions, but also by intra-protein interactions. Clearly, the shape, conformation, chemical nature and nature and extent of dynamics associated with the interface in a protein are quite important in conferring stability and specificity of protein-protein complexes . TherefoWe also show that intra-protein interactions are a general feature of almost all the interfacial residues. The nature and extent of energy contribution in such “self-stabilizing” interactions differ between interfacial residues. The energy contribution from intra-protein interactions is shown to be quite substantial. Residue types with good propensity for simultaneous intra and inter protein interactions include hydrophobic residues Leu, Phe, Trp and Met and also polar residues Asp, Glu, His and Arg. Therefore, the simultaneous intra and inter-protein interactions include various kinds, such as interactions between hydrophobic groups and hydrogen bonds.The learning from this work encourages one to consider intra-protein interactions by the interfacial residues, apart from inter-protein interactions, while designing site-directed mutants, tinkering the stability/specificity of a protein-protein complex and in the de novo design of protein-protein complexes.Comment:In this work, Jayashree S. et al. analyzed the residue interactions that occur in their dataset of 45 high-resolution transient protein-protein binary interfaces using in-house programmes. They defined the interaction of interface residues into two categories- ‘solely inter-protein’ and ‘bifurcated’ (simultaneous intra and inter-protein interactions). The major findings include- i) 75% of the interface residues are of bifurcated type, ii) Bifurcated residue propensities are similar to residue propensities in PP-interfaces where aromatic, hydrophobic and charged side chains (except Lys) occur more compared to polar side chains, and iii) the energy contribution of the interface residues are higher for intra-protein interactions than inter-protein interactions in general, though the later can be equal to or even more than the former in cases. The findings can be useful for the better understanding of the structural aspects of transient PPI, which in turn can be useful for PPI design. The write-up, standard of English and data presentation is satisfactory. I would like to recommend the work suitable to be published in Biology Direct.Response:We thank the reviewer for a nice summary of our work and for highlighting positive aspects in our work.Comment:Following minor issues are needed to be addressed before publishing. I would like to highlight a few general comments as well. Minor issues: 1) The definition of an interface residue is not clear. 2) Authors found that 75% of the interface residues are of bifurcated type. While this is the overall statistic, it will be interesting to see the distribution of the percentage of such residues in each interface. For example, an interface full of  bifurcated residues should be more suitable to design an inhibitory peptide. 3) Comment about the rest 25% interface residues and their propensities. 4) The dataset can be divided into two parts depending on the extent of conformational changes (low and high) upon binding. Will be interesting to see if there are differences in trends for the two sets, as found in the case of 4 complexes that undergo large conformational changes. 5) Methods (Identification and categorization of residue-residue interactions) – ‘It is possible that the same residue pair could be listed in more than one type if there are simultaneous van der Waals and hydrogen bond interactions etc. In such instances, interaction at the residue pair was counted only once.’- which interaction is given preference and why? 6) Fig. Response:1) We have defined the interface residues better in the revised manuscript (Section 2 under Methods).2) A new supplementary table  has been included with % of interfacial residues involved in bifurcated interactions for every protein chain used in the data set. A histogram showing the distribution is also included .3) We have now commented on the 25% of the cases in the revised manuscript (towards the end of first subsection under Results and Discussion.4) We thank the reviewer for this nice suggestion. However, we are facing two problems in performing this analysis at the present time: (1) Data set for this analysis require 3-D structures of both protein-protein complex and 3-D structures of proteins involved in their uncomplexed forms. Though we have used such a data set in our analysis, the number of protein-protein complexes which show substantial change in conformation upon complexation is too few to show a clear pattern compared to the complexes which do not change the structure significantly upon binding. (2) In some of the complexes of two proteins, one of the proteins undergo substantial conformational change upon binding, while the other does not show much conformational change. Such complexes cause difficulty in classifying them into “low conformational change complex” or “high conformational change complex”.We feel that this interesting project should be carried out when the large dataset could be formed, with clarity in the definition of protein-protein complexes with low and high conformational change.5) We are sorry that these statements look misleading. We have now rewritten this part to give the correct message. Basically, it is possible that the same residue pair could be listed in more than one type if there are simultaneous van der Waals and hydrogen bond interactions etc. In such instances, the pair with interacting residues was counted only once, though the number of interactions between the same two residues could be more than one.6) Thanks for this suggestion. Figure has been modified to show the least squares line and vertical & horizontal lines at propensity value of 1. Correlation-coefficient value is provided in the legend to the figure.7) Figs. are protein structural pictures in white background. When we tried the suggested colours, they were not shown well in the figure. Therefore, we prefer to leave these figures unchanged. However, we are open for any suggestion that will improve the figures.8) All the suggested changes in Fig. have been made (increase in font size and least squares line). .Correlation-coefficient value is provided in the legend to the figure.9) All the suggested changes in Fig. have been made. Correlation-coefficient value is provided in the legend.General comments: 1) Conservation of residues involved in ‘solely inter-protein’ vs. ‘bifurcated’ interactions can be checked. 2) An abstract graphics could be used to describe the concept and the major findings easily. 3) Since Fig. Response:Concerning point 1 above, as can be seen from the section on propensity calculations, the propensities of residue types to form simultaneous intra- and inter-protein interactions are very close to the general propensities of residue types to occur in protein-protein interfaces in general. It is well known in the literature that protein-protein interfacial residues are reasonably well conserved . Therefore, it is only expected that the residues which form bifurcated interactions and those which are involved in inter-protein interactions are reasonably well conserved.Regarding graphical abstract (point 2 above), we will be happy to provide one if journal requires it.Regarding coupling Fig. in a single figure, we see the point of the reviewer. But, it will compromise on the discussion of Fig. as 4C is discussed in relation to 4B and 4A. However, if it is strongly felt that these figures should be combined into one, we will do our best in rewriting those sections not to affect the readability of the paper.Comment:Typos: 1. Methods, line 51 - categorization 2. Line 53 - A fullstop after  3. ResulResponse:Thanks. All these typos have been corrected in the revised manuscript.Comment:This manuscript attempts to categorise interface residues according to whether they mediate only inter-chain interactions or whether they participate in both inter- and intra-chain interactions. Propensity values for all 20 amino acids are drawn on this basis. The results could however be interpreted as being a trivial outcome of amino acid size. One potentially interesting aspect of this study is the observation that the interaction environment of the residues in the uncompleted and complexed forms are similar. This idea is however not explored in detail.Response:The main take-home message of our work is that the most of the interfacial residues in a transient protein-protein complex are also involved in intra-protein interactions. To the best of our knowledge, this has not been shown previously using a systematic analysis. Secondly, it is our belief that this is an important result as this is likely to have important implications in engineering protein-protein interactions, in the design of inhibitors of protein-protein complexation etc.Addressing the point of size dependency of residue types on the tendency to form bifurcated interactions, it must be noted from the section on the propensity calculations that both long/bulky sidechains  and not so long sidechains  show tendency to form bifurcated interactions. Based on such observations, we think that there is no clear size dependency on the tendency to form simultaneous intra- and inter-protein interactions.Concerning the point made by the reviewer on our results of comparison of complexed and uncomplexed structural forms of proteins, our main message is that most of the residues involved in simultaneous intra- and inter-protein interactions in the complexed form are also involved in intra-protein interactions in the uncomplexed form. We believe that we have presented concrete data and discussed it in the manuscript in sufficient detail.Comment:The manuscript by Srinivasan and coworkers attempts to decipher the roles of residues on protein-protein interfaces, specifically of interfaces involved in transient interactions. The authors have bifurcated residues at the interface into two types – those that make interactions only with the interactor (inter-) and those that make interactions with the interactor and residues of their own protein (intra/inter-). The main results of the finding are that a large number of residues belong to the latter category and there is seemingly no preference of amino acid type in defining one type over another. More interestingly, the authors point out that when they analyze the protein structures in the uncomplexed form, the interaction of intra/inter- residue tend to conserve their interaction environment. While the manuscript is clearly written several of the analysis are not convincing.Response:We thank the reviewer for the comments and constructive criticisms on our work. We find it helpful in strengthening our work. We are providing our point-by-point response below.Comment:The authors should address the following criticisms and comments 1. Why have the authors only considered transient complexes? This choice has not been justified. Presumably, this inter and intra/inter property of residues would be a feature of all protein-protein interactions (as implied in the opening section of the manuscript). Why then were transient interactions selected? Is there reason to believe that the behavior of interface residue in transient interactions are different than those mediating obligate interactions?Response:Questions we addressed in our work and the analysis we carried out requires availability of experimentally determined 3-D structures protein-protein complexes and structures of same proteins in the uncomplexed form. These conditions are necessarily met only by the transient protein-protein complexes as the permanent complexes, by definition, are not stable in isolation (uncomplexed form) and therefore cannot be crystallized in isolation.Further, as also commented by the other reviewer of our paper, we believe performing this analysis on the transient complexes would be more useful, especially in the context of design of small molecules that target protein-protein interfaces. We believe targeting the interface of permanent complexes is less attractive and more challenging as the chances of success appear very small.Having said this, suggestion of the reviewer is well taken – in a separate and explicit project, we will analyze the interfaces of permanent complexes for simultaneous intra- and inter-protein interactions.Comment:2. The authors have in different parts of the manuscript expressed surprise at the proportion of intra/inter- interacting residues. They also point out there appears to be no residue-type preference to be an intra or intra/inter- residue. This reviewer has an alternate explanation, which is apparent from Figs. Response:We thank the reviewer for an alternate interpretation of our data. As mentioned above, we find both long  and short sidechains  in the dataset of interfacial residues which are involved in simultaneous intra and inter-protein interactions. Reviewer has also noted the behaviour of Lys and Gln as deviant from the hypothesis on size dependency.But it must be noted that the main new conclusion we report in our paper, that most of the interfacial residues are involved in bifurcated interactions, is strongly supported by our data analysis irrespective of the interpretations on the nature of sidechains involved.We feel that energy calculations are necessary in order to reach an understanding on the comparative strengths of intra- and inter-protein interactions.Comment:3. The one interesting observation of this study is that interface residue in uncomplexed monomers tend to preserve the interaction environment when they are part of a complex. However, the data shown in support of this claim need to be more substantive. This is a crucial part of the analysis and is likely to be of some importance to researchers in the field. The authors should concentrate on getting more concrete evidence of this fact. In their analysis 78% (the authors report this as 83% by discounting 4 poorly performing cases) of the interactions are common to the uncomplexed and complexed cases  of common residues involved in intra-protein interactions in complexed and free forms, we believe that there is no doubt on our conclusion. We would set out to address fresh useful questions on this aspect in a separate project. We thank the reviewer for this suggestion.Comment:4. An important control is missing in this study. The authors claim that interface residues contribute significantly to intra-protein interactions. Their interpretation is that this strengthens the protein-protein interaction. Another way of looking at it would be that these residues are contributing to the integrity of the interface structure. The authors should contract this with other residues on the surface that are not known to be a part of any interface and how these residues interact with other residues of the same protein.Response:We agree with the reviewer that intra-protein interactions by the interfacial residues contribute to the integrity of the conformation of the interfacial region. In fact, we did not mean that intra-protein interactions directly contribute to strengthening protein-protein interaction. We have ensured that, in the revised manuscript, we did not give the impression of intra-protein interactions contributing directly to the strength of protein-protein association.Comment:Minor Points: 1. The manuscript has many places where it would help if the analysis were quantitative instead of qualitative. Instances of this could be found on lines such as – “..residues involved in most of the inter…”; “..20 residue types are reasonably well correlated with ..”; “Reasonably good correlation …”; etc. 2. It would be better to colour the atoms in Figs. Response:Regarding point 1 above, we have ensured that in the revised manuscript we provide quantitative information  to support the statements. Correlation coefficient values are now mentioned in the legends to appropriate figures.On the point 2 above, we have ensured that the two proteins involved in various panels in Figs. and interactions are clearly shown in distinct colours. There are no het atoms involved in these figures.Regarding Fig. (point 3 above), we have re-done the figure with better clarity.On the point 4 above, Fig. provides a comparison of intra-protein interaction energy and inter-protein interaction energy. This figure provides information on strengths of intra-protein and inter-protein interactions. Such a information is pertinent to our paper, as we report extensive involvement of interfacial residues in the intra-protein interaction as well. As Fig. provides useful and relevant information we would like to retain this figure and associated discussion in our manuscript. But, we are open to removing this figure and associated discussion, if it is strongly felt that this figure should be deleted.The references of Chou-Fasman and PIC are included.Additional file 1:Table S1. List of transient protein-protein complexes used in the analysis (shown in green colour). PDB code of the unbound form of protein 1 is shown in pink. Where available, the PDB code of the unbound form of the second subunit is also noted (as shown in blue colour). (XLSX 16 kb)Additional file 2:Table S2. List of PDB chains and percentage interface residues involved in bifurcated (both intra- and inter-chain) interactions. The first column shows the PDB code as well as the interacting chains. (XLSX 13 kb)Additional file 3:Figure S1. Percentage distribution of interfacial residues involved in bifurcated interactions in the protein-protein complex structures used in the current study. (TIFF 569 kb)Additional file 4:Table S3. List of interacting residues in the bound and unbound forms for those interface residues which are involved in bifurcated interactions.


Therefore, identifying lncRNA–protein interactions is of great significance to understand the molecular functions of lncRNAs. Since the experiments to identify lncRNA–protein interactions are always costly and time consuming, computational methods are developed as alternative approaches. However, existing lncRNA–protein interaction predictors usually require prior knowledge of lncRNA–protein interactions with experimental evidences. Their performances are limited due to the number of known lncRNA–protein interactions. In this paper, we explored a novel way to predict lncRNA–protein interactions without direct prior knowledge. MiRNAs were picked up as mediators to estimate potential interactions between lncRNAs and proteins. By validating our results based on known lncRNA–protein interactions, our method achieved an AUROC (Area Under Receiver Operating Curve) of 0.821, which is comparable to the state-of-the-art methods. Moreover, our method achieved an improved AUROC of 0.852 by further expanding the training dataset. We believe that our method can be a useful supplement to the existing methods, as it provides an alternative way to estimate lncRNA–protein interactions in a heterogeneous network without direct prior knowledge. All data and codes of this work can be downloaded from GitHub ( Non-coding RNAs (ncRNAs) refer to RNAs that do not encode proteins. These genes were once considered as “junk DNAs” or “dark matters” in the genome . HoweverLong non-coding RNAs are ncRNAs with a length larger than 200 nt . ExperimAlthough we can now obtain RPIs (RNA–protein interactions) through large-scale experiments such as RNAcompete , RIP-ChiAll existing methods rely on known lncRNA–protein interactions to construct the predictor. However, the number of experimentally verified lncRNA–protein interactions is limited, which affects the prediction performances of all existing methods. To expand the spectrum of predictable lncRNA–protein interactions, we took miRNAs as intermediates in predicting lncRNA–protein interactions.-16).MiRNAs are short RNA molecules with a length of 19 to 25 nucleotides . Some miIn the light of this observation, miRNAs were taken as mediators to predict novel lncRNA–protein interactions in this work. Both lncRNA–miRNA interactions and miRNA–protein interactions were considered as the basis to predict lncRNA–protein interactions. In the cause of improving our prediction performance, the similarity of lncRNAs and proteins was calculated in various aspects, which is based on the assumption that similar lncRNAs or proteins tend to have similar interactions. Our methods provide a way to explore novel lncRNA–protein interactions without prior knowledge of direct lncRNA–protein interactions. Since existing methods always require direct lncRNA–protein interactions as training data, our method may provide a useful supplement to the state-of-the-arts methods.Biomolecule interactions have become a hot research topic in computational biology. RAID v2.0 is a large database for biomolecule interaction information, which contains more than 5.27 million RNA-associated interactions, including over 4 million RNA–RNA interactions and 1.2 million RNA–protein interactions, involving nearly 130,000 genes across 60 species . We downvia a miRNA, and vice versa, common miRNAs were extracted from these interactions. Altogether 360 miRNAs were included in our dataset. Subsequently, the lncRNA–miRNA interactions and the protein–miRNA interactions were selected according to the common interacting miRNAs. We kept 1,356 lncRNA–miRNA interactions and 1,156 protein–miRNA interactions in our dataset. These interactions are among 331 lncRNAs, 360 miRNAs, and 103 proteins. The sequences of lncRNAs and proteins were obtained from NCBI Gene database , we define the set of its interacting lncRNAs as L(km), which is a subset of L:For a given miRNA, P(km), which is a subset of P, as follows:We also define M, of which the network contribution in the lncRNA–miRNA interaction network or the protein–miRNA interaction network can be calculated respectively as follows:For miRNAs in c1(km) is the network contribution of miRNA km in the lncRNA–miRNA interaction network, c2(km) the network contribution of miRNA km in the protein–miRNA interaction network, and |.| the cardinal operator on a set.where M(il) and M(jp), which are both subsets of M, are defined as follows:For convenience, M(il) and M(jp) represent the set of miRNAs that interact with a given lncRNA or a given protein.where ul and vl  can be defined as follows:With all definition above, the network similarity between two lncRNAs n1 is the network similarity between ul and vl.where up and vp, the network similarity between up and vp can be defined as follows:Similarly, given two proteins, n2 is the network similarity between up and vp.where The sequence similarity was calculated by the Smith-Waterman algorithm. Given two lncRNAs, the sequence similarity between two lncRNA sequences is defined as follows:e1 is the sequence similarity, w the Smith-Waterman score between ul and vl, and |ul| and |vl| the length of the lncRNA ul and vl, respectively.where Given two proteins, the sequence similarity between two protein sequences is defined similarly as follows:e2 is the sequence similarity, and w, |up|, and |vp| the length of the protein up and vp, respectively.where Pseudo-amino acid composition (PseAAC), which was proposed by Chou in 2001 , has beeIn this work, we employed pseudo di-nucleotide composition (PseDNC), which is a special form of PseKNC when k = 2, to represent lncRNA sequences, and PseAAC for protein sequencesFor simplicity, we do not describe the computational details of the PseDNC and PseAAC algorithms here. We only describe how we apply PseDNC and PseAAC in our work.Given a lncRNA, its PseDNC representation can be described as a numerical vector with 16+λ dimensions as follows:V1 is the PseDNC representation of il, λ and ω1 two parameters in computing the PseDNC representation, and H a set of di-nucleotide physicochemical properties that are applied in computing the PseDNC representations.where The similarity between two lncRNAs can be defined as follows:f1 is the feature similarity between two lncRNAs, and ||.|| the operator that takes the length of a vector.where Similarly, given a protein, its PseAAC representation can be described as a numerical vector with 20 + τ dimensions as follows:V2 is the PseAAC representation of jl, τ and ω2 two parameters in computing the PseAAC representation, and H a set of amino acid physicochemical properties that are used in computing the PseAAC representations.where The similarity between two proteins can be defined as follows:f2 is the feature similarity between two proteins.where We utilized online webserver Pse-In-One  to generG1 and G2, we can construct a heterogeneous network model, where lncRNAs, miRNAs, and proteins are connected together. A part of this network is illustrated as il and a protein jp, a whole network correlation that is brought by the km can be defined as follows:By integrating the bipartite graph il and protein jp can be defined as follows:The whole network correlation function between lncRNA K= {k}, i = 1, 2,.., x and j = 1, 2,…, z.The whole network correlation matrix can be established as s1, where ul and vl are two lncRNAs. Similarly, for two given proteins, the similarity between them can be noted as s2. The similarity between two lncRNAs or two proteins can be measured in various aspects, which have been elaborated in the above section.For two given lncRNAs, the similarity between them can be noted as S1= {s1}, u, v = 1, 2,…, x and S2 = {s2}, u, v = 1, 2,…, z, respectively. We normalize the values in matrix S1 and S2 as follows:The similarity matrix for lncRNAs and proteins can be established as q1 and q2 are normalized value in S1 and S2. We note the normalized matrix as Q1 and Q2 respectively, where Q1= {q1}, u, v = 1, 2,…, x and Q2 = {q2}, u, v = 1, 2,…, z.where With all above definitions, we can establish the final scoring matrix as follows:W. If a value in W were larger than a given threshold, the corresponding lncRNA and protein would be predicted to interact. Otherwise, no interaction would be predicted.The prediction of lncRNA–protein interactions is made based on the scores in The whole flowchart of our method is illustrated in W. By comparing this set against the testing dataset, the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) can be obtained, respectively. Five statistical measures can be calculated as follows:Given a threshold, a set of lncRNA–protein interactions can be predicted from the matrix TPR is for true positive rate, FPR for false positive rate, Pre for precision, Rec for recall, and Acc for accuracy.where W, a receiver operating curve (ROC) can be plotted using the TPR and the FPR values. In the meantime, a precision-recall (PR) curve can be obtained using the precision and the recall values. Due to the nature that the negatives are far more than the positives in the current topic, the area under the ROC (AUROC) and the area under the PR curve (AUPR) are used both as primary performance measures of our method.By varying the threshold from the maximum value to the minimal value in λ varies from 10 to 20 with a step of 1, ω1 from 0.1 to 1 with a step of 0.1, τ from 10 to 20 with a step of 1, and ω2 from 0.05 to 0.5 with a step of 0.05. We finally choose λ = 10, ω1 = 0.1, τ = 11 and ω2 = 0.5. The physicochemical properties in the PseDNC are Rise, Tilt, Twist, Slide, Shift, and Roll, which are defined in Pse-In-One  to see the effect of the other matrix solely. The ROC and the PR curve of all six different configurations are illustrated in In order to analyze the effect of every single similarity matrix individually, we combined every single similarity matrix solely with the whole network correlation matrix, respectively. In other words, either HeteSim is a widely applied measure, which aims at quantifying the correlation of nodes in a heterogeneous network . It has In order to evaluate the actual prediction effect of our method, we selected 20 interactions that are top ranked in our results. These interactions are recorded in Since only experimentally verified interactions were obtained to compose our benchmarking dataset, a large number of predicted interactions in the RAID V2.0 database were discarded. We incorporated these predicted interactions to optimize our method. A total of 20,425 lncRNA–miRNA interactions and 1,349 protein–miRNA interactions were extracted while sharing a common set of miRNAs. These interactions are among 1,133 lncRNAs, 464 miRNAs, and 113 proteins. We also collected 2,803 lncRNA–protein interactions as our independent testing dataset. Altogether 615 lncRNAs and 65 proteins were included in this testing dataset. Our method achieved an AUROC of 0.852 on this dataset Figure 8Due to the mechanism of our method, we restricted the lncRNA–protein interactions within those lncRNAs and proteins, which can find a sharing miRNA interactor. This restriction narrowed the profile of applicable data in the database. There are 2,862 experimentally verified lncRNA–miRNA interactions in the RAID v2.0 database, including 358 lncRNAs and 1,208 miRNAs; 1,356 lncRNA–miRNA interactions between 331 lncRNAs and 360 miRNAs were utilized in this work, accounting for 47.4%, 92.5%, and 29.8% of which in the whole database, respectively. There are 2,521 experimentally verified protein–miRNA interactions between 144 proteins and 1,032 miRNAs in the RAID v2.0 database; 1,156 protein–miRNA interactions of them were selected as our training data, composed by 103 proteins and 360 miRNAs. The protein–miRNA interactions, proteins, and miRNAs take up 45.8%, 71.5%, and 34.9% of the entire RAID database, respectively. There are 40,668 experimentally verified lncRNA–protein interactions in the RAID v2.0 database, including 3,066 lncRNAs and 10,224 proteins. The testing dataset in our work including 1,981 verified lncRNA–protein interactions between 266 lncRNAs and 58 proteins, taking up 4.87%, 8.7%, and 0.57% of which in the database, respectively. Due to the limited number of known miRNA–protein interactions and miRNA–lncRNA interactions, the coverage of proteins in the whole database is low.We admit that this will limit the application scope of our method. However, we believe this will get better when the number of available miRNA–protein interactions is increased, because the statistical test has already shown that the lncRNA–protein interactions are significantly enriched in the set of lncRNAs and proteins that are sharing a common set of miRNAs.LncRNAs can affect biological processes from various levels. It is of great importance to study the molecular functions of lncRNAs. In the meanwhile, LncRNAs perform their role mostly by their interaction with proteins. Therefore, lncRNA–protein interaction should be studied in detail. In this paper, we proposed a method to predict lncRNA–protein interactions without prior knowledge of existing lncRNA–protein interactions. Instead, we utilized the lncRNA–miRNA interactions and the miRNA–protein interactions as the basis of our prediction. The miRNAs are used as mediators to connect the realm of lncRNAs and the realm of proteins. This is based on the hypothesis that a lncRNA and a protein may interact if they share interacting miRNAs. By quantitatively modelling the heterogeneous network that is formed by lncRNAs, miRNA, and proteins, we developed a simple, yet effective, method to predict the lncRNA–protein interactions. The best similarity measure in our method is the network similarity, which does not rely on sequence information. This gives our method a unique capability to predict lncRNA–protein interaction without comprehensive sequence information of both interactors. By comparing our predictions to the known lncRNA–protein interactions, we can conclude that our method has, at least, a comparable prediction performance to the state-of-the-art methods. Since our method does not rely on prior knowledge of lncRNA–protein interactions, it is a helpful supplement to existing methods.All datasets generated for this study are included in the article/Y-KZ curated the dataset, designed the algorithm, implemented the algorithm, and calibrated the parameters. Z-AS and HY performed the experiments and collected the results. TL, YG, and P-FD investigated the question, designed the whole study, conceptualized the algorithm, analyzed the results, and wrote the manuscript.This work was supported by the National Key R&D Program of China (2018YFC0910405); the National Natural Science Foundation of China (NSFC 61872268); and the Open Project Funding of CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences (CASNDST201705).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers. Rational drug design has revolutionized the pharmaceutical industry with the idea that drugs can be designed or engineered according to an identified protein or DNA target. This is an efficient alternative method in the drug design and discovery areas as opposed to screening thousands of samples extracted from natural products for therapeutic purposes. However, such a rational design of drugs is knowledge-based and requires an understanding of the intermolecular forces involved, as well as an understanding of protein structure and function. Although compounds are created based on the structure of the protein that is targeted, once a basic template is developed several possible combinations of the functional groups that are spatially separated by a particular distance must be investigated to obtain a lead compound to be optimized. In other words, designed compounds have to be optimized for drug-like properties in three-dimensional space. This type of optimization necessitates knowledge of the 3D structures of the protein receptors involved as well as the conformational space of the ligand drug . This mein silico methods provide a faster and less expensive way of screening compounds [Drug creation involves several stages, starting from the design of a compound to lead identification (hit-to-lead), optimization, preclinical and toxicology reports for a New Drug Application (NDA), and final approval from the Food and Drug Administration (FDA) ,5. As drompounds . Howeverompounds ,8,9. In ompounds ,11. Baseompounds ,13. In metc. Hence, among these PPI, at least a sizable number of proteins can be used as drug targets [2. It is reported that standard-sized interfaces are 1200 to 2000 Å2 [2 size normally constitute short-lived and low-stability complexes, and large surfaces ranging from 2000 to 4600 Å2 are observed between proteases and particular inhibitors and between G-proteins and other components of the signal transduction system [2. In addition to this, surfaces of PPI are generally flat and lack the grooves and pockets that are present at the surfaces of proteins that bind to small molecules [Cells communicate with one another via protein-protein interactions. All of the physiological processes of life are controlled via interactions of different proteins that are well regulated. In an organism, PPI form a huge complex network known as an “interactome,” which contributes significantly to the biological processes that are carried out in that organism . It is e targets ,17,18. M targets . Althoug targets . How one 2000 Å2 . Smallern system . In compolecules ,24,25,26olecules ,28 and dolecules . Since PThe most significant contribution to understanding the PPI surface comes from structural biology via X-ray crystallography or NMR as well as mutational studies. Detailed 3D structure analysis of PPI revealed that PPI interfaces can form a continuous epitope that often forms a single secondary structure or discontinuous epitope originating from multiple secondary structures. These continuous or discontinuous epitopes form small pockets and groove-like structures that together form a binding site for proteins. Protein-protein interaction surfaces are generally hydrophobic in nature. This was assessed by measuring the area of accessible surface on the protein surface that forms the interface region of partner proteins that becomes inaccessible to solvent due to protein-protein contacts. It is known that the PPI interface area is large; however, only certain hydrophobic spots contribute to the free energy of binding and help to hold the two proteins together. Such regions on PPI interfaces that contribute more to the binding energy are called hot spots. Hot spots account for less than 50% of the contact area of PPI. A region of protein surface is called a hot spot when replacement of an amino acid residue by alanine in that spot lowers the free energy of binding by at least 2 kcal/mol . The hotDocking methods are used in several stages during the design of PPI inhibitors . Even beet al. [Experimental procedures such as yeast two-hybrid method, co-immunoprecipitation, pull-down assay, and protein chip- or mass spectrometry-based assays can be used to identify the protein partner interactions. Several PPI databases are available in the literature . Advanceet al.  have dishttp://www.vls3d.com/index.php/links/bioinformatics/protein-protein-interaction/protein-protein-docking.Before extensive docking studies, hot-spot identification studies, and design of PPI can begin, experimental methods must be used to show that the two proteins of interest interact with one another or to identify the partner of a known protein of interest. If the structure of the complex of protein partners is available along with mutational data, hot spots are readily available for docking of designed drug-like molecules to one of the proteins. However, if the structure of the complex is not available but the structures of individual proteins are available along with molecular biology data on mutation of residues and binding, protein complexes may be built to identify the binding interface. In the first step when the structures of monomers of interacting partners are available, docking methods are used to determine the complex structure of the protein. There are two types of docking used for protein-protein docking: (1) Template-based docking, where the structures of individual proteins are manually docked using the template structure of a dimer in the same homologous family. This method is fast and simple in the sense that no automatic docking and scoring algorithms are used. The generated structure of the complex based on a template is minimized to obtain the docked conformation; (2) Template-free docking, in which the structures of monomers are docked using docking algorithms with or without the support of experimental data. Several protein-protein docking methods are available. Some available databases , dockinget al. [i.e., van der Waals, electrostatics, and desolvation, was used to rank the structures.We discuss few methods of protein-protein docking here. ZDOCK is a software developed by Chen et al.  in whichet al. ,137. As et al. ,138. Theet al.  relies oet al. [Protein-protein docking methods developed use a similar general approach in which one protein is fixed in space and the second is rotated and translated around the first one. For each new configuration, the energy of interaction is calculated based on terms such as surface complementarities, electrostatic interactions, van der Waals interaction, and additional terms depending on the method developed. The calculated configurations are given a score based on energy functions used. One of the drawbacks of these methods is that it is almost impossible to search every possible rotation and translation for two interacting objects, and the search through the entire conformational space of the complex geometry makes the calculation very time-consuming, seldom resulting in a unique solution. A docking method that incorporates experimental data to dock the two protein structures was developed by Domingues et al. ,141. In et al. . Based oet al. [A new computational method that uses an assembly of structures of molecules that can fit into an electron density map data generated by cryo-electron microscopy (cryo-EM) electron density maps was described by de Vries et al. . The metet al. ,143,144.Since different docking programs developed by different researchers around the world use different criteria for scoring based on the need and the problem encountered, a general assessment method for results of docking was established to compare the quality of docked protein complex structures. Performances of docking algorithms are compared biannually in the Critical Assessment of Predicted Interaction competition (CAPRI)  and are et al. uses the data obtained from structure-activity relationships (SAR) by NMR and multiple solvent crystal structures (MSCS) [One of the most important steps in PPI design is identification of hot spots using computational or experimental methods. Alanine scanning is a widely used experimental method for hot-spot identification. Apart from this, NMR and X-ray crystallographic methods along with mutational data are used to define hot spots in 3D structures of protein complexes. These hot spots are suitable for binding to a variety of small molecules and, hence, can be used as “druggable” hot spots with high hit rates ,151. Thes (MSCS) . In thiset al. [Protein-protein interactions can also be viewed as protein-peptide interactions as a small fragment of one protein interacts with protein surface of another protein. The peptide part that binds could have a particular secondary structure or a linear chain. Examples of such protein-peptide interactions include SH3 domain, WW domain , and PDZ domain ,164,165.et al.  have devIn vitro methods such as proximity ligation assay [Based on hot spots and the structure of the protein complex, compounds that bind to one of the protein partners and inhibit PPI are designed. Structure-based design or database screening is used to arrive at a hit compound and a template structure is designed. Once the basic template structure of the compound is designed and synthesized, one has to show experimental evidence that the designed molecule is indeed a PPI inhibitor of particular partner proteins. Results obtained from docking studies should be validated using experimental methods. Several methods are available to study PPI and their inhibition. Since the intent of this article is to provide an overview of docking methods in the design of PPI inhibitors or modulators, we have briefly highlighted some biophysical methods that are used in concert with docking methods to validate the hit compounds. Surface plasmon resonance (SPR) , and NMRon assay  and enzyon assay  provide on assay , and mason assay ,175,176.etc. However, the design in these examples includes fragment-based discovery [Once the hot spots are identified on the protein surface, molecules that are designed to inhibit PPI can be docked to the protein with a grid box covering the area of several hot spots, and the binding ability of the designed compound to one of the proteins can be evaluated. At this stage, any of the docking methods that evaluate small molecule or peptide docking to proteins can be used. The most widely used methods are AutoDock , DOCK 1, FlexDociscovery  and expeiscovery ,193 to siscovery ,194,195.iscovery , a TNF-liscovery , toll-liiscovery , calmoduiscovery , and Ephiscovery . Here, wplasmodium falsiparum. The PPI inhibition strategy was used to discover new antimalarian agents. Invasion of red blood cells by plasmodium parasite involves the interaction of two proteins, apical membrane antigen 1 (AMA1) on the surface of the parasite and Rhoptry neck protein 2 (RON2) that is discharged from the parasite and imbedded in the membrane of the host cell [et al. [2. Using mutagenesis studies, hot spots on the interacting proteins have been elucidated. On AMA1, Phe183 and Tyr234 had an effect on binding upon alanine mutation  NMR studies indicated binding of these small molecules to GPIbα.Crystal structures  were use2. However, mutagenesis studies indicated that mutation of the residues involved in salt bridges and hydrogen bonds did not affect the binding of two proteins significantly. Mutation at a hydrophobic Tyr86 reduced the binding affinity of CD2-CD58 by 1000 fold, suggesting that the hot spot is a hydrophobic region. Our idea is to design peptides to mimic the CD58 binding region of CD2 protein that blocks CD2-CD58 interactions. We have designed several peptides and peptidomimetics that bind to CD58 and block the CD2-CD58 interaction [In vitro and in vivo studies suggested that the designed peptides suppress the adhesion between T cells and antigen-presenting cells by inhibiting CD2-CD58/CD48 interaction and are able to suppress immune response in an autoimmune disease model of rheumatoid arthritis.As mentioned earlier, most of the PPI inhibitors are based on the structure of one of the protein partners and, hence, peptides designed from the interface are natural PPI inhibitors. In our lab, we use peptides and peptidomimetics as PPI inhibitors. The T-cell adhesion molecules or co-stimulatory molecules CD2-CD58 have an important role in the immune response in strengthening the adhesion of T cells to antigen-presenting cells (APC). The protein-protein interaction between CD2 on T cells and CD58 on APC is important in the early stages of immune response ,213,214.eraction ,220,221.The literature suggests that PPI hot spots have some common features; however, PPI surfaces in different proteins are different, which makes the interactions highly specific. When homologous protein partners are involved, how do we design PPI inhibitors that bind specifically to one protein and inhibit PPI interaction? We have studied such cases in our laboratory using experimental and computational approaches.The human epidermal growth factor receptor (EGFR/HER) system of receptor tyrosine kinases plays an important role in cell growth and differentiation . Among tet al. [et al., the results are normalized in both ligand and target dimensions. This method was applied to dock 287 FDA-approved small drugs with 35 small peptide-binding proteins, which include 15 true positives. AutoDock 4.0 utility was used as a docking function. Different known targets and their ligands such as ML-IAP/XIAP, Factor VIIa, Renin, Bcl-2, Thrombin, and MDM2 were used for virtual normalization. The results suggested that the method provides true positives . Such normalized methods are very useful for creating a diverse set of compound data bases that can be used to screen PPI inhibitors for any given PPI. Such docking methods need high-performance computers with parallel processors and computational methods applicable for parallel processing [Most of the PPI studies and docking methods describe a particular set of PPI and finding inhibitors for that particular PPI using docking methods. Casey et al.  describeocessing .et al. [in vitro filters was used for the drug design. From the NCI diversity library (https://dtp.cancer.gov/branches/dscb/div2_explanation.html) of drug-likeness criteria, 1420 molecules were retained on which high-throughput docking was performed. FlexX software was used for virtual high-throughput screening, and calculated scores were reevaluated by GFscore. The first 335 lowest energy compounds were selected from docking. This selection was further narrowed by applying a filter based on RT loop binding region of SH3 domain  binding surface through which interactions are formed . Betzi eet al.  made a s3 domain . The finnhibitor .et al. [in vitro, but was not efficient for medical use. Using a docking approach, a chimera peptide was designed based on eight existing human BRC protein motifs. To design the peptide, models of all eight BRC motifs were complexed with HsRad51 on the basis of HsRad51-BRC4 motif crystal structure. The complex modeled was analyzed for interaction energy of each amino acid residue of the BRC motifs and the best amino acid residue in the binding position with HsRad51 was selected. The designed chimera peptides were evaluated for their PPI inhibition potential. One of the chimeric peptides designed exhibited 10 times more potency than a similar peptide described in previous studies. The study demonstrates that PPI inhibitors can be designed when crystal structures of homologous proteins are known [Using a docking approach, Nomme et al.  designedre known .Protein-protein interaction can also be stabilized by drug-like molecules. These PPI stabilizers can bind to one partner protein and stabilize the interaction of the two proteins  or bind to the interface of the complex, stabilizing the dimers. Such examples are available in the literature, and the molecules that bind to stabilize the complexes are already on the market. One of the best examples of protein stabilizers is paclitaxel, which binds to microtubules and stabilizes the protein dimers ,237. Heret al. [Myc is similar to a myelocytomatosis viral oncogene that codes for a transcriptional factor oncoprotein Myc. Myc shows constitutive overexpression in many types of cancer and, hence, is a target for cancer therapy. Myc protein belongs to the helix-loop-helix leucine zipper protein family that activates or represses transcription as a heterodimer. The heterodimer partner of Myc is myc-associated factor X (Max). Max also belongs to the helix-loop-helix leucine zipper protein family, and can form homo- and heterodimers. Both Myc and Max are needed in heterodimerization form to bind DNA since Max lacks a transactivation domain and Myc contains a transactivation domain but cannot homodimerize. Myc can heterodimerize with Max to form heterodimers that can both bind DNA and transactivate. The transcriptionally active Max/Myc dimer promotes cell proliferation as well as apoptosis. Overall, Myc-Max heterodimers promote cancer and Max homodimers suppress cancer . This PPet al. , have uset al.  and, henPPI inhibition for designing drug-like molecules is an area that has been explored extensively during the last decade. Considering the number of databases available for PPI inhibitors and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in various stages of screening and designing PPI inhibitors as well as stabilizers. Starting from protein-protein docking, binding site analysis, and screening compounds for drug-like compounds, docking methods are used for identification of hot spots and design of PPI inhibitors. There are three major challenges in the use of docking on relatively flat surfaces of PPI. The first problem is the flexibility of proteins in docking and scoring. This is particularly important when the protein-protein interactions are transient. Furthermore, if there are conformational changes in the binding site in the bound or unbound state of one or both of the proteins, docking methods have to incorporate a method to deal with this. The analysis of ensembles of conformations and prediction of consensus hot spots is a good option for proteins that have a flexible surface. The second major problem is taking into account the interaction of small molecules or peptides with water molecules in the bound state. Since the PPI surface is flat, even if the hot spots are hydrophobic, only one part of a small molecule or peptide is buried in the protein shallow groove or cleft. The part of the ligand molecule that binds to the surface of the protein may not have any contact with water molecules. In most of the PPI hot spots, water molecules often form a ring around the center of the hot spot, and the hot spots are dry. Hence, the contribution of water molecules to the energetics of binding is small . The othA promising area in PPI inhibitors is fragment-based drug design, in which fragments are developed on the PPI and linked to create new PPI inhibitors. Docking will play a significant role in this method also. There are attempts to create focused libraries for specific target types. Such PPI inhibitor libraries can be used to dock compounds using virtual screening. Rather than screening the entire data set , employing only a limited data set of compounds that are useful for target protein surfaces is more efficient . A data The design of PPI inhibitors is gaining confidence as more molecules that have drug-like properties are generated. Without defining the proper surface for PPI, the design of molecules that inhibit PPI will be vague. Docking methods are used in various stages of PPI inhibitor design whether it is small molecule-based or peptide-peptidomimetic-based drug design. Improved docking methods are necessary as a starting point for PPI inhibitor design. A combination of experimental and computational techniques with scoring functions appropriate for particular cases is necessary for designing PPI inhibitors. Docking algorithms used for PPI are in the early stages; however, as more data are available, it will become a highly promising area in the design of PPI inhibitors or stabilizers.


To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at  When investigating these interactions, it is common practice to isolate the binding motif of the disordered region and analyze the binding as a protein-peptide interaction5. Protein interactions involving disordered regions, as well as other protein-peptide interactions, are frequently associated with various human diseases such as cancer, cardiovascular disease, amyloidosis, and neurodegenerative diseases7. It has also been shown that protein-peptide interactions can be regulated using small molecules10, making them prime candidates as drug targets. Thus, understanding the structural details of protein-peptide interactions is fundamental for our understanding of complex cellular processes and complex diseases, and in addition they can provide a basis for rational drug design to modify protein-peptide interactions.Protein-protein interactions are a fundamental part of all major biological processes. A particularly interesting class of protein-protein interactions are those involving interaction including intrinsically disordered regions. These regions are often the size of small peptide fragments 5 to 25 residues long11. High-throughput methods that measure binary interaction, such as affinity-purification mass spectrometry, yeast-two-hybrid, and BioID14 are lacking in molecular detail. Thus, many computational methods have been developed to predict protein complex interactions15. There are both methods that predict binary interactions from the amino acid sequence16 or by combining other sources of information17, as well as methods that predict the complete 3D-structure of a complex using sequence- and profile-based alignments19 or structural alignments21 to libraries of interacting proteins of known structure.Experimental methods for determining protein-protein interactions in molecular detail, such as x-ray crystallography, cryo-EM, and NMR, are both difficult and time-consuming22. These methods search through the Protein Data Bank23 for interacting complexes structurally similar to both query proteins, and work well when both partner proteins are well-ordered protein structures. However, for modeling protein-peptide interactions these methods cannot be used, since the interaction in this case is between an ordered partner and a smaller, relatively flexible, peptide fragment. Instead, methods that try to dock the peptide onto the ordered partner have been applied to this problem with some success for very short peptides (≤4 residues)24. The success for larger peptides is limited due to the large number of peptide conformations and binding poses that needs to be sampled27.Methods that utilize structural alignments have proved to be especially successful in the latest CASP12-CAPRI25 or the CABS force field in CABS-dock27. Another way to tackle the sampling problem is to first predict the peptide binding site on the protein surface32. If successful, docking methods can focus the attention to the predicted binding site instead of wasting time on sampling incorrect binding sites. In addition, experiments can be directed towards the predicted binding site and the functional effects of the interaction can be studied. PepSite29 is a method that predicts the peptide binding sites by describing he preferred binding environment for peptide residues in protein-peptide interactions with known structure using a spatial position-specific scoring matrix (S-PSSM) that can be used to scan the protein surface for candidate binding sites for peptide residues. More recent methods, PeptiMap30 predicts peptide binding by adapting the fragment mapping (FTmap) for ligand peptide binding site prediction33 to peptide binding characteristics, PEP-SiteFinder31 combines peptide 3D conformation generation and fast rigid body docking, and ACCLUSTER32 clusters surface residues that have good chemical interactions with amino acid probes.The sampling problem can remedied to some degree by coarse-graining using side-chain centroids like in Rosetta FlexPepDock34 performs template-based docking using templates from a database of experimentally determined peptide-protein structures and builds models using molecular docking, while PIPER-FlexPepDock35 combines coarse-grained mass-sampling of the protein receptor surface using PIPER36 with molecular docking of the peptide using Rosetta FlexPepDock25, and HPEPDOCK38 uses a modified protocol from MDock40 to dock a wide range of peptide conformations by shape complementarity before energy minimization.Other methods combine both the peptide binding site prediction with peptide-protein docking into one pipeline. GalaxyPepDock30, PEP-SiteFinder31, HPEPDOCK37, and ACCLUSTER32 are unfortunately available only as web servers with limited programmatical access. Similarly, PIPER-FlexPepDock35 is only available either through a web-server or an expensive license. In fact, to the best of our knowledge the only software that is currently available as stand alone download is GalaxyPepDock34, and even this method is distributed with precompiled executables, meaning it only runs on a select few system architectures. The lack of method availability clearly hinders progress in the field and makes it difficult to benchmark against these methods.PeptiMap41. Recent studies have suggest that the protein-protein interaction space in current PDB is close to complete43, indicating that it could potentially be possible to model the majority of protein-peptide interactions using structural templates.In this study, we present InterPep, a novel protein-peptide interaction site prediction pipeline that predicts which residues on the surface of a target protein are most likely to interact with a given peptide fragment. It is based on the hypothesis that, if a protein-peptide interaction exists within the PDB describing a similar interaction surface, that interaction surface can be used as a template to model the interaction. Apart from the use of machine learning another difference to previous studies is that we expand the possible templates for interaction from protein-peptide to any protein-protein interaction. This is a reasonable assumption since protein-peptide interactions adopt the same structural motifs as monomeric protein folds upon binding25.The complete InterPep pipeline runs mass structural alignments to find interaction templates, ranks these templates with a random forest classifier using both structural and sequence information, and outputs both a predicted per residue probability and an amino acid preference profile for the interaction. Both these types of information could potentially be used as constraints in local protein-peptide docking, such as by Rosetta FlexPepDockThe performance of InterPep demonstrates the strength of a structural template-based approach to investigate protein-peptide interactions. Even though InterPep certainly can be used to model ordered interactions, we believe that best use-cases will be modeling of interactions with disordered regions, i.e an ordered protein interacting with a disordered partner. These interactions are immensely important but yet extremely difficult to study. The approach can easily be modified and extended to include other types of ligand binding, such as binding of phosphorylated targets and saccharides. InterPep is developed using only freely available software, and the source code is available for download, see link above.23 . The dataset consisted of structures containing interactions between one larger protein chain and a smaller poly-peptide chain of 5 to 25 residues acquired through NMR or X-ray crystallography with a resolution of at least 3 Å. To make sure all protein-peptide interactions within the dataset were biologically significant and not simply crystal contacts or close in space because of other binding partners rather than direct interaction, the shared surface between the peptide and the protein had to be at least 400 Å2, which approximately corresponds to a peptide of 5 to 10 residues along the protein surface (semi-exposed). Examples include 1BC544  and 1C9I45 . The peptide length and shared surface area requirements were based on the molecular recognition feature (MoRF) definitions from Mohan et al.1. Peptides of these lengths have been observed to be disordered when unbound, representing intrinsically disordered and difficult-to-predict peptides or regions. The size of the shared surface was calculated with NACCESS46. Furthermore, to focus on surface interactions, rather than on cases where the peptide is a requirement for proper folding of the receptor protein, any case where the peptide was buried by more than 60% were filtered. To ensure common proteins were not overrepresented within the dataset, the longer of two protein chains were clustered at 30% sequence identity, using BLASTCLUST47. The final dataset consisted of 502 protein-peptide interactions, and is freely available at http://wallnerlab.org/InterPep.To develop and evaluate InterPep, a dataset of known protein-peptide interactions was constructed using Protein Data BankSeveral of the protein-peptide complexes in the dataset are taken from multi-chain structures containing addititional protein chains. This was a deliberate choice, since when investigating protein-peptide interactions it is often not known if the receptor protein oligomerize or interacts with additional proteins. As such, it is important for a protein-peptide interaction site predictor not to falsely predict protein-protein interaction sites. When testing InterPep and other methods, no knowledge of any protein-protein interaction sites was utilized in order to test their capability to predict the correct site, in the presence of potentially disturbing noise from other protein-protein interaction sites.An interaction template library was constructed using all pairs of interacting chains from the PDB , defined as two chains with any heavy atom within 6 Å. Templates where the receptor was less than 25 residues large were removed, as was templates from files with excessive number of chains, such as complete virus capsid structures. For NMR structures, only the first model was included. The interaction template library consisted of 402,102 template pairs of interacting protein chains.48 with E-value cutoff at 10−2 and maximum pairwise identity of 90%, to search against the uniprot20_2016_02 database to build the Hidden Markov Model profiles, which were then used to search against the PDB23 database clustered at 70% sequence identity. To ensure having models of varying quality, up to eight alignments per target were chosen and modeled by MODELLER49. In total, 1,617 models for all targets were constructed in this way.To investigate the impact of model quality of the target protein and to determine whether InterPep is robust enough to work on modeled structures rather than on native structures, a set of structural models derived from the protein sequences of the protein-peptide interaction dataset set was constructed. These models were produced using HHblitsThe complete InterPep pipeline is outlined in Fig. 50 to every protein chain in each pair from the Interaction Template Library described above. Any alignment with a TM-score ≥0.5, which is the cutoff value for a significant structural match51, is used as a potential template for the target protein-peptide interaction. The alignment of the residues from the template involved in protein-protein interaction to the equivalent residues in the target protein, gives a template for a potential interaction surface in the target.In the first step, the target protein is structurally aligned using TM-AlignIn the second step, features relevant to protein-peptide interactions are calculated for each template. The features used by InterPep are summarized in Table 52 with 100 trees and no depth limit. To avoid overfitting, a maximum of In the third step, the extracted features are used in a random forest classifier to separate correct and incorrect templates. Templates were considered correct if at least 80% of the interaction site residues suggested by the template where correct. Other cutoffs where also tried, including training regressional random forest to predict the fraction of correct residues, but classification was superior (see Supplementary Information). The random forest is implemented using scikit-learn53, and the average method for calculating distances to clusters. The distance between two potential interaction surfaces is calculated as the mean of the distances between the coordinates for each residue in the different surfaces. The distance cutoff is determined automatically through the elbow method54. The highest predicted probability among the templates in a cluster defines the score of that cluster (cluster score).In the fourth and final step, the potential interaction surfaces are clustered using hierarchical clustering utilizing the SciPy packagelocal score measures the likelihood of each residue to be involved in a protein-peptide interaction. Hierarchical clustering and the 10% cutoff were utilized in favor of calculating a weighted average over all structures to ensure that strong and correct signals were not hidden by many weak ones, and to make the final prediction more robust to random outliers and fragmented templates by basing it on an ensemble of predictions.The top 10% of all templates within the highest scoring cluster are then combined through a weighted average to produce the individual local residue scores that make up the final prediction. The weights are the probabilities predicted by the random forest for the templates, and global score is designed to measure the confidence in the prediction, and is defined by combining the cluster score with the highest local score using a weighted mean, selecting the weights 0.61 for cluster score and 0.39 for local score maximized the correlation to 0.63 for the quality of prediction as measured by Matthew’s Correlation Coefficient (MCC) between correct and incorrect template residues, Fig. global score has the advantage that it combines the confidence in the predicted area measured by the cluster score with the confidence of the predictions in that area measured by the local score, and should thus represent the confidence in the prediction as a whole. As cluster score indirectly describes the maximum quality of the templates available, it is natural that it also has the highest weight. The distribution of precisions (fraction of correct residues) for different score thresholds shows that the global score is a good estimate of performance, see Fig. global score above 0.4 the majority of the predictions have precisions above 60% increasing to 80%, with very few predictions with precision less than 50% for global score above 0.7.For every prediction method, it is important to have a score that correlates with the confidence in the predictions. In InterPep, the Throughout the paper, a binding site counts as successfully identified if at least 50% of the residues of the predicted interaction-site overlaps the true interaction-site, i.e. if the prediction has a PPV of at least 0.5. This metric will reward methods for pin-pointing the center of interaction, rather than making sweeping attempts at predicting as large sites as possible.The overall method has been described in Methods. Below we describe, benchmark, and validate some of the design choices that were made during the development of InterPep.z, βz is a constant, i is the total number of templates, and β are assigned values so that the total error local score from InterPep.InterPep relies on finding interaction templates that can be used to model a particular protein-peptide interaction. As such, the set of interaction templates, or the interaction template library, available to InterPep sets the boundaries for the prediction. Relevant questions in this regard are how many protein-peptide interactions can be modelled by such templates, and if protein-protein interaction templates can be used to model protein-peptide interactions? To answer these questions, the full interaction template library (see Methods) was divided into a peptide-binding and a non-peptide-binding part, and the possibility of using perfect combinations of templates from the full, peptide and non-peptide parts to model protein-peptide interaction was assessed. Perfect combinations of templates for the different template libraries were constructed by fitting all templates available to the correct answer using linear regression, as shown in the equation below:A binding site was considered correctly identified if it had at least 50% overlap with native interaction site. All templates with BLAST E < 0.001 to the targets had been removed from the Interaction Template Library prior to this analysis. As can be seen from Table 29, PeptiMap30, and GalaxyPepDock34, of these the latter is available as stand alone download, while the other two allows programmatical web access. In addition, two reference methods were also included, one method using surface conservation (Surf-Cons) calculated using Rate4Site55 for surface residues, and one method similar to InterPep, but with the random forest classifier score replaced with the TM-score from TM-align (InterPep-TMonly).The performance of InterPep was benchmarked against other established methods. There are several published methods that potentially could be included in a benchmark. However, as most of them are only available as web servers with limited or even blocked programmatical access, we only included the following methods that could actually run on the full benchmark set: PepSite230, none of these scores are available to the user and thus PeptiMap could only be represented by a single point. PepSite2 only allows peptides 10 residues long or less, so as to make sure PepSite2 was not disadvantaged for longer peptides, it was run with a sliding window of size 10 for the whole peptide and the best prediction was chosen for each target. Also, Surf-Cons can sometimes predict several different binding sites on the same target, in these cases only the best was included.Since some methods give very few predictions and some very similar predictions for the different targets we restricted the analysis to the first ranked prediction for each target. For each method, the top-1 prediction for each targets of the dataset was analysed in Precision-Recall curve, see Fig. Overall, InterPep performed best of all methods considered, with a consistently larger precision over 5% recall compared to all other methods, see Fig. 56 that only contains protein-peptide interactions, thus it should be less likely to find protein-protein interaction sites compared to other template-based methods, e.g. InterPep and InterPep-TMonly that actually directly use protein-protein interactions as templates. However, the inclusion of protein-protein interaction-templates improved the maximum recall for InterPep and 60 of the 255 interfaces correctly identified by InterPep used a protein-protein interaction-site as template for the interaction.Table The fact that InterPep performs much better than InterPep-TMonly clearly indicates that the random forest classifier in InterPep is much better at ranking predictions compared to TMscore alone. Still, even tough InterPep-TMonly does a poor job in ranking predictions between targets, it achieves a decent maximum recall of 31.9% (160/502 correct predictions) comparable to PepSite2 with 35% (176/502 correct predictions). The reason why TM-score is not a good discriminator by itself is of course that a high TM-score, ergo a close structural match, is important, but is not alone sufficient, to classify a prediction as correct, and additional information about the peptide and sequences are needed to make the prediction more accurate. This also explains why InterPep-TMonly makes mistakes by finding more protein-protein interaction sites compared to InterPep (48 vs. 32), as without additional information about the template and peptide it is impossible to tell the difference between protein-protein and protein-peptide interaction templates using only TM-score, see Table Thus far, InterPep has only been trained and benchmarked on native structures. Of course, in a real case scenario it is more than likely the prediction needs to be performed using modeled structures, simply because the native structure does not exist for the protein target of interest. It is therefore important to benchmark and assess the performance also on modeled structures.Models of the native structures were constructed (see Methods) and used as input to the InterPep pipeline. The ability of InterPep to predict the correct binding sites of model structures was only slightly lower than for native structures, correctly identifying 49.8% protein-peptide interaction sites in models compared to 50.7% for native structures, see Table 57, see Fig. Finally, we investigated whether InterPepM performance on native and modelled structures was related to the quality of the models, e.g. whether InterPep performs well for the native structure, and whether poor performance could be attributed to poor model quality. We analyzed the difference in precision between the models and their corresponding native structure as a function of the quality of the modelled structures measured by S-scoreSince neither InterPep nor InterPepM is universally better, the InterPep pipeline is distributed with both versions, and depending on what input data is available the appropriate version can be selected.To get an idea of the degree to which the different features influence the prediction, the relative importance of each feature used by the random forest classifier in InterPep was estimated by Gini impurity . These relative feature importances are shown in Fig. Of the five most important features, two describe length and mean sequential distance of the peptide template, and the other three describe the quality of the structural alignment between the target and template, see Fig. 58, and is intrinsically linked to function59, it is no surprise that features regarding how well the target structurally matches a template should be ranked as important; 56% of TM-score branchings prefer higher TM-score, and 55% of RMSD branchings prefer low RMSDs.Additionally, since structure has been shown to be more conserved than sequenceIt should be noted that counting label distribution at branchings only allows a very rough and generalized estimate of how a forest estimates a feature, as the total prediction trees are much more complex, e.g. in how the different branchings affect others further down the trees.Three examples of final predictions from InterPep are shown in Fig. Overall, InterPep has proven a powerful tool for protein-peptide interaction site prediction. We show that a majority (81.3%) of protein-peptide interactions in the test set have structural templates without significant sequence similarity, and that a large proportion (67.7%) of the protein-peptide interactions actually have templates from protein-protein interactions. The InterPep pipeline, designed to take advantage of this, successfully identified half (50.7%) of the binding sites at top-1 and a majority (69.3%) among the top-5 using only structures with low sequence similarity as templates. In addition, the confidence score from InterPep correlates fairly well (0.63) with the precision of the prediction as measured by MCC for the predictions. By training InterPep also on modelled structures the performance is maintained for cases when the native structure is missing. In fact, the quality of the model did not turn out to be a determining factor for performance. InterPep is available as open source, and should be a useful tool in protein-peptide interaction analysis, providing possible guidelines for initial experiments or analysis of results. It should also be possible to use the results from InterPep as restraints to build 3D models of protein-peptide interaction.Supplementary Info


Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at  Most biological processes, including cell proliferation , signallAdvances in next-generation sequencing techniques have created an explosive increase in the number of genetic variants available in the literature. However, experimental techniques to study these variants are still expensive and time consuming. mCSM  was one Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability ,26, dynaHere we introduce mCSM-PPI2, a webserver that integrates our well-established mCSM graph-based based signatures framework with evolutionary information, inter-residue non-covalent interaction networks analysis and energetic terms, in order to provide an optimized overall prediction performance.The data used on this work was derived from the recently updated version of the SKEMPI database , which cG):The binding affinity of protein–protein complexes were used to calculate the binding Gibbs free energy (ΔGWT→MT) should be equivalent to the negative change binding free energy of the hypothetical reverse mutation, from the mutant to the wild-type protein (–ΔΔGMT→WT) (Gwt-mt ≥ 0) and 3268 decreased (ΔΔGwt-mt < 0) binding affinity, and in order to build a more robust and balanced predictive method, we also included the hypothetical reverse mutations. Therefore, the final dataset for building mCSM-PPI2 predictive model includes 8338 single-point mutations (S8338), which represents an increase of up to three-fold in datapoints in comparison with previous methods that used data from the first version of SKEMPI with 2007 (S2007) (Since the Gibbs free energy formulation is a thermodynamic state function a change in binding affinity of a mutation from a wild-type protein to its mutant (ΔΔΔGMT→WT) . Given t (S2007) , 1964 (S (S2007) , 1102 (S (S2007)  and 1327 (S2007) .A subset of 487 mutations in 56 complexes (S487) contained within S4169 and not in S2007 were recently curated  and herehttp://biosig.unimelb.edu.au/mcsm_ppi2/datasets.The datasets used in this work are freely available for download at mCSM-PPI2 uses as one of its core components our well established graph-based structural signatures (mCSM) to represent the environment of the wild-type residue. This approach models both the geometry and physicochemical properties of the interactions and architecture of wild-type structure and has been widely applied to the study of small molecule and protein structure ,26–34,42Single-point mutations can affect protein–protein interactions via different molecular mechanisms, including changing folding free energy of interacting partners or disrupting non-covalent interactions essential for complex formation ,43. In mBased on 3D structures collected from the Protein Data Bank , we wereThe conformational flexibility of glycine side chains and the rigidity of proline side chains are important for defining the backbone flexibility. Mutations from and to these two amino acids can lead to large structural effects. For our model we included binary terms to capture if the mutation was from or to a glycine or proline.Binding regions are known to be evolutionarily conserved, which has been exploited in a variety of studies to identify potential protein interaction interfaces. For mCSM-PPI2 we also harnessed this information by using the Position Specific Scoring Matrix (PSSM) scores. PSSM was calculated through PSI-BLAST of BLAST 2.2.3 using the non-redundant Swiss-Prot database of protein sequences and the sequence of the chain in which the mutation occurs as the query parameter.We performed analysis of the non-covalent interactions for the wild-type residue and for the closest interface using the contacts calculated by Arpeggio . Here, wInteraction energy information between the two interacting chains were extracted from FoldX . In addiWe used the Bio3D R package  to calcuhttp://biosig.unimelb.edu.au/mcsm_ppi2/). The server front end was built using Materialize framework version 1.0.0, while the back-end was built in Python via the Flask framework (Version 1.0.2). It is hosted on a Linux server running Apache.We have implemented mCSM-PPI2 as a user-friendly and freely available webserver  or saturation mutagenesis  .In order to assist users to submit their jobs for predictions, sample submission entries are available in both submission pages and a help page is also available via the top navigation bar.For the Single Mutation option , mCSM-PPFor the Alanine Scanning option on the interface analysis, the server first presents a table with all the interfaces identified on the submitted structure, and it also allows for inspection of the individual interfaces. On the results page of each interface the server shows a downloadable table with the prediction outcomes for each mutation, a bar chart that summarizes the predicted changes in binding affinity  and an iIn order to build a more robust and reliable predictive model we performed four types of validation. Firstly, we performed 10-times stratified 10-fold cross-validation, using 90% of our original dataset (S8338) for training and the remaining as a blind test. Selection of the blind test was repeated 10 times in a stratified manner, with the model retrained on the remaining data, in order to test the robustness of the model . For this approach the hypothetical reverse mutations were kept in either training or test sets during the splits according to its counterpart original mutation. Our method was able to achieve an average Pearson correlation (ρ) of 0.82 with a standard deviation (σ) of 0.06 across the 10 runs Figure  showing We further evaluated the performance of our approach on two low-redundancy sets; low redundant at the (i) complex and (ii) interface level. The complex low redundancy test was performed using leave-one-complex out cross-validation, in which we trained our model on 318 complexes of our dataset and evaluate the performance on the one remaining complex. After repeating this procedure for each complex we achieved ρ = 0.75 Figure  and Rootr-to-z transformation) than the results reported for MutaBind when trained using only mutations from SKEMPI1 .Similarly, we applied leave-one binding site out using the ‘hold-out’ information extracted from SKEMPI2. Here, we removed all mutations located in identical binding sites for testing and trained on the remaining data. mCSM-PPI2 achieved ρ = 0.67  Figure , which wIn addition, we evaluated the performance of our approach on a subset of 472 mutations (S472) not present within the first version of SKEMPI but included in SKEMPI2. For this experiment, we trained a predictive model using all variants from the first version of SKEMPI (S1964). Our method achieved a correlation of 0.63 .de novo influenza inhibitor targets (T55 and T56: 1007 mutations at 53 different positions in T55 and 855 mutations at 45 different positions in T56). The in vitro experimental measurements used the enrichment values generated from deep sequencing and were calculated based on the binary logarithm of the ratio of number of times the variant sequence was observed after and before the selection for binding. Although the 3D structures for these two complexes were not available, structures of close homologues have been described  . Our metYCR . OurFinally, we looked at the ability of mCSM-PPI2 to accurately identify PPI hotspots, residues that contribute to the majority of the binding free energy of the interaction and have been recognized as important sites for drug development . Here wehttp://biosig.unimelb.edu.au/mcsm_ppi2/.Here, we introduce mCSM-PPI2, a web server that implements an integrated computation approach for predicting effects of missense mutations in protein–protein affinity. By consolidating our graph-based signatures framework with evolutionary information, inter-atomic contacts and energy terms our updated method has shown to perform better than its previous version and other methods. In addition, the use of hypothetical reverse mutations has shown to improve the robustness of our predictive model allowing for a more balanced prediction. mCSM-PPI2 is freely available as a user-friendly and easy to use web server at gkz383_Supplemental_FilesClick here for additional data file.