In addition, we demonstrate the usefulness of the inferred functional landscape to investigate the cellular role of both well-characterized and novel RBPs in the context of human diseases.The coordination of the synthesis of functionally-related proteins can be achieved at the post-transcriptional level by the action of common regulatory molecules, such as RNA–binding proteins (RBPs). Despite advances in the genome-wide identification of RBPs and their binding transcripts, the protein–RNA interaction space is still largely unexplored, thus hindering a broader understanding of the extent of the post-transcriptional regulation of related coding RNAs. Here, we propose a computational approach that combines protein–mRNA interaction networks and statistical analyses to provide an inferred regulatory landscape for more than 800 human RBPs and identify the cellular processes that can be regulated at the post-transcriptional level. We show that 10% of the tested sets of functionally-related mRNAs can be post-transcriptionally regulated. Moreover, we propose a classification of  This indicates that the regulation of mRNA transcripts is key to achieve coordinated protein synthesis. Indeed, it has been shown that sets of transcripts coding for functionally related proteins are bound by common regulatory molecules, such as RNA-binding proteins (RBPs) and/or non-coding RNAs, thus forming the so-called RNA regulons4.While transcription contributes to coordinated gene expression in time and space, several studies highlighted the discordance between levels of mRNAs and protein production6. In mammalian cells, several sets of related mRNAs are part of RNA regulons as well, e.g., histone mRNAs bound by the stem-loop binding protein (SLBP)7, transcripts involved in inflammation regulated by the RBPs ELAVL1, HNRNPL and TTP8, those implicated in DNA damage response and regulated by the RBPs BCLAF1, ELAVL1 and THRAP310 and mRNAs coding for cell cycle and proliferation factors bound by Dead end protein homolog 1 (DND1) and Pumilio 1 (PUM1) proteins9.Early protein-RNA interaction mapping studies in yeast demonstrated that many RBPs bind specific mRNAs coding for proteins involved in the same biological process  or that are cytotopically related 11. However, even though RNA regulon perturbations can lead to the onset of neurological diseases and cancers in human14, the control of these regulatory circuits exerted by RBPs is rather sketchy16, therefore calling for further scrutiny.As this regulatory phenomenon has been observed in different species, RNA regulons represent a conserved feature of the post-transcriptional regulation in eukaryotes18 allowed to identify thousands of protein–RNA interactions. However, these studies have focused on the binding ability of a reduced number of established RBPs in a few cell lines18, indicating that the protein–RNA interactions space is largely unexplored. Moreover, thanks to the recent development of RNA interactome capture technologies, the catalogue of RBPs has dramatically increased (e.g.24). Importantly, many of these RBPs lack a known RNA-binding domain and their role in RNA biology has not been characterized yet22. In this context, large-scale computational prediction of protein-RNA interactions can provide a better coverage of the protein-RNA interaction space and improve our understanding of post-transcriptional regulation.A deeper understanding of post-transcriptional regulation is subordinate to the availability of experimentally verified protein-mRNA interaction data. Over the last years, studies based on high-throughput methods to detect RNA molecules bound by RBPs, such as RNA immunoprecipitation and CLIP-based techniquesWhat is the extent of the regulon theory at the coding transcriptome scale? What are the cellular functions regulated at the post-transcriptional level? Can RBPs be classified based on the regulation they exert? To answer these questions, we inferred the functional landscape of the post-transcriptional regulation mediated by the human RBPs, by assessing the RNA regulon theory at different levels of organization of the cellular processes, such as biological pathways and protein complexes. For this, we developed and applied an original large-scale approach to identify cellular processes post-transcriptionally regulated by RBPs, using both experimentally identified and predicted human protein–RNA interactions combined with protein-protein interaction network data and statistical analyses. We showed that the post-transcriptional regulation of functionally-related mRNAs by RBPs concern 10% of the groups that we tested in the regulatory landscape. Furthermore, we identified 3 groups of RBPs possibly regulating these groups of functionally-related mRNAs by using different molecular strategies.3, an RBP can regulate a given biological process by binding a substantial fraction of mRNAs encoding the proteins involved in that process. We therefore expect to detect a statistically significant over-representation of mRNAs bound by the RBP among groups of functionally-related coding transcripts. To determine the extent of the RNA regulon theory across all human biological processes, we gathered the transcripts encoding proteins involved in the same biological process or pathway, taken from four datasets representing different levels of organization of the cellular functions, and collectively named hereafter “functional units” (FUs): (i) 1846 manually curated protein macromolecular complexes from the CORUM database25; (ii) 874 functional modules detected in a human protein-protein interaction network using the OCG algorithm, which decomposes a network into overlapping modules based on modularity optimization26; (iii) 300 pathways described in the KEGG database27; and (iv) 1627 pathways from the Reactome knowledgebase28 . We computed the ratio of interacting vs. non-interacting transcripts for each functional unit with every RBP and assessed its significance to be higher or lower than expected by chance by performing a two-sided Fisher’s Exact test  tested in only two cell lines18. Since (i) we aim at investigating a comprehensive set of RBPs including the non-canonical ones, and (ii) the depletion phenomenon observed for both RBPs and FUs could be explained by a lack of coverage of the investigated network, we generalized our analysis to a large computationally predicted network of biophysically possible and biological context-independent interactions between 877 RBPs and 13,984 mRNAs (see below). In doing so, we expectedly circumvented the limitation of our scrutiny by missing interaction data. The results obtained on the experimental RBP–mRNA network . We generated more than 12 million protein-mRNA interaction predictions, of which 3.2 million show high interaction propensity score (catRAPID score ≥ 50) (see Methods) between the 877 RBPs and ~87% of the initial coding transcripts . With the standard catRAPID score cutoff36 , RBPs are predicted to interact with 3176 mRNAs on average (26% of the tested mRNAs)   , using the m29 Fig. . This toOverall, to the best of our knowledge, we have generated the largest predicted human RBP–mRNA interaction network to date.Next, we applied our approach Fig.  to inferThe first important outcome of our functional analysis is that, based on the detection of a significant functional enrichment, we could assign at least one potential target FU to 468 RBPs for which eCLIP interaction data is not available yet , indicating that these RBPs exclusively display a binding preference for a number of FUs. A second category accounting for 427 RBPs shows both significant enrichments and depletions of their predicted targets among functional units  suggesting that they bind the mRNAs of certain functional units and avoid those of others. Finally, the third category contains 211 RBPs that display only significant depletions  within functional units, illustrating that some functional units avoid RBP binding Fig. .i.e., the D-RBP set). This discrepancy between our predicted regulatory landscape and the results obtained on eCLIP data suggests a possible influence of the chosen catRAPID interaction propensity threshold .On the other hand, from the perspective of the FUs, we observe a mirrored situation. Most functional units  are exclusively enriched in targets of at least one RBP, thus possibly regulated at the post-transcriptional level through the binding of those RBPs. Few functional units, namely 27 , are both enriched and depleted in RBP predicted targets, indicating that they may be regulated by the binding of certain RBPs and the avoidance of others. Finally, 50 functional units  show only significant depletions thereby indicating that their mode of post-transcriptional regulation consists uniquely in the avoidance of RBPs binding Fig. . The com37 (see Methods). Importantly, we found the three distinct categories for both RBPs and functional units, with the M-RBP set being involved in a similar fraction of the significant functional enrichments and depletions  those functional units that can be regulated at the post-transcriptional level and that account for 10% of the tested FUs, and (ii) the RBPs responsible for such regulation. The results obtained on both predicted and experimental RBP–mRNA interactions suggest that both FUs and RBPs can adopt several possible regulation strategies and should be classified accordingly.Overall, this two-step statistical analysis allowed us to define the potential post-transcriptional regulatory landscape of numerous cellular processes by identifying The classification of RBPs in distinct groups based on the functional analysis of their interactors motivate us to assess whether the RBPs have distinct functional and sequence features as well as system-level properties  and depletions  compared to those of the E-RBP  and the D-RBP  sets respectively. This suggests that the more numerous RBP group in our classification (M-RBP set) can potentially regulate the larger number of FUs . Indeed, Beckmann and colleagues annotated RBPs into four classes: (i) established RBPs ; (ii) RBPs carrying a characterized RNA-binding domain (RBD); (iii) enigmRBPs, which are proteins found to bind RNA but lacking a canonical RBD and with no previous evidence of involvement in RNA fate; (iv) RNA-binding enzymes, which have an RNA-independent metabolic activity.Second, we checked whether RBPs of the three groups were characterized by an over-representation of different types of RBPs according to a previously proposed functional classification38. Interestingly, we observed that the RBPs binding predominantly mRNAs are more frequent in the E-RBP (82%) compared to the M-RBP (66%) and D-RBP (64%) sets. Indeed, in the latter two sets we observed a higher fraction of ribosomal proteins and RBPs binding small RNAs . The E-RBP set has a slightly higher disorder , which is otherwise enriched in enigmRBPs  Fig. . In the 38. In a compendium of 58 human tissues (see Methods), we did not observe any statistically significant difference among the three groups . Increasing evidence indicates that the activity of RBPs can also be regulated by PTMs43. We collected the modification site data for seven PTM types from the PhosphoSitePlus database44 (see Methods) and mapped them onto the RBP sequences of the three groups. We found that RBPs of the E-RBP set have a significantly lower PTM density , whereas the 50 D-FU units show significant depletions only among the interactors of 499 RBPs (average number of RBPs per unit: 21.5). The 27 functional units in the M-FU groups are enriched among the targets of 74 RBPs (average number of RBPs per unit: 3.7) and depleted among the interactors of 600 RBPs (average number of RBPs per unit: 45.8). These results underline the importance of RBP avoidance as a possible mode of regulation.45. Among the FUs related to chromatin organization and transcription activation, we found SWI/SNF-containing complexes and distinct forms of the Mediator complex from CORUM, as well as several network modules  and pathways  involved in energy production. Interestingly, these results corroborate the known post-transcriptional regulation of the mitochondrial components55.Additional enriched FUs are related to cellular processes localized in the mitochondria. Indeed, we find that several FUs have a large number of enrichments, including the large subunit of the mitochondrial ribosome from CORUM, four Reactome pathways related to mitochondrial translation as well as complexes  or epithelium 57. Our results indicate that these RBPs could potentially regulate the fate of a regulon made of the olfactory signaling mRNAs.M-FU units are involved in several signaling pathways. Indeed, we found that two pathways related to olfactory signaling (one from KEGG and the other from Reactome) are depleted in interactors of around two-third of the tested RBPs. However, they are exclusively enriched in those coded by the ERAL1, G3BP1, G3BP2, MKRN2 and TUFM genes, all expressed in brain tissues, according to Human Protein Atlas−16) and M-FUs , respectively  are related to viral and bacterial infections, whereas the other disease functional units are linked to immune-related, neurological and metabolic disorders  we found 20 disease-related pathways from the KEGG database. The majority of them . These units are enriched in interactors of the PRKC apoptosis WT1 regulator protein, an RBP encoded by the PAWR gene , which it has been implicated in mRNA splicing in cancer cells58. Furthermore, the pyrimidine metabolism pathway is also enriched among PAWR predicted interactions. Interestingly, it has been shown that intracellular pathogenic bacteria–such as Salmonella, Shigella and Bordetella –can modulate several host cell metabolism pathways for their own benefit, including nucleotide biosynthesis59, indicating a potential role of PAWR in the post-transcriptional regulation of genes involved in bacterial infection response.We also found common RBPs among FUs-related to bacterial infection as well  some FUs may be partially overlapping  or redundant, therefore leading to an overestimation; (ii) the choice of a strict catRAPID threshold for the prediction of protein–mRNA interactions, as well as the catRAPID restriction on transcript length, may have led to an under-estimation of the number of potentially regulated FUs. Moreover, as by construction, our statistical approach detects regulation events by considering a pairwise combination of FU and RBP, ignoring possible combinatorial and/or dynamic regulation modulations that could involve several RBPs16, the regulon prevalence could have been underestimated. Indeed, the analysis carried out on the eCLIP data provides a higher proportion of regulated FUs , thus suggesting that the underestimation is the most plausible scenario.By studying both types of data, we detected statistically significant over- and under-representations of the mRNAs bound by the RBPs among the functionally related coding transcripts. First of all, these results allow an estimation of the prevalence of the regulon theory. Among the 2977 functional units that we tested, comprising protein complexes, network modules and pathways, 10% have been found possibly regulated in the predicted functional landscapes. This result can be affected by two contrasting factors: Second, the different patterns of enrichments and depletions for the RBP binding to functional unit transcripts revealed by our analysis lead to a post-transcriptional landscape shaped by the RBP-mRNA interactions. It reveals that 57,2%  of the 877 tested RBPs regulate FUs by possibly binding to their mRNAs whereas 72%  do so by being avoided, therefore indicating the prevalence of this latter RBP regulatory mode. On the other hand, the groups of functionally related mRNAs  appear to be regulated through binding rather than through avoidance of the RBPs . Notwithstanding this, 90% of the FUs do not appear as being regulated by a particular RBP. Indeed, promiscuous RBPs interacting with most cellular mRNAs and FUs interacting with those RBPs are not expected to be detected as significantly enriched by our approach since the spread of the RBP targets precludes the detection of a statistically significant signal. This could be the case for 18% of the RBPs (164 RBPs) and 90% of the FUs (2677 FUs) for which no statistical signal has been detected.(i) the presence of RBP binding in the case of RBP targets enrichment, (ii) its avoidance in the case of depletions, or (iii) presence or avoidance of binding, when both enrichments and depletions are observed for a given RBP. Indeed, whereas some RBPs (the E-RBP set) appear to act exclusively through their binding to the mRNAs of the FUs , some others (the D-RBP set) are excluded from binding by having less targets than expected by chance among the mRNAs of the FUs . Finally, for other RBPs (the M-RBP set), both strategies, presence and avoidance of binding are observed.We observed 3 different patterns of enrichments and depletions for the RBP binding of functional unit transcripts. These patterns may reflect different possible FU molecular regulation strategies by the RBPs, involving catRAPID identifies RNA-protein interactions, the ‘presence’ is the physical ability for an RBP to regulate the FUs through its binding, independently of the binding status itself, bound or unbound, which may change with conditions. Conversely, the ‘avoidance’ is the physical inability for the RBP to bind, e.g., because of the lack of binding sites. As well as the ability, the inability to bind can lead to a regulation event.What does the ‘presence’ and the ‘avoidance’ of RBP binding represent? As 60 that coding sequences are evolutionarily constrained to avoid certain RBP binding motifs in order to prevent inappropriate interactions that could impair, for instance, their correct mRNA processing. Such avoidance of regulatory elements has also been observed for target sites of microRNAs within 3′UTRs61 and to limit spurious transcription binding sites62. Our striking observation that some functional units could contain the information to not interact with certain RBPs could therefore represent a cellular regulatory mechanism per se, calling for further investigation. However, as the repertoire of experimentally identified mRNA-binding proteins is constantly increasing63, we cannot exclude that some of the D-FUs can be regulated by a RBP not present in our dataset.Interestingly, the observed depletion, or avoidance of binding, could represent a molecular mechanism that limits inappropriate binding, which could interfere with correct gene expression. Indeed, it has been recently proposed by Savisaar and Hurst64 and several signaling and structural proteins. In addition, RBPs in this group have a significant low density in PTM sites, which can regulate, for instance, RNA binding or dictate the subcellular localization of a given RBP43. Altogether, this suggests that this set of RBPs contains putative multifunctional proteins whose RNA binding activity, which represents one of their possible molecular tasks, can be potentially modulated by a not yet identified molecular signal.We further studied the properties of the RBPs belonging to the three sets and found that several features can distinguish them. For instance, the E-RBP set is characterized by an enrichment in enigmRBPs that lack canonical RBDs and for which a role in RNA biology has not been established so far. Among the 29 enigmRBPs in the E-RBP set, there are 8 metabolic enzymes, including the moonlighting protein Leukotriene A-4 hydrolase (LTA4H)65, ELAVL167 and FMR1 proteins68. Moreover, RBPs in the M-RBP group, as well those in D-RBP, show a wider range of binding preferences among RNA biotypes compared to the E-RBP set, which comprises a high fraction of RBPs binding preferentially/exclusively mRNAs. Overall, our analysis indicates that RBPs in E-RBP group have distinct features that discriminate them from the two other groups. Consequently, further experimental studies are needed to identify the in vivo RNA interactors of RBPs in the E-RBP set (only 4 have been tested with the eCLIP technology) and, in the case of the enigmRBPs, decipher their role in mRNA fate.Conversely, the M-RBP set is enriched in RBPs with canonical RBDs showing a significantly higher PTM density compared to the E-RBP set, consistent with the current knowledge that the function of established RBPs is modulated by post-translational modifications, as in case of SR splicing factorsAltogether, our analyses defined a post-transcriptional regulatory landscape occupied by functionally related mRNA differently regulated by RBPs, thereby allowing us to provide a novel classification of the RBPs. This classification may help understanding the regulatory of action of the continuously increasing number of newly discovered RBPs.18 gathering 159 experiments for 112 RBPs. We mapped BED peak coordinates referencing the GRCh38 human assembly to Ensembl v82 coding transcript models using BEDTools intersect v2.1769 with flag –wa. Interactions from replicates and different cell lines were pooled. To have an interaction set comparable to catRAPID predictions, interactions involving transcript isoforms were mapped to the corresponding coding gene and counted as one. Doing so, we obtained a final list of 131,366 experimental interactions between 112 RBPs and at least one transcript encoded by 11,647 genes.We retrieved interaction information from the ENCODE enhanced CLIP (eCLIP) dataset25; 873 functional network modules, defined as groups of proteins densely connected through their interactions and involved in the same biological process, detected by the OCG algorithm26 on a human protein binary interactome built and annotated as previously described72 , which provides Ensembl identifiers for annotated genes. The genes/proteins annotated in the OCG network modules were mapped to the corresponding Ensembl v82 gene identifiers through the Ensembl BioMart service. We restricted subsequent analyses to complexes, modules and pathways having at least 5 and no more than 500 genes/proteins .We built a wide compendium of 4646 functional units and processes by gathering annotations from different sources: 1846 manually annotated human protein complexes from the CORUM databasefu,int is the number of FU-annotated mRNAs that interact with a given RBP, mRNAfu,no-int is the number of FU-annotated mRNAs that do not interact with a given RBP, mRNAno-fu,int is the number of mRNAs that interact with the given RBP but that are not present in the FU, and mRNAno-fu,no-int is the rest of mRNAs in the interaction space. We assessed the significance of the enrichment/depletion ratio by performing a two-sided Fisher’s Exact test. P-values were corrected for multiple testing using the Benjamini-Hochberg procedure and we considered as significant only those enrichments/depletions with a corrected P-value below 0.05. We used annotated mRNAs in the eCLIP interaction space as statistical background.To assess the enrichment/depletion of FU-annotated mRNAs interacting with RBPs in eCLIP dataset, we computed, for each functional unit, the log2-transformed - ratio of FU-annotated mRNAs among RBP interacting and non-interacting transcripts as:et al.22 and their corresponding amino acid sequences from the UniprotKB human reference proteome74 (May 2016). We downloaded the human coding transcriptome cDNA sequences  from Ensembl v8275 (September 2015).We collected a list of 1217 human RBP protein-coding genes identified by mRNA interactome capture from Beckmann 22 the following annotations: whether a role in RNA biology is known, presence or absence of a recognized RNA-binding domain according to the classification proposed in Castello et al.19, whether it has been categorized as ‘classic’ metabolic enzyme . Those RBPs lacking a recognized RNA-binding and with no established role in RNA biology are labelled as enigmRBP22.For each RBP in our dataset, we gathered from the original articlecatRAPID omics algorithm29, which allows large-scale predictions between transcript and protein sequences, to compute the interaction propensities between human RBPs and coding transcripts. Due to catRAPID computational constraints, we selected mRNA sequences between 50 and 1200 nucleotides of length, as well as protein sequences between 50 and 750 amino acids. Around 72% of the RBPs (877 proteins) and 57% of the human coding transcriptome  respected the length criterion. To avoid functional biases in subsequent analyses, we further reduced sequence redundancy among mRNAs  by selecting, for each protein-coding gene, the longest transcript as the representative sequence. Doing so, we retained 13,984 transcripts coded by ~63% of the annotated protein-coding genes in Ensembl v82 . We then predicted more than 12 million protein-RNA interactions between 877 RBPs and 13,984 mRNAs.We used the standalone version of 36, we considered as interacting all RBP-mRNA pairs with a catRAPID interaction propensity score of at least 50 and non-interacting all those with a score below 50. For each functional unit in the compendium, we computed the log2-transformed ratio of the FU-annotated mRNAs among RBP predicted interacting and non-interacting transcripts and assessed its significance as described above for the analysis on eCLIP interaction dataset. As RBPs are predicted to bind to many mRNAs, we further evaluated the number of enrichments/depletions expected by chance in each dataset by shuffling protein labels among functional units 1000 times. Only RBPs having a significantly higher number of enrichments/depletions than expected by chance  were kept.To assess the over- and under-representation of the functional units among RBP predicted interactions, as done previously37 (GSEA) using annotated mRNAs in a given functional unit as gene set. We selected as significant only those enrichments  or depletions  with a false discovery rate (FDR) < 0.05 based on 1000 gene set permutations. In both tests, we used annotated mRNAs in the catRAPID interaction space as statistical background.In a second approach, we carried out a Gene Set Enrichment Analysis76 (both long and short predictions) and DISOPRED377. An amino acid was considered disordered if its probability score was greater than 0.4. We calculated the RBP sequence low complexity using the NCBI segmasker application, which is based on the SEG algorithm78, using default parameters. For each RBP, we computed the fraction of the number of predicted disordered and low complexity amino-acid residues divided by the sequence length.We computed protein residue disorder propensity using the stand-alone version of two state-of-the art disorder prediction algorithms: IUPred44, which stores data for seven different PTMs: acetylation , methylation , O-GalnAc (2115 sites in 476 proteins), O-GlcnAc (420 sites in 166 proteins), phosphorylation , sumoylation (7932 sites in 2500 proteins) and ubiquitination . We extracted PTM data for the RBPs and computed their PTM densities as the number of PTM sites over the sequence length.We collected post-translational modification (PTM) information for 18,030 proteins from PhosphositePlus56. We considered as expressed 10,579 protein-coding genes with a qualitative expression level of at least ‘low’ and a reliability score equal to ‘approved’ or higher. For each protein-coding gene, we computed the expression breath as the fraction of tissues in which the given gene is considered as expressed over the total number of tissues present in the Human Protein Atlas .We downloaded protein expression data in human tissues based on immunohistochemistry from the Human Protein Atlas (version 18)post hoc Dunn Test, which performs multiple pairwise comparisons between the individual distributions . The network in Fig. 79.Distributions of disorder propensity and low complexity content fractions, PTM densities and tissue expression breath ratios were compared by using a two-sided Kruskal-Wallis test (significance level = 0.05), a non-parametric analysis of variance method. In case of a null-hypothesis rejection, we applied a All data generated or analyzed during this study are included in this published article and its supplementary information files. The predicted protein-RNA interactions are available from the corresponding authors on reasonable request.Supplementary InformationDataset 1


Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method.All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms:  Computational methods have been leveraged to rapidly estimate protein interaction networks. Given advances in compute power and more efficient prediction algorithms, we are now able to estimate comprehensive interactomes  for several species3. A number of paradigms have emerged in the field of PPI prediction including sequence-based4, structure-based5, evolution-based6, ontology-based7, and network-based methods8, each with varying degrees of success and compatibility9.Elucidating the protein-protein interaction (PPI) network of an organism is necessary to understand cellular function and study disease pathogenesiset al. demonstrated that Support Vector Machines (SVMs) used with auto-covariance can be successfully applied to sequence-based PPI prediction tasks10. Hamp and Rost expanded upon this work with the incorporation of evolutionary profiles to generate profile-kernel SVMs, demonstrating further improvements to PPI classification performance11. Neural networks with varying model architectures have also been successfully leveraged and most recently, Sun et al. have demonstrated that stacked denoising autoencoders  can achieve similar successes to predict PPIs12. The sites of a protein which mediate PPIs have also been predicted algorithmically13 and using Random Forest ensemble models leveraging features extracted from the physiochemical properties of protein structures14. More recently, Wang et al. used a Rotation Forest ensemble approach leveraging multiple sequence alignments which was found to be successful in the large-scale identification of PPIs in several species15. Overall, the field of PPI prediction has been highly active in the last decade, with new methods proposed each year as recently reviewed in Kotlyar et al.16. While the field of PPI prediction is methodologically diverse, irrespective of the paradigm, learning algorithm, and scale of the number of predictions, the field has certain fundamental commonalities.Historically, a broad range of machine learning algorithms have been applied to various facets of PPI prediction. Guo 17. While some proteins can exhibit promiscuous binding behaviours, as in the case of hub proteins, others are highly specific with few binding partners18. The selection of an appropriate decision threshold remains an open question and directly influences the performance of a PPI predictor.All of these methods examine the query protein pair and output a score denoting the predicted likelihood that the pair will physically interact. Despite the methodological differences between each paradigm, common to each is the intrinsic need to apply a decision threshold to the score that distinguishes the set of high-confidence predicted PPIs, potentially warranting experimental validation, from the set of protein pairs that are unlikely to physically interact. Establishing the expected number of PPIs for any given protein is an openly debated question, which is further complicated given that a protein can exhibit context-specific binding patternsi.e. the triangular number), the computational time for a given PPI using a given predictor is critical. For example, the human proteome contains over 20,000 proteins representing over 200 million potential interactions. A method predicting one interaction per second would require over 6.3 years to examine all pairs and produce the complete human interactome. This has prompted research groups to develop optimised predictors and leverage high performance computing. Only recently have these high-throughput predictors afforded us the capability to scale predictions and consider the comprehensive set of all possible interactions3. This has given rise to context: the ability to appraise a given PPI prediction relative to all possible interactions. While high-throughput methods have now enabled the study of a given PPI within the context of all protein pairs, the interpretation of these results is critical to appropriately evaluate the PPI predictor.Historically, PPI prediction tasks have been limited to modest subsets of the complete interactome enabling the elucidation of localized sub-networks or the identification of an interspersion of isolated PPIs relative to the complete interactome. This limitation is due to the algorithmic time-complexity of most PPI predictors, particularly those examining protein structure. Given that the number of putative PPIs grows as the square of number of proteins  have been adopted as the new standard19.Traditionally, cross-validation experiments are used to evaluate the performance of a trained predictor: a random subsample of all interactions are retained, the remaining data are used to train a model, and the subsample is then evaluated. Receiver Operating Characteristic (ROC) curves are widely used to compare the performance between models and are leveraged to select an operating threshold that balances sensitivity and specificity. The area under the ROC curve (AUC) is often used to summarize a model’s performance across all possible decision thresholds, although the value of such a summary statistic is questionable, particularly in the case of highly imbalanced problems such as PPI prediction. Increasingly, the suitability of ROC curves and the AUC metric is being reconsidered in various fieldsOnce a decision threshold is selected, it is applied globally to all predictions and is used as a binary discriminator: predicted PPIs scoring above the threshold are considered positives, warranting further investigation, whereas those below are considered negatives. Tuning this threshold to less conservative levels threatens to introduce a large number of false positives, thereby reducing the utility of the classifier.xi and yj in pair Pij. Researchers studying protein xi would typically consider the set of sorted scores for all pairs k is arbitrary since no single value of k can be optimal for all proteins. Furthermore, when considering the interaction Pij, this top-k ranking approach only considers the scores of pairs involving xi, but not all pairs involving yj. That is, they are only leveraging half of the available context.In PPI prediction tasks, a quantitative score can be assigned to a given protein pair of interest, say proteins 22. Reciprocal Best Hit is an example of the most conservative application of a local threshold, where k = 1. While useful for determining an orthologous relationship between the two genes that are typically expected to have a single ortholog in other species, our situation differs as proteins are expected to participate in several PPIs  for correlation with phenotype or experimental condition. The established method for controlling against multiple testing has been to adjust family-wise error rates (FWERs) such as using the Holm-Bonferroni method23. The control of FWERs has been considered too conservative and severely compromises statistical power resulting in many true loci of small effect being missed24. Recently local FDRs (LFDRs) have been proposed and are defined as the probability of a test result being false given the exact value of the test statistic25. The LFDR correction, through re-ranking of test statistic value, has been demonstrated to eliminate biases of the former non-local FDR (NFDR) estimators26. Our application is similarly motivated, though differs in that we consider the paired relationships between two elements and leverage the context of a given protein relative to all others.A similar challenge arises in the control of false discovery rates (FDRs) in applications such as genomics where high dimensional genotyping arrays are used to evaluate millions of variants . While useful for post hoc evaluation of cluster density, cliques, and protein complex prediction28, these approaches are notoriously plagued with false positives29. Network-based PPI prediction methods will attribute scores based on these quantitative metrics and often incorporate external information to re-weight their score such as protein localization, co-expression, and literature-curated data.The network-based analysis of PPI networks contextualizes proteins using undirected connected graphs, often with a scale-free topology and hierarchal modularityIn essence, various methods have taken into account localized decision thresholds, paired comparison, PPI context, and rank-order metrics, however no one modality has leveraged these in combination and proposed a unified method to determine localized decision thresholds for predicted PPIs based on their interactome context. Again, such analysis has only recently become possible with the development of efficient high-throughput methods capable of assessing all possible protein pairs.31 wherein the same polypeptide will participate in several interactions. Furthermore, the subset of proteins known to exhibit promiscuity, given their high affinity binding sites, tend to have an increased probability to develop new connections over time relative to proteins exhibiting modest binding affinities32. These hub proteins have the tendency to interact with considerably larger number of proteins and often have physiochemical sites (structured or unstructured) enriched with intrinsic disorder enabling an above-average propensity to interact with other proteins35.A central problem to PPI prediction is the wide variance observed in the number of actual interactions of each protein (the degree from network-based terminology). This is further complicated when considering protein binding site characteristics, where some proteins exhibit highly specific interaction profiles, while others have a tendency towards promiscuity due to large  binding sitesA, produce a large number of high-scoring targets, one naturally questions the validity of each score and would favor a conservative decision threshold, DA. Conversely, when applied to protein B and only a small number of high-scoring targets are observed, even if these few are low relative to the stringent decision threshold DA, one might be inclined to pursue these few predictions further.PPI predictors trained with known interactions involving promiscuous proteins risk developing a bias towards these windows, thereby producing an inflated predicted score for such proteins relative to others. While useful for the identification of hub proteins, resulting PPI models risk having an over-representation of these windows and an under-representation of their highly specific counterparts. Given that true PPIs are rare and PPI predictors tend to over-predict interactions for some proteins while under-predicting for others, we aim to quantify this behaviour and incorporate it into the decision function. True interactions are rare among all possible pairs and predictors exhibiting a bias towards certain proteins is problematic. For example, should a PPI predictor, when applied to a given protein One-to-All Score Curve using seven example proteins from the Saccharomyces cerevisiae predicted interactome36, where PIPE was used to predict all scores between the yeast proteome and seven diverse yeast proteins. These One-to-All Score Curves depict the rank order distribution (i.e. the sorted descending ordering of scores) of a single protein. When plotted together, these curves illustrate the comprehensive set of predicted scores for one protein, enabling researchers to interpret each distribution relative to other proteins and compared to the globally defined decision threshold , one can examine A-B in the context of all of A’s putative interactions (A-*) and also in the context of all of B’s putative interactions (*-B). Here, we formalize these concepts into a generalized method termed RP-PPI  and demonstrate its ability to improve the classification accuracy of two state-of-the-art PPI predictors using a cascaded Random Forest classifier.We introduce a novel concept in the application of interactome-wide analyses of protein-protein interaction networks: Reciprocal Perspective (RP), which jointly considers an interaction from the perspective of each partner to determine a new assessment of the interaction. Intuitively, when two elements interact, that interaction may have differing implications from each element’s respective perspective. Therefore, the relationship can be characterized from the two perspectives separately, taking into account the context relevant to each element. We define Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus.Inspired by the visualization of the rank order distribution of PPIs for selected proteins Fig. , we seekThe generation of the set of comprehensive PPI predictions using the two state-of-the-art methods for each of the five organisms is described in the Evaluation Design section and their results were used here. An examination of several one-to-all PPI score curves reveals a characteristic “S”- or “L”-shaped curve with a certain number of higher-scoring PPIs relative to a large baseline. Due to the inherent class imbalance in PPI prediction tasks, true interactions are rare. We therefore assume that the baseline region corresponds to the typical score assigned to a non-interacting protein pair involving the query protein. The baseline level thus provides a proxy for estimating the degree of bias that the predictor has for the query protein; as described above, some proteins will universally produce higher prediction scores than others. We therefore seek to estimate this baseline level and identify those putative interactions residing significantly above this baseline score.37. LOESS curve fitting is parameterized by the span parameter, α, which determines the proportion of points considered in a window and controls the degree of smoothing. Parameter tuning was accomplished by varying α over the  range and observing the quality of curve fit over a stratified sample of 1% of proteins. The space of one-to-all score curves was stratified and visualized by first sorting all curves by their maximum score and then plotting a surface over all curves in a third dimension in increments of 100 proteins. We could therefore qualitatively appraise the fit of the curve with a given α over the space of all one-to-all curve shapes by randomly selecting a representative from each increment, accounting for 1% of the complete dataset (repeated for each method and organism dataset). A preliminary coarse-grained analysis restricted our range to the  range, wherein a fine-grained analysis varying α by 0.01 was used to select the final value. Repeating the stratified sampling procedure, a value of α = 0.10 was selected as it provided qualitatively the best fit and generalized across both methods and for distributions across the five organism datasets. Fig. To differentiate between high-scoring PPI and baseline, we seek to identify the knee of these curves. A continuous smooth curve is therefore fit to the one-to-all score curves and the maximum value of the second derivative of the fitted curve represents the knee. Given the variability of the shapes of the curves we do not assume a specific distribution and use the non-parametric Locally Weighted Regression (LOESS) method to fit low-order polynomials degree = 2) to local windows of scores to produce a piecewise continuous curve to locala as a is said to have a proteome of size n. Similarly, organism b has a set of proteins defined as m. We define C is the set of all possible interactions between organisms a and b:R represents the binary protein-protein interactions relation over the set C.We define our metrics using set theory. We first define the set of proteins for a given organism i.e. organism a is also organism b), we satisfy xiyj. The score of a given PPI, xi is defined as the totally ordered set yj is similarly defined as xiyj as xiyj in X, and yjxi in Y and say that In the case where we consider intra-species interactions  value is the product of the rank order values, inversed to cast into the This representation doesn’t account for the proteome size and the potentially imbalanced number of predictions when comparing predicted values from different species. To account for inter-species predictions where each organism has a differently sized proteome (such as predicting PPIs between host and pathogen), we must normalize the NaRRO value to avoid artificially inflated values from smaller proteomes. To correctly normalize scores for inter-species interactions we define the Adjusted Reciprocal Rank Order (ARRO); we normalize each rank order value with the respective proteome size:This corrects any biases due to differently sized dataset in inter-species predictions. ARRO values are positive float values in the range i.e. A = B), we simplify the ARRO notation to define the Normalized Reciprocal Rank Order (NoRRO) which normalizes each RRO value by dividing by the proteome size, where p is the proteome size, p = n = m.Finally, for the case of intra-species predictions 13 and the Scoring PRotein INTeractions (SPRINT)3 algorithms. PIPE has successfully been used in the comprehensive prediction of a number of intra- and inter-species interactomes since its release in 2006 including H. sapiens, S. cerevisiae, Human Immunodeficiency Virus, the Zika Virus, Plasmodium falciparum, and Glycine max39. Leveraging pairs of known interacting proteins, the PIPE algorithm identifies subsequence regions of similarity (termed “windows”) and uses these windows to determine whether or not pairs of unknown PPIs are likely to interact. High frequency windows present challenges as they may artificially inflate predicted scores for those proteins that contain them. Efforts to normalize variable frequency windows have previously been proposed, most notably with the introduction of the Similarity-Weighted (SW) scoring metric used in PIPE3, which normalizes the PIPE-Score for a given window by the frequency of that window throughout the training dataset. The SW score is a positive, float value which captures the likelihood of interaction between any two proteins. We consider this as the original score in the RP method.We validated our method using the comprehensive set of predicted scores from two state-of-the-art PPI prediction methods and on five organisms: 3. It has been compared to several state-of-the-art methods including PIPE2, as in40. For H. sapiens, the SPRINT method has been postulated to out-perform the PIPE2 algorithm, suggesting it is comparable with the state-of-the-art3. This work used the PIPE3 (i.e. MP-PIPE) model which was previously successful in generating the first comprehensive H. sapiens interactome2. An explicit comparison of the SPRINT and PIPE3 methods is beyond the scope of this work and here the two are used as independent methods capable of generating predicted scores for any given PPI, and therefore are amenable to analysis using RP.The SPRINT algorithm is also a sequence-based PPI predictor amenable to the comprehensive prediction of all possible PPIs. It distinguishes itself from similar predictors by using a multiple spaced-seed encoding to prune away high frequency elements that occur too often to be involved in PPIs, and then returns a score indicative of the probability to interaction41. Specifically, the “conservative” dataset, as defined in41 was acquired for each organism except for the C. elegans where the “permissive” dataset was used following the recommendations of41 for organisms where the conservative dataset is excessively stringent resulting in very few training samples. This framework pre-processes the data and ensures a consistent definition of a PPI and provides the corresponding protein sequences data for all proteins present in the dataset. The training data for each of the five organisms is summarized in Table i.e. “all-to-all”) was obtained using the respective default parameters for each of the PIPE and SPRINT prediction methods for each organism.PPI predictions for five different organisms were used to validate the RP method. To generate the comprehensive set of interactions, the high quality PPI datasets to train and evaluate each method were obtained using the Positome framework42.With the all-to-all datasets computed for each organism over each method, the global threshold was determined for each model and for each organism. The leave-one-out cross-validation (LOOCV) method will remove a known interaction from the positive training dataset, build a model on the remaining, and then predict that removed interaction. It is considered the computational equivalent of wet-lab experimental validation of a PPI and useful in estimating the global decision threshold of a given predictor. Given the large class imbalance, it is crucial to select a decision threshold which minimizes the number of false positives. For both methods, the specificity was set as 99.95% and the corresponding decision threshold was chosen as inscikit-learn Python library default parameters where tree depth was unbounded and feature selection occurred over the root of the number of features. We selected a forest size of t = 100 trees in favor of the default t = 10 since a finer-grained classification confidence and numerical precision could be obtained from the t number of trees. We opt to arbitrarily select these hyperparameters as we cannot expect that there exists a singular set of Random Forest hyperparameters which will uniquely lead to the best increase in performance over all methods and all organisms. Moreover, using the same set of hyperparameters in our Random Forest models enables us to appropriately compare the results between the two principal conditions  for a single dataset, as well as observe consistent improvement in predictive performance across all methods and organisms. The feature space with respect to t and PRC-AUC was explored and summarized in Supplementary Figure The RP method was then used to compute the defined metrics for each PPI and a feature matrix was generated. To evaluate the improvement provided by the RP post-processing method, the set of positive PPIs and an equivalently sized subsample of negative PPIs (sampled without replacement) was created to train and evaluate a Random Forest classification model. We used the 43. Each model was produced using a five-fold cross-validation to further avoid overfitting the data. The feature subsets types  of the post-processed RP features, from Table TP is the estimated true positive rate of the classifier, FP is the estimated false positive rate of the classifier, r is the expected ratio of negative to positive samples in the real-world data, Sn is the estimated sensitivity, and Sp is the estimated specificity of the classifier. The precision is interpreted as the portion of positively predicted PPIs that are actually true interactions whereas the recall is interpreted as the proportion of positives that were correctly classified as true. Here we use a value of 100 for r representing a 1:100 class imbalance, as estimated and used in42. Finally, to quantify the statistical difference between the non-RP and RP-enhanced conditions we used bootstrap testing over 1,000 iterations and summarize the resulting PRC and ROC curves using the area under the curve metrics: PRC-AUC, ROC-AUC.We selected this machine learning method given that they have been demonstrated to be robust against overfitting, relatively simple to tune, and generally outperform standard classifiersThe data that support the findings of this study are available from BioGrid (known PPIs) and UniProt (protein sequences).τ has been a source of much debate. Applications with balanced and equivalently valued classes can simply select the point which jointly maximizes the sensitivity and specificity of an ROC curve  at the expense of the true positive rate. As a result, we have an increased confidence in the predicted positives (high precision), however this comes at the cost of missing a majority proportion of actual positives (high number of Type II errors).Throughout the history of PPI prediction, and in general applications of machine learning, the question of selecting an appropriate decision threshold 42; however, the visualization of predicted scores relative to that globally applied decision threshold provides insight into the distribution of the scores of a given PPI in the context of each participating protein relative to all others within the proteome. Most notably, we observe that the definition of a single conservative and globally applied decision threshold appears to be appropriate for a subset of proteins and inappropriate for others  necessitating predictors capable of producing predictions on the order of the fraction of a second. With the advent of such predictors and the availability of cloud computing resources, such interactomes are available and give rise to context: the ability to appraise a given interaction in relation to all others. This enables the opportunity to estimate the baseline on a per-protein basis and derive quantitative metrics of a given PPI in relation to all others and inspired the development of the RP framework.Generating a score for all possible protein pairs is a computationally demanding process. Historically, only targeted subsets of protein pairs were considered and therefore did not face scalability challenges. The size of the comprehensive set of all intra-species predictions is the triangular number of the size of the proteome, Leveraging visualization techniques, we observe considerable variation in the distribution of comprehensive predictions between the one-to-all curves of various proteins Fig. . This isTo evaluate the improvement of the post-processing RP layer, we examine the contribution of each feature type and summarize the classification performance enhancement when utilizing RP-Enhanced context-based features. Figure n = 1,000) sampling random subsets of PPIs, performing the cross-validation training and testing, and evaluating the resulting classification performance. Considering the null hypothesis, (H0: no significant difference in summary statistics between non-RP and RP-Enhanced) we computed p-values using Welch’s unequal variances t-test  and found the observed differences in AUC to be significant at the p < 0.001 level, for all cases. These results are summarized in Table t. In combination, these findings support the claim that the incorporation of context to derive post-processed features from the comprehensive predicted output can help further improve the classification results and these improvements promise to apply to any PPI predictor capable of generating these compressive predictions.To test for statistical significance of the difference between the non-RP and RP-Enhanced classification performance bootstrap testing was performed. A distribution of PRC-AUC and ROC-AUC summary statistics were formed by repeatedly . Due to the sensitivity of machine learning methods to the directionality of the inputs, we investigated the symmetry property of the RP method to account for the arbitrary selection of the paired ordering (choosing A-B vs. B-A). To quantify the difference of swapping the directionality of the paired relationships we ran permutation tests  comparing the two directionalities across the four conditions (both organisms and methods) computing the percent difference over the same summary statistics ROC-AUC and PRC-AUC, in addition to the F1 Measure, and Test Accuracy. Across all conditions and metrics, the largest percent difference observed was 0.55% with an average of 0.33%. From these experiments, we conclude that the magnitude of these differences are negligible. However to eliminate them in future applications of RP, we consider combining the complimentary metrics into a single averaged value thereby guaranteeing the symmetry of the method.The RP framework has been defined in such a way that it is amenable to any weighted complete graph problem. PPI predictions are an example of an undirected graph problem, however, the framework is extensible to directed graph problems. A directed graph problem can easily be converted into an undirected graph problem by averaging the values of the directed edges  using two state-of-the-art PPI predictors, PIPE and SPRINT, over five organisms: H. sapiens, S. cerevisiae, A. thaliana, C. elegans, and M. musculus.In this work, we suggest revising the assumption that a single global threshold can be appropriately defined across the proteome due to the inherent diversity of protein interaction profiles. In leveraging visualization techniques, we propose that the recent development of PPI predictors capable of generating the comprehensive set of putative interactions has given rise to context, enabling us now to evaluate a putative PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective, which estimates a localized threshold on a per-protein basis using several rank order metrics. We demonstrate that it significantly improves classification performance (ROC-AUC, PRC-AUC; Supplementary Figure S1


The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physicochemical and geometrical properties of the protein surface with information coming from complete cross‐docking (CC‐D) simulations. We show that our predictions match well interacting regions and that the different sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET Given a2, for predicting interacting patches based either on protein sequence and structure analysis or on any pre‐computed residue based property. All data and implemented code are available at: http://www.lcqb.upmc.fr/dynJET2/.We provide sets of experimentally known interaction sites and regions and CC‐D results for our dataset of 262 proteins, along with a computational tool, called dynJET22.12.1.1http://www.ihes.fr/carbone/HCMDproject.htm), for which we performed CC‐D. We considered the subset of PDB structures comprising a protein complex previously reported in Reference α structures, (b) chains for which docking results were missing, (c) chains forming coiled‐coils complexes, (d) deprecated PDB codes, (e) chains for which no biologically relevant interface (see subsection Surfaces and interfaces for definition) could be found in the whole PDB . The remaining 262 protein chains comprise on average 200.5 ± 131.2 residues (Table We defined a dataset of 262 protein chains and associated Protein Data Bank (PDB) structures featuring both single and multiple partners interactions. This dataset was extracted from a larger set of 2246 protein chains defined in the scope of the HCMD2 project  extracted from the PDB files associated to P‐262 and extPPI‐262 comprises 370 IRs  defined from PDB files of close homologs of the proteins from P‐262.We defined two datasets of experimental protein‐protein interfaces, namely xtFigure . Both daextPPI‐262  rigid‐body coarse‐grained docking program.i, belonging to protein P, asNpos,P is the total number of docking poses considered for protein P and Nint,P(i) is the number of docking poses where residue i lies in the interface. In order to limit the number of docked interfaces to reconstruct, which is the most time‐consuming part of the analysis, we considered only the lowest energy docking poses  values to formally define a normalized interaction propensity score, called NIP, that dynJET2 uses in order to predict interface sites.Below, we shall use 2.3JET, PC, CV, and NIP, are used to evaluate single residues in a protein and to define scores for the prediction of protein interfaces.Four measures, TJET reflects the evolutionary conservation level of a residue, and is computed from phylogenetic trees constructed by using sequences, homologous to a query sequence and sampled by a Gibbs‐like approach.N representative subsets of N sequences%, [40‐59]%, [60‐79]%, and [80‐98]% sequence identity with the query sequence are similar . Sequences in a subset are then aligned using CLUSTALW2T, a tree trace level is computed for each position in the query sequence: it corresponds to the level n in the tree T where the amino acid at this position appeared and remained conserved thereafter j∈P are the average IP and the maximum IP, respectively, computed over all the residues j in P. The NIP value represents how often a residue is docked on the retained conformations .NIP is the normalized form of the Interface Propensity score JET, PC, CV are computed using dynJET2, a modified version of JET2These four residue‐based properties were previously shown to be useful for the prediction of protein interfaces.2.4% of relative accessible surface area (rasa), as computed by Naccess.A residue is considered to be at the surface of the protein if it displays at least 5Experimental interfaces are detected on known PDB complex structures, considering only biological assemblies. We define two types of interfaces, namely interacting sites (ISs) and interacting regions (IRs). ISs are detected in single PDB structures by using the INTBuilder softwarehttp://www.lcqb.upmc.fr/INTBuilder/) with a distance threshold of 5 Å. They may represent single‐ or multiple‐partner interactions , then we will define one multi‐partner IS at the surface of A , will be merged into an IR if their maximum overlap with respect to their respective sizes , overlap]) is greater than an arbitrarily chosen threshold of 60%. In practice, this threshold gave us the most realistic IRs given the experimental information. This merging criterion is relaxed when dealing with very small ISs. Namely, we request that an IS comprising at most five residues shares at least one residue in common with another IS to be merged with it. The merging procedure is iterated over all ISs for a given protein. To construct PPI‐262 and extPPI‐262, we retained only ISs and IRs of reasonable sizes, that is, comprising more than five residues.s Figure A. For inPredicted interfaces are identified by the dynJET2 software (http://www.lcqb.upmc.fr/dynJET2/). Given the sequence and the structure of a query protein, dynJET2 predicts the location of potential protein binding sites on the protein surface.2 implements a clustering algorithm and scoring strategies specifically aimed at detecting the different layers of a protein interface, namely the support, the core and the rim.predicted patch. We used the iterative mode of dynJET2 (i‐dynJET2) and considered a residue to be predicted as interacting if it was detected at least twice over 10 iterations (as done in Reference 2 (compared to three in JET2cons targets very conserved residues (identified by the TJET score) to form a seed which is then extended using both TJET and PC scores. An outer layer is added considering both PC and CV scores. SCcons is intended to detect diverse protein binding sites.SCnotLig detects both seed and extension layers using a combination of TJET and CV scores. It aims at detecting highly conserved residues that are not buried too deeply beneath the surface of the protein. The outer layer is defined based on PC and CV scores, as in SCcons. SCnotLig specifically distinguishes protein interfaces from small ligand binding sites.SCgeom disregards evolutionary information and solely employs PC and CV scores for detecting all three layers of the interface. SCgeom yields consistent predictions for interfaces displaying very low conservation signal, eg, antigen binding sites.SCdock applies the NIP score of the residues to all three layers . The usage of NIP is motivated by the observation that proteins tend to dock to their cognate partners and also to noninteractors via the same region at their surface.2.SCTo evaluate the performance of our predictions, we mainly relied on the F1‐score, which is the harmonic mean of precision and recall. Predicted patches were compared to ISs from PPI‐262 and IRs from extPPI‐262. The union of all predicted interface residues was also compared to the union of all experimental interface residues, for each protein.Four scoring schemes or strategies are implemented in dynJET2.52's different scoring schemes were collected. The SCcons seeds were discarded because almost half of their residues were shared with the SCnotLig seeds  was computed between the query structure from P‐262 and each of the homologous structures on which the IR was detected. For each homologous structure, only the subset of residues detected on this structure were considered to compute the RMSD. RMSD values were then averaged over the homologous structures . This gives us a single RMSD value for each IR.For each IR, the RMSD of its backbone atoms  was applied on the 262 protein chains from P‐262. Multi‐VORFFIPhttp://www.bioinsilico.org/cgi-bin/SUPER_VORFFI/htmlVORFFI/home) was applied on a subset of 252 protein chains. The 10 proteins that were discarded for the analysis either belonged to the training set of Multi‐VORFFIP or produced an error when running the tool. For both tools, we considered residues displaying a probability above 0.5 as predicted to interact. Residues separated by less than 5 Å were subsequently clustered to form predicted patches.SPPIDER33.1P‐262, involving 262 protein chains . Based on the observation that functional interfaces are conserved across closely related homologs,P‐262 or their close homologs (≥ 90% sequence identity) from the PDB.extPPI‐262. The two examples in Figure Our analyses were performed on a set of protein complex crystallographic structures, which we call ng ISs ≥ % overlap3.2P‐262 by relying on four residue properties : evolutionary sequence conservation inferred from the analysis of homologous sequences, physicochemical properties expected at the interface based on experimentally known complex structures, local geometry computed on the protein 3D structure, and propensities to be found at docked interfaces inferred from CC‐D calculations  aimed at identifying different types of protein‐protein interfaces . It reflects the propensity of each protein residue to bind partners and non‐partners in docking calculations. To evaluate docking conformations, we used a coarse‐grained empirical energy function comprising a Lennard‐Jones potential for van der Waals interactions and a Coulomb potential for electrostatics.2, an upgraded version of the JET2 method.We predicted interacting patches at the surface of the proteins from s Figure . The fir3.3PPI‐262 cover ∼30% of the protein surface , the truncated distributions  are indistinguishable . The differences at the extremities are the following: the minimum coverage is higher for predictions than for experimental interfaces (18% vs 6.2%), and there are more proteins completely or almost completely covered (≥ 80%) by predictions than by experimental interfaces. The first difference can be explained by the specifics of dynJET2 clustering algorithm, which discards very small predictions  display sizes similar to those of experimental ISs  predicted patches are bigger, covering about one third of the protein surface, on average  are the smallest . To do so, we relied on the F1‐score, which reflects the balance between precision  and recall (or sensitivity). The average F1‐score on PPI‐262 is 0.41 ± 0.24 and it increases up to 0.57 ± 0.19 on extPPI‐262 . The proportion of proteins with very good predictions (F1‐score > 0.6) increases from 18% to 46%, while that of proteins with very poor predictions (F1‐score < 0.2) drastically reduces from about one quarter to 4%. These results highlight the importance of considering all available experimental information to properly evaluate protein interface predictions. Predicted residues that would be considered as false positives when looking at the restricted dataset, PPI‐262, are actually involved in interactions with other partners, as revealed by the extended dataset, extPPI‐262.The identification of a protein's set of interacting residues is important to understand the determinants of molecular association. For each protein, we compared the union of all predicted patches with the union of all ISs (respectively IRs) from cons, SCnotLig, and SCgeom enables increasing the average F1‐score by about 0.1 compared to individual SCs, and drastically reducing the number of completely missed IRs to only 28 over 370 . In particular, the detection of the binding sites at the surface of antibodies and G proteins is very sharp, with average F1‐scores of 0.67 and 0.64 . Hence, evolutionary conservation, physicochemical properties and local geometry are generally able to better capture protein interface signals than the coarse‐grained empirical energy function used in the docking experiment. Nevertheless, there are a number of cases where docking‐based data provide valuable information to improve predictions by unveiling interfaces that could not be detected otherwise. An example is given by the anticoagulation Factor X  but completely missed by the other SCs.To better characterize the contribution of docking‐based information, we compared the predictive performance of SCk Figure B. We obsX Figure C, where 3.5extPPI‐262 which are only slightly (<1.5 times) bigger than the corresponding IS(s) from PPI‐262. Four examples are illustrated on Figure 2 predictions is equal to or greater than that computed on the corresponding ISs . Almost all (95%) IRs display average conformational deviations lower than a 4 Å . Another example is given by the interface between the heavy and light chains of the anticoagulation factor X which is well detected by SCdock . Some other patches cover several IRs, as exemplified by SCgeom in Profilin  IRs from % IRs frok Figure C, in redn Figure A could b2 clustering algorithm. We collected all seeds generated by SCnotLig, SCgeom, and SCdock and clustered them based on 3D proximity . The total number of resulting clustered seeds is 562, which corresponds to 2.14 seeds per protein on average. By comparison, the average number of IRs is 1.4. About one quarter of the seeds are completely inside an IR (100% precision) and almost than half of the seeds detect an IR with very high (≥80%) precision . SPPIDER implements a consensus based classifier that combines 10 different neural networks. It integrates various sequence‐ and structure‐based features, those contributing the most being based on solvent accessibility prediction. It was applied on all proteins from P‐262. The distributions of F1‐scores obtained for the union of the residues predicted by Multi‐VORFFIP and SPPIDER, respectively, are similar to that obtained for the union of dynJET2 predictions . In particular, dynJET2 is more sensitive than the two other tools , disorder and post‐translational modifications. In this work, we have analyzed a pool of proteins with different functions to address the two first aspects.2 predictions, we could appreciate that a large amount of predicted patches better matched IRs, compared to ISs. This result is expected from a good protein interface prediction algorithm, as the notion of IR seems more biologically pertinent than that of IS in many cases, especially when the IR synthesizes the variability inherent to structure ensembles of the same complex.In line with a previous study,Our predictions were generated based on three sequence‐ and structure‐based properties of monomeric protein surfaces and also on residue propensities inferred from docking calculations. The latter reflect how energetically favorable the interatomic interactions established between a protein and many other proteins are. The energy was evaluated by a combination of Lennard‐Jones and Coulomb potentials. We have systematically assessed the contribution of this physical description of protein interactions to the prediction of interfaces. We have shown that in most cases, its predictive power is lower than that of the sequence and structure‐based descriptors. Nevertheless, it brings complementary information, which helps to improve the accuracy of the predictions and, in some cases, it even unveils binding sites that could not be detected otherwise.We have highlighted several cases where almost the entire protein surface is involved in functional interactions. This finding challenges the role of specificity in the evaluation of protein interface prediction methods and rather put the emphasis on precision. We have demonstrated the usefulness of the prediction patches' three‐layer structure by showing that the patches' seeds enabled precisely locating and discriminating IRs at the protein surface. We have further shown that the seeds could help determine whether a protein has a few or many partners. Future work will aim at getting a more accurate estimation of the number of partners. Moreover, with the help of future PPI data, it seems achievable to associate functions to the partners binding on different surface areas, described by different seeds on a region.2 predictions match reasonably well experimentally identified interacting regions, the match is not perfect. Given the degree of complexity we have highlighted in the usage of a protein surface, it seems legitimate to ask whether perfect match with experimental interfaces is an attainable goal for protein interface predictions algorithms that, like dynJET2, do not use any knowledge about the query protein's partners. An accurate estimation of the maximum level of agreement one could expect would be most valuable. Besides, even without perfect match, dynJET2 predictions can be fully exploited to guide experiments. For example, the above‐mentioned ability of patches' seeds to precisely locate IRs has implications for the control and modulation of existing protein‐protein interactions. Mutating seed residues should impact the binding of the associated partners. Another way to go would be to design interactors that bind to the predicted patches. Indeed, dynJET2 algorithm provides a mean to delineate regions at the protein surface with sizes similar to those of experimental interacting sites or regions and complying with a few properties known to be relevant to protein‐protein association. Thus, in addition to detecting regions being actually used to interact, it also reveals the potentiality of other regions to interact.Although dynJETThe authors have no conflict of interest to declare.Appendix S1: SUPPORTING INFORMATIONClick here for additional data file.