dataesr/scientific-paragraphs-categorization huggingface dataset:
In other terms, if a surface residue is identified as being part of at least one of the experimental binding sites of the target protein, this residue is tagged as interface residue in the global reference experimental interface. Conversely, if a residue is not identified as being part of any of the experimental interfaces of the query protein, it is considered as non-interface residue in the global experimental reference. In the second score, named Best Interface (BI) score, and similar to the approach developed for the JET 2 ! 14 program  35  , the predicted interface is compared to each reference experimental interface separately, and only the predicted interface associated with the best binding site prediction performance was kept (Figure  S4b ).
Search for alternate interfaces protocol After coloring the residues of the 358 proteins of the SubHCMD dataset according to their PIP value, we realized a systematic visual inspection of the binding site predictions, which led to the identification of alternate binding sites predictions in 188 cases (53% of the protein dataset). To explain the existence of these predicted alternate binding sites, we searched for alternate experimental partners for the 358 proteins of the SubHCMD dataset. In this perspective, we first analyzed for each one of the proteins whether the original PDB structure, from which the protein was extracted, comprises other chains that are not included in the SubHCMD dataset, and which could bind on the predicted alternate interaction site. We distinguish the cases where the alternate experimental partner identified with this analysis is a protein ("Interface with another protein chain of the same pdb structure not included in the dataset") and the cases where the alternate experimental partner identified consists of a DNA or RNA molecule ("Interface with nucleic acid").
Then, for proteins with predicted alternate binding sites that remained unexplained after this first step of investigation, we searched in the PiQSi (Protein Quaternary Structure investigation) database  47  for possible partners fitting our predicted binding sites. This database allows the investigation and curation of quaternary structures by using information about the quaternary structure of homologous proteins. The PiQSi webser thus allowed searching for possible homodimeric structures of the proteins that are not described in the original PDB structure ("Interface from homodimers") and whose ! 15 interfaces correspond to the predicted alternate binding sites. We also investigated the quaternary structures of homologous proteins to identify complexes between proteins of the SubHCMD dataset and partners different from those observed in the original PDB structures to explain the predicted alternate binding sites.
Nucleic acid interfaces. Since 45 proteins in the SubHCMD dataset also present an interface with nucleic acids, we compared the 48 nucleic acid experimental interfaces (NAI) existing between a protein of our dataset and one molecule of nucleic acid (8 with DNA and 40 with RNA, 3 proteins presenting 2 distinct interfaces with RNA) to the 501 initial protein-protein reference experimental interfaces (PPI) formed by proteins of our dataset using the following metrics: -Fraction of each residue type in the interface (FRIres(type)): where Nres(type) [interf] is the number of residues from the corresponding type present in the experimental interface and Nres(total)[interf] the total number of residues in the interface. -Number of each residue type in the interface -Accessible surface area (ASA) computed with NACCESS  46  -Binding site global charge in the interface.
Using a one-tailed Wilcoxon test  48  , we evaluated for each one of these metrics whether the mean value associated with the NAI was significantly different from the mean value observed in PPI. We also evaluated whether the mean value of the fraction of each residue type in the interface (FRIres(type)) in the NAI was significantly different from the one observed in the whole corresponding protein surface (FRIres(type)[surface]): ! 16 where Nres(type)[surface] is the number of residues of the corresponding type present in the total surface and Nres(total)[surface] the total number of residues in the surface. Ocupancy rate. For every protein included in our dataset, we computed the occupancy rate of each predicted binding site on its surface, to see if some binding sites are more targeted by the protein partners during the simulations.
2P2I inspector. We used the occupancy rates to extract from the SubHCMD dataset 85 proteins (Table  S2 ) presenting one primary interface (PrimI), and one secondary interface (SecI). To be included in this part of the analysis, proteins should present at least 2 binding sites on their surface, and the occupancy rate for the PrimI should be at least 50% larger than the occupancy rate for the SecI (Table  S2 ). 43 descriptors (Table  S3 ) were computed for each one of the 170 protein complexes (85 complexes forming the PrimI and 85 complexes forming the SecI) using the 2P2I inspector website  49. Predicted interfaces with no corresponding experimental binding site or interfaces with a non-protein partner (nucleic acids) could not be included in this part of the study since the 2P2I inspector website only accepts protein PDB files as input.
The performances of the GI and BI scores are summarized in Table  1 , Figures  1  and 2. Differences only appear for the 96 proteins (out of the 358 proteins included in the dataset) associated with more than one experimental partner, the two scoring schemes being identical when only one experimental partner is available (Figure  S5 ). When considering the complete dataset of 358 proteins, the BI score is associated with a higher overall AUC value (0. 732 compared to 0. 696, while random predictions would give an AUC value of 0. 5) and lower minimum error (0. 48 compared to 0. 51) than the GI score (Figure  1 ). For 80 over the 96 proteins with more than one experimental partner included in the dataset, the best individual AUC is obtained with the BI scoring scheme. The GI scoring scheme is associated with an individual AUC superior to the BI scoring scheme for only 12 proteins over 96.
Predicted alternate interfaces The efficiency of the PIP values for predicting protein interfaces in different protein functional groups has been assessed in our earlier work  24  on 168 proteins from the Docking Benchmark 2. 0  28. Here the individual AUC values obtained with the 358 proteins of the dataset show a large distribution in the binding site prediction efficiency depending on the protein studied, even when using the BI score (see Figure  S6 ). 66 proteins are associated with very high binding site prediction efficiency, with individual AUC values superior to 0. 9, ! 20 whereas the individual AUC values lie below the random prediction threshold of 0. 5 for 68 proteins (i. e. 19% of the complete set). By coloring the protein surface residues with the PIP values resulting from the cross-docking calculations, we could observe predicted alternate interfaces that are visibly different from the expected reference experimental interfaces for 188 out of the 358 proteins included in the dataset (53%).
Rationalizing the prediction of alternate interfaces In an earlier study  24  , we showed for some examples that the apparent failure of cross-docking calculations for predicting experimental binding sites could be explained by the detection of ! 21 alternate interfaces formed by the protein with other biomolecular partners that are not present in the original reference dataset. We decided to push this analysis further in an exhaustive fashion by investigating whether it is possible to find experimental partners that would bind on the predicted alternate interfaces and to which extent. For 146 proteins over the 188 proteins (78%) with predicted alternate interfaces, we could identify an alternate experimental partner, not included in the original dataset (and therefore in the docking calculations), whose experimental interface with the protein under study corresponds to the predicted alternate interface.
Interfaces with another protein partner Interface with another protein chain from the same PDB structure not included in the dataset. Figure  3a  presents the case of the structural protein collagen alpha 2 (IV) (1LI1_F). In our dataset, 2 experimental partners for this protein are included: the collagen alpha 1(IV) (1LI1_B) and a second monomer of collagen alpha 2 (IV) (1LI1_C) (shown in grey and black, respectively, on Figure  3a ). However, the residues of 1LI1_F presenting the highest PIP values do not belong to any of the expected reference experimental interfaces, leading to a low individual AUC value of 0. 360. Nevertheless, the global 1LI1 PDB structure comprises six protein chains in total, and the residues of 1LI1_F presenting the highest PIP values are in fact those involved in the interface with two other chains of the same PDB structure not included in our dataset, 1LI1_D (Figure  3a , shown in smudge green) and 1LI1_E (Figure  3a , shown in green).
When adding the information about these new reference experimental ! 22 interfaces, the individual AUC of the binding site prediction associated with 1LI1_F increases to 0. 689 (corresponding to the interface with 1LI1_E). One must note that the binding sites of 1LI1_D and 1LI1_E on the surface of 1LI1_F are contiguous. These two sites form a large patch that can bind multiple partners (similar to what could be observed in refs. 35 and  36  ), and in this specific case, the GI score computed by comparing the PIP values to the global reference interface obtained by concatenating the 1LI1_D and 1LI1_E experimental interfaces performs better than the BI score (0. 800 vs 0. 689). We proceeded in the same way for each one of the proteins of our dataset and we identified 81 proteins over 188 with predicted alternate interfaces (43%), whose predicted alternate binding site corresponds to the interface with another protein chain of the same PDB that was not included in the original dataset.
In addition to these 81 proteins, 10 proteins (1AVO_B, 1D8D_A, 1D8D_B, 1JJO_C, 2NNA_A, 2NNA_B, 3BRT_B, 3BRT_C, 3C5J_A, 3C5J_B) present enhanced binding site predictions when adding new reference experimental interfaces from other protein chains of the same PDB structure not included in the dataset. These 10 proteins do not present obvious predicted alternate interface, i. e. their individual AUC values are superior to 0. 5, their FDR values computed at the optimal PIP cut-off of 0. 15 are below 0. 67 and no predicted alternate interface could be detected by visual inspection (Figure  S8a -j, left panels). In fact, as illustrated in Figure  S8a -j (right panels), the new experimental interfaces are overlapping the initial reference experimental interface, explaining why no predicted alternate interface was detected at first.
Finally, taking into account all these new reference experimental interfaces, the global performance of the binding site prediction obtained with the BI score and measured using the overall AUC increases from 0. 732 (value obtained with the initial reference experimental interfaces) to 0. 776 (Figure  S9 , green line). Interfaces from homodimers and heterodimers. Using the PiQSi webserver  47  , we searched for ! 23 alternate partners for the 188 proteins with predicted alternate binding sites. For 12 proteins over these 188 proteins (6%), interfaces formed in homodimeric structures (described in the PiQSi database) overlap alternate binding sites predicted using the PIP values, as illustrated in the Figure  3b. Adding these new reference experimental interfaces to the binding site prediction calculations leads to only a small increase of overall AUC (0.
736 vs 0. 732, Figure  S9 , blue line). In the same way, for 22 proteins over the 188 proteins investigated (12%), we could identify homolog proteins forming heterodimeric complexes that could explain the predicted alternate binding site. This is notably the case for the RalA protein (2BOV_A) that presents one of the lowest binding site predictions quality (individual AUC = 0. 232). As shown in the Figure  3c , cross-docking led to the prediction of an alternate binding site on the opposite side of 2BOV_A compared to the reference experimental interface with the C3 ribosyltransferase (2BOV_B). Using the PiQSi database, we could identify another complex (PDB ID: 1UAD) between the same RalA protein (1UAD_A) and a different partner, the Sec5 protein (1UAD_C). The experimental interface between RalA and Sec5 perfectly concurs with the alternate interface predicted by cross-docking on the surface of RalA (2BOV_A).
Single docking vs Cross docking To study whether similar interface prediction could be obtained by docking a single, randomly selected in the PDB, protein against one protein of the subHCMD dataset, i. e. using singledocking, we used the 4 proteins previously presented as test cases: 1LI1_F, 2P1L_B, 2BOV_A and 1GT0_D. For each case, we computed all the individual AUC values obtained ! 24 when performing single-docking with every proteic partner from the SubHCMD dataset (Figure  S10 ).
Correlation between JET score and PIP values To reduce the computational time associated with cross-docking, we used binding sites predictions from JET to restrain the conformational space of the docking algorithm. The JET score we used, ranging from 0 to 10, represents the number of time a residue was selected as interface residue by an iteration of iJET. To explore whether this initial pre-filtering protocol impacts the resulting PIP values and thus the binding site predictions, we evaluated the correlation between <PIP> (i. e. , the average PIP value of a given residue after docking with all the proteic partners from de SubHCMD dataset) and JET score values for each residue of each protein (see Figure  S11  for examples of correlation between <PIP> and JET score within a protein). An analysis of variance (ANOVA) on the distribution of <PIP> as a function of the JET score (Figure  4 ) shows that the average <PIP> for each JET score group differ significantly (p-value below 2E  -16  ).
Interfaces with nucleic acid molecules For 31 proteins over the 188 with predicted alternate interfaces (16%), the binding signal observed with cross-docking simulations turned out to correspond to a binding site with nucleic acids. The transcription factor SOX-2 (1GT0_D) is an example of such proteins. The PDB structure 1GT0 represents the complex formed between SOX-2 (1GT0_D), the octamerbinding transcription factor 1 (1GT0_C) and a DNA double strand. The 1GT0_C and 1GT0_D proteins were included in our cross-docking dataset, unlike the DNA double strand. When focusing on the cross-docking binding site predictions for 1GT0_D (Figure  3d ), a weak binding site prediction of the interface with 1GT0_C is obtained (individual AUC of 0. 595). Indeed, the residues of 1GT0_D presenting the highest PIP values are those surrounding the DNA double strand.
Thus, using our cross-docking procedure, we could identify interfaces with non-proteic alternate partners, like nucleic acids, not included in the original dataset used to run the simulations. New binding site prediction performances were computed including this additional experimental interfaces with nucleic acids leading to a slight increase in the overall AUC value (0. 739 vs 0. 732) (Figure  S9 , magenta line). Since our docking scheme has been developed to study protein-protein interactions and only proteins were included in the CC-D simulations, its ability to predict nucleic acid binding sites came as a surprise to us. However, these predictions concur with earlier work using evolutionary data, which showed that DNA and RNA binding sites do overlap with protein ! 26 binding sites in many cases  44.
Unexplained interfaces and global prediction performances For only 42 proteins over the 188 (22%) with a predicted alternate interface, we could not identify an alternate biomolecular partner explaining the predicted alternate binding site. When adding all the newly identified alternate reference experimental interfaces to the initial ones, the binding sites prediction performances jumped from 0. 732 to 0. 801 (Figure  1 , green line and Table  1 ), with enhanced sensitivity, specificity and precision values. Using the BI score with reference experimental PPIs for the original dataset, we failed to correctly predict the binding site location on the protein surface for 68 cases (out of 358 proteins), which presented an individual AUC value below 0. 5. After including the alternate experimental binding sites in our calculations, only 10 proteins kept an individual AUC value below 0.
Identification of protein interaction sites The ability of cross-docking simulations to identify binding sites on protein surface has already been proved and studied with docking benchmark datasets of 12 proteins for the proof of concept  21  , and 168 proteins for more recent studies  23, 24. However, to the best of our knowledge, the impact of the presence of multiple binding sites on proteins surface, a common feature observed in the protein world, has never been systematically addressed. We thus decided to tackle this problem, using 358 non-redundant proteins that were not extracted from a docking benchmark, but from a database of proteins conceived to study proteins involved in neuromuscular diseases or in the pathways monitoring essential cardiac or cerebral mechanisms. Among those proteins, 96 presented more than one experimental partner in the dataset and thus more than one binding site on their surface.
The differences observed in terms of specificity and sensitivity could be explained by the differences in the proteins dataset used. In the two previously published ! 30 studies, proteins were extracted from docking benchmark dedicated to protein docking evaluation, whereas in this study, the proteins were selected according to their biological functions and not for benchmarking purpose, and thus represent more challenging cases for docking predictions. In addition, one should note that the SubHCMD dataset only comprises bound protein structures, and our previous studies  24  have shown that these will perform less well than unbound structures for binding site prediction via CC-D, probably because a protein's specific conformational adaptation to a given partner might decrease the quality of its binding to other potential partners.
We chose to use the SubHCMD dataset and not a benchmark database since the SubHCMD is part of a bigger dataset we are currently investigating to learn more about some proteins involved in neuromuscular diseases. However, a comparable study could have been achieved with a benchmark database such as the Docking benchmark 5. 0  25  , DOCKGROUND  26  , or 2P2IDB  27. It is to note that some proteins included in our SubHCMD dataset are also present in these benchmarking databases (1ATN_A, 1ATN_D, 1US7_A, 1US7_B, 2C0L_A, 2C0L_B, 2OT3_A and 2OT3_B are in both SubHCMD and Docking Benchmark 5. 0; 1AOX_A, 2BOV_A, 2BOV_B, 1M63_E, 2C0L_A, 2C0L_B, 1MHW_A, 3BS5_A and 3BS5_B are in both SubHCMD and DOCKGROUND).
Identification of alternate interfaces different from the reference experimental interfaces Even using the BI score, i. e. the score accounting the best for multiple binding sites, the quality of prediction associated with some proteins was very low. A systematic visual inspection of the proteins, realized by coloring the proteins residues according to their PIP values, highlighted 188 cases where the predictions led to the identification of alternate binding sites. We could define a threshold of FDR (computed at the optimal PIP cut-off of 0. 15) equal to 0. 67 that could be used to replace the systematic visual inspection to detect ! 31 proteins with predicted alternate binding site. For 78% of the proteins for which we could visualize a predicted alternate binding site, we identified an alternate partner, not included in the SubHCMD dataset, which fits the corresponding interface.
Conclusion In this work, we evaluate the ability of our cross-docking protocol to lead to the identification ! 34 of interaction sites on proteins surfaces by using the PIP index computed for each protein residue. Unlike previous studies, the SubHCMD dataset of 358 proteins used is not a benchmarking dataset developed for protein docking evaluation, but a subgroup from a larger database (2246 proteins) built around about 400 proteins known to be involved in neuromuscular diseases. The SubHCMD proteins thus represent more challenging test cases for binding sites prediction, particularly for those presenting multiple binding sites on their surface. We first compared the use of two scores accounting for proteins with multiple binding sites: the GI score that compares PIP values to a single global reference experimental interface and the BI score that treats separately each known experimental interface and keeps only the one that is best predicted.
Overall, high quality binding sites predictions are obtained using the BI score, which is best suited for proteins presenting multiple binding sites. However, some proteins were associated with very low performance. In particular, 68 proteins (19 % of the complete set) presented an individual AUC below the random threshold of 0. 5. For these 68 proteins and 120 more, alternate interfaces different from the expected reference experimental interfaces were predicted. For about 80% of them, we could identify hidden partners, i. e. a partner not included in the SubHCMD dataset and that binds in the predicted interaction site. An unexpected result was the prediction of nucleic acid binding sites, probably linked to the force field used, which overestimates the electrostatic interactions between charged residues.
The ability of the cross-docking simulations to guide towards the identification of these alternate binding sites (and thus of alternate potential partners) is a very promising result since the final aim of the project is to understand the function, and to identify binding sites and potential binding partners for the 400 proteins involved in neuromuscular diseases included in the HCMD (Help Cure Muscular Dystrophy) dataset. We also showed that for proteins with multiple binding sites on their surface, one binding site can attract the ! 35 majority of the docking partners, and we could identify some interface structural determinants linked to our docking protocol. This study demonstrates that our cross-docking simulations protocol enables to identify accurately and efficiently binding sites on proteins surfaces and defines a first step in the analysis of the whole HCMD dataset of 2246 proteins.
Acknowledgments This work was originally carried out in the framework of the  DECRYPTHON  Project, set up by the  CNRS  ( Centre National de la Recherche Scientifique ), the  AFM  ( French Muscular Distrophy Association ) and  IBM. The cross-docking calculations were carried out on the  World Community Grid  (www. worldcommunitygrid. org) in phase 2 of the  Help Cure Muscular Distrophy  project. The authors wish to thank all the members of the  WCG team  for adapting our docking program for use on this grid, and also WCG volunteers for donating the computing time that made this work possible. S. S. -M. , A. C. and N. L. thank the  Mapping Project (Investissement d'Avenir )  ANR-11-BINF-003  for postdoctoral funding. A. C. thanks the  Institut Universitaire de France. This work was supported by the " Initiative d'Excellence" program  from the  French State  (grant «  Dynamo  »,  ANR-11-LABX-0011 ).