We present Interactome INSIDER, a tool to link genomic variant information with structural protein–protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.
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The authors would like to thank G. Hooker, D. Bindel, and K. Weinberger for helpful discussions and J. VanEe for technical support. This work was supported by National Institute of General Medical Sciences grants (R01 GM097358, R01 GM104424, R01 GM124559); National Cancer Institute grant (R01 CA167824); Eunice Kennedy Shriver National Institute of Child Health and Human Development grant (R01 HD082568); National Human Genome Research Institute grant (UM1 HG009393); National Science Foundation grant (DBI-1661380); and Simons Foundation Autism Research Initiative grant (367561) to H.Y.
The authors declare no competing financial interests.
Integrated supplementary information
(a) A schematic showing the five feature categories from which feature sets are optimized to train ECLAIR. (b) The portions of high-quality binary interactomes for which each feature type is available. (c) Feature aggregation strategies employed for combining multiple points of evidence into single co-evolution- and structure-based features. For co-evolution, we select the top co-evolved residue, the mean of features for the top 10 co-evolved residues, or the mean over all co-evolved residues in the partner protein. For proteins with multiple structures, we take the mean, minimum, or maximum SASA over all available structures.
Balance between testing/training and prediction sets of sequence- and structure-based feature depths. (a) Sources (PDB or ModBase) and number of structures used to calculate solvent-accessible surface area. (b) Number of homologous sequences used to calculate evolutionary features. (c) Sources of docked models for calculating docking-based features.
A comparison of (1) imputation and (2) an ensemble of fully-trained classifiers for handling missing data. During training, imputation must fill in gaps in feature coverage, whereas an ensemble trains independent classifiers on each feature-availability scenario. Since structural feature coverage is highly correlated with the existence of known interface residues in training, imputation will fail to predict interface residues outside of regions with structural feature coverage (red). An ensemble will predict interface residues based only on the features available and will not be biased by the missing structural feature.
(a) Training the ECLAIR classifier. (b) Four methods for optimizing machine learning algorithm hyperparameters, showing the order of trials and granularity of hyperparameter sampling spaces for optimizing two hyperparameters. (c) Cross-validation strategy using TPE to optimize hyperparameters and window sizes for both feature selection and ensemble classifier training. (d) Cross-validation results using TPE trials to select top performing feature or set of features (in red) in each feature category. (e) Comparison of four hyperparameter optimization methods’ performance (top panel) and hyperparameter and residue window sampling patterns (bottom panels) on one of the eight sub-classifiers of the ECLAIR ensemble.
(a) Number of residues predicted in each prediction confidence category. (b) Cumulative distribution of interactions with ≥ n residues classified as interface for each of the highest interface potential categories.
(a) Receiver operating characteristic (ROC) curves for each sub-classifier. (b) Precision-recall curves for each sub-classifier. (c) Distribution of raw prediction scores for each sub-classifier. For all panels, sub-classifiers plotted in blue used only sequence-based features; sub-classifiers in red used additional structure-based features. (d) Raw prediction scores compared to actual probabilities of residues in each bin to be at the interface.
Supplementary Figure 7 ROC and precision-recall curves comparing ECLAIR with other popular interface residue prediction methods.
Here, only known surface residues were used in benchmarking all methods. All methods have a slightly lower AUROC (since it is more difficult to distinguish interface from non-interface among only surface residues), however ECLAIR still performs as well or better than all tested methods.
Supplementary Figure 8 Genomic properties of predicted interface residues in interactions lacking structural features.
(a) Enrichment of disease mutations in predicted and known interfaces. (b) Enrichment of recurrent cancer mutations in predicted and known interfaces. (c) Enrichment of rare and common population variants in predicted and known interfaces. (d) Predicted deleteriousness of population variants in known and predicted interfaces (using PolyPhen-2). (e) Predicted effects of population variants in known and predicted interfaces (using EVmutation). (In a-b, significance determined by two-sided Z-test. In d-e, significance determined by a two-sided U-test. n.s. denotes not significant)
(a) Enrichment of disease mutations in predicted and known interfaces. (b) Enrichment of recurrent cancer mutations in predicted and known interfaces. (c) Enrichment of rare and common population variants in predicted and known interfaces. (d) Predicted deleteriousness of population variants in known and predicted interfaces (using PolyPhen-2). (e) Predicted effects of population variants in known and predicted interfaces (using EVmutation). (In a-b, significance determined by two-sided Z-test. In d-e, significance determined by a two-sided U-test)
(a-c) Precision recall curves for interfaces predicted with ECLAIR: (a) interface residues in all benchmarked interactions, (b) interface residues in interactions lacking structural features, and (c) interface domains in interactions lacking structural features. (d) Fraction of interface residues localized to domains for known interface residues in co-crystalized co-bound proteins, predicted interface residues in interactions with structural features, and predicted interface residues in interactions without structural features. (e) Enrichment of human disease mutations in domains determined by known interface residues in co-crystalized co-bound proteins, predicted interface residues in interactions with structural features, and predicted interface residues in interactions without structural features. (Significance determined by two-sided Z-test)
Supplementary Figures 1–11 and Supplementary note 1–7 (PDF 3047 kb)
Comparison of ECLAIR using docking benchmark 4.0 (XLSX 12 kb)
PSI-MI binary evidence codes (XLSX 14 kb)
Training and Testing Sets (XLSX 141 kb)
Feature Selection (XLSX 16 kb)
Full sub-classifier training (XLSX 10 kb)
Comparison of ECLAIR performance with and without co-evolution (XLSX 11 kb)
ECLAIR prediction category performance using docking benchmark 4.0 (XLSX 9 kb)
Initially-trained ECLAIR vs. fully-trained ECLAIR performance (XLSX 11 kb)
ÉCLAIR software (ZIP 127 kb)
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Meyer, M., Beltrán, J., Liang, S. et al. Interactome INSIDER: a structural interactome browser for genomic studies. Nat Methods 15, 107–114 (2018). https://doi.org/10.1038/nmeth.4540
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