Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

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 7 model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit similar functional properties as 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 upload their own set of mutations for this purpose.

SUMMARY Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ~50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.

Understanding the functional relevance of DNA variants is essential for all exome and genome sequencing projects. However, current mutagenesis cloning protocols require Sanger sequencing, and thus are prohibitively costly and labor-intensive. We describe a massively-parallel site-directed mutagenesis approach, “Clone-seq”, leveraging next-generation sequencing to rapidly and cost-effectively generate a large number of mutant alleles. Using Clone-seq, we further develop a comparative interactome-scanning pipeline integrating high-throughput GFP, yeast two-hybrid (Y2H), and mass spectrometry assays to systematically evaluate the functional impact of mutations on protein stability and interactions. We use this pipeline to show that disease mutations on protein-protein interaction interfaces are significantly more likely than those away from interfaces to disrupt corresponding interactions. We also find that mutation pairs with similar molecular phenotypes in terms of both protein stability and interactions are significantly more likely to cause the same disease than those with different molecular phenotypes, validating the in vivo biological relevance of our high-throughput GFP and Y2H assays, and indicating that both assays can be used to determine candidate disease mutations in the future. The general scheme of our experimental pipeline can be readily expanded to other types of interactome-mapping methods to comprehensively evaluate the functional relevance of all DNA variants, including those in non-coding regions.

Identifying disease-associated missense mutations remains a challenge, especially in large-scale sequencing studies. Here we establish an experimentally and computationally integrated approach to investigate the functional impact of missense mutations in the context of the human interactome network and test our approach by analyzing ~2,000 de novo missense mutations found in autism subjects and their unaffected siblings. Interaction-disrupting de novo missense mutations are more common in autism probands, principally affect hub proteins, and disrupt a significantly higher fraction of hub interactions than in unaffected siblings. Moreover, they tend to disrupt interactions involving genes previously implicated in autism, providing complementary evidence that strengthens previously identified associations and enhances the discovery of new ones. Importantly, by analyzing de novo missense mutation data from six disorders, we demonstrate that our interactome perturbation approach offers a generalizable framework for identifying and prioritizing missense mutations that contribute to the risk of human disease.

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual’s genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

With the rapid growth of structural genomics, numerous protein crystal structures have become available. However, the parallel increase in knowledge of the functional principles underlying biological processes, and more specifically the underlying molecular mechanisms of disease, has been less dramatic. This notwithstanding, the study of complex cellular networks has made possible the inference of protein functions on a large scale. Here, we combine the scale of network systems biology with the resolution of traditional structural biology to generate a large-scale atomic-resolution interactome-network comprising 3,398 interactions between 2,890 proteins with a well-defined interaction interface and interface residues for each interaction. Within the framework of this atomic-resolution network, we have explored the structural principles underlying variations causing human-inherited disease. We find that in-frame pathogenic variations are enriched at both the interface and in the interacting domain, suggesting that variations not only at interface “hot-spots,” but in the entire interacting domain can result in alterations of interactions. Further, the sites of pathogenic variations are closely related to the biophysical strength of the interactions they perturb. Finally, we show that biochemical alterations consequent to these variations are considerably more disruptive than evolutionary changes, with the most significant alterations at the protein interaction interface.