Protein-protein interactions facilitate much of known cellular function. While simply knowing which proteins interact with each other provides valuable information to spur functional studies, far more specific hypotheses can be tested if the spatial contacts of interacting proteins are known. However, co-crystal structures and homology models cover only ~10% of all known human interactions. To solve this issue, we developed ECLAIR (Ensemble Classifier Learning Algorithm to predict Interface Residues), a unified machine learning framework that we used to create the first multi-scale whole-proteome 3D structural interactome in human for all experimentally-determined binary interactions reported in major databases. Finally, we demonstrate that our 3D interactome approach offers a generalizable interactome-based framework for prioritizing missense mutations that contribute risk to human disease and for understanding their mechanisms at the molecular level by analyzing 2,821 de novo missense mutations identified from whole-exome-sequencing of ~2,500 families from the Simons Simplex Collection. We find that Interaction-disrupting de novo missense mutations are more common in autism probands, these mutations principally affect hub proteins, and importantly, protein harboring these mutations in male probands are significantly more likely to be known ASD genes, confirming the effectiveness of our framework .

Dissect global dynamics of protein interactome across human populations and in disease

ACS Spring Meeting 2021

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.