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Computational methods are needed to differentiate the small fraction ofmissense mutations that contribute to disease by disrupting protein functionfrom neutral variants. We describe several complementary methods usinglarge-scale homology modeling of human protein complexes to detect non-neutralmutations. Importantly, unlike sequence conservation-based methods, thisstructure-based approach provides experimentally testable biochemicalmechanisms for mutations in disease. Specifically, we infer metal ion, smallmolecule, protein-protein, and nucleic acid binding sites by homology and findthat disease-associated missense mutations are more prevalent in each class ofbinding site than are neutral mutations. Importantly, our approach identifiesconsiderably more binding sites than those annotated in the RefSeq database.Furthermore, an analysis of metal ion and protein-protein binding sitespredicted by machine learning shows a similar preponderance ofdisease-associated mutations in these sites. We also derive a statistical scorefor predicting how mutations affect metal ion binding and find many dbSNPmutations that likely disrupt ion binding but were not previously considereddeleterious. We also cluster mutations in the protein structure to discoverputative functional regions. Finally, we develop a machine learning predictorfor detecting disease-associated missense mutations and show that itoutperforms two other prediction methods on an independent test set.

Predicting non-neutral missense mutations and their biochemical consequences using genome-scale homology modeling of human protein complexes