  Computational methods are needed to differentiate the small fraction of
missense mutations that contribute to disease by disrupting protein function
from neutral variants. We describe several complementary methods using
large-scale homology modeling of human protein complexes to detect non-neutral
mutations. Importantly, unlike sequence conservation-based methods, this
structure-based approach provides experimentally testable biochemical
mechanisms for mutations in disease. Specifically, we infer metal ion, small
molecule, protein-protein, and nucleic acid binding sites by homology and find
that disease-associated missense mutations are more prevalent in each class of
binding site than are neutral mutations. Importantly, our approach identifies
considerably more binding sites than those annotated in the RefSeq database.
Furthermore, an analysis of metal ion and protein-protein binding sites
predicted by machine learning shows a similar preponderance of
disease-associated mutations in these sites. We also derive a statistical score
for predicting how mutations affect metal ion binding and find many dbSNP
mutations that likely disrupt ion binding but were not previously considered
deleterious. We also cluster mutations in the protein structure to discover
putative functional regions. Finally, we develop a machine learning predictor
for detecting disease-associated missense mutations and show that it
outperforms two other prediction methods on an independent test set.
