Increasing interest in determining the effects of genetic variation for bioengineering, human health and basic biological research has propelled the development of technologies for high-throughput mutagenesis and selection. However, since designing functional assays is challenging and systematic testing of combinations of mutations is intractable, there is a parallel need to develop more accurate computational predictions.. Most computational methods have relied significantly on the signal of evolutionary conservation, but do not account for dependencies between positions in a sequence. We present an unsupervised method for predicting the effects of mutations (EVmutation) that explicitly captures residue dependencies between positions. We find that it improves the prediction accuracies of a comprehensive collection of recent high-throughput experimental fitness landscapes, biochemical measurements and human disease mutations. We suggest EVmutation may be useful to assess the quantitative effects of mutations in genes of any organism and provide precomputed predictions for ~ 7000 human proteins.

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.