  Modern biomedicine is challenged to predict the effects of genetic variation.
Systematic functional assays of point mutants of proteins have provided
valuable empirical information, but vast regions of sequence space remain
unexplored. Fortunately, the mutation-selection process of natural evolution
has recorded rich information in the diversity of natural protein sequences.
Here, building on probabilistic models for correlated amino-acid substitutions
that have been successfully applied to determine the three-dimensional
structures of proteins, we present a statistical approach for quantifying the
contribution of residues and their interactions to protein function, using a
statistical energy, the evolutionary Hamiltonian. We find that these
probability models predict the experimental effects of mutations with
reasonable accuracy for a number of proteins, especially where the selective
pressure is similar to the evolutionary pressure on the protein, such as
antibiotics.
