Identifying amino acids sensitive to mutations using high-throughput rigidity analysis

Understanding how an amino acid substitution affects a protein's stability can aid in the design of pharmaceutical drugs that aim to counter the deleterious effects caused by protein mutants. Unfortunately, performing mutation experiments on the physical protein is both time and cost prohibitive. Thus an exhaustive analysis which includes systematically mutating all amino acids in the physical protein is infeasible. Computational methods have been developed over the years to predict the effects of mutations, but even many of them are computationally intensive else are dependent on homology or experimental data that may not be available for the protein being studied. In this work we motivate and present a computation pipeline whose only input is a Protein Data Bank file containing the 3D coordinates of the atoms of a biomolecule. Our high-throughput approach uses our rMutant algorithm to exhaustively generate in silico mutants with amino acid substitutions to Glycine, Alanine, and Serine for all residues in a protein. We exploit the speed of a fast rigidity analysis approach to analyze our protein variants, and develop a Mutation Sensitivity (MuSe) Map to identify residues that are most sensitive to mutations. We present three case studies and show the degree to which a MuSe Map is able to identify those amino acids which are susceptible to the effects of mutations.