No abstract

A mutation to the amino acid sequence of a protein can cause a biomolecule to be resistant to the intended effects of a drug. Assessing the changes of a drug’s efficacy in response to mutations via mutagenesis wet-lab experiments is prohibitively time consuming for even a single point mutation, let alone for all possible mutations. Existing approaches for inferring mutation-induced drug resistance are available, but all of them reason about mutations of residues at or very near the protein-drug interface. However, there are examples of mutations far away from the region where the ligand binds, but which nonetheless render a protein resistant to the effects of the drug. We present a proof-of-concept computational pipeline that generates in silico the set of all possible single point mutations in a protein-ligand complex. We assess drug resistance using a graph theoretic rigidity analysis approach. Unlike existing methods, we are able to assess the impact of mutations far away from the protein-drug interface. We introduce several visualizations for exploring how amino acid substitutions both near and far away from where the ligand interacts with a protein target have a stabilizing or destabilizing effect on the protein-drug complex. We discuss our analytical approach in the context of experimental data from the literature about clinically known protein-drug interactions.