On Correlation between Structural Properties and Viral Escape Measurements from Deep Mutational Scanning

Abstract

Encouraged by recent efforts to map responses of SARS-CoV-2 mutations to various antibody treatments with deep mutational scanning, we explored the possibility of tying measurable structural contact information from the binding complexes of antibodies and their targets to experimentally determined viral escape responses. With just a single crystal structure for each binding complex, we find that the average correlation coefficient R is surprisingly high at 0.76. Our two methods for calculating contact information use binary contacts measured between all residues of two proteins. By varying the parameters to obtain binary contacts, we find that 3.6 [A] and 7 [A] are pivotal distances to toggle the binary step function when tallying the contacts for each method. The correlations are improved by short simulations ([~]25 ns), which increase average R to 0.78. With blind tests using the random forest model, we can further improve average R to 0.84. These easy-to-implement measurements can be utilized in computational screening of viral mutations that escape antibody treatments and potentially other protein-protein interaction problems.