Predicting Thermodynamic Stability at Protein G Sites with Deleterious Mutations Using λ-Dynamics with Competitive Screening

Abstract

Free energy predictions are useful in protein design and computer-aided drug design. Alchemical free energy methods are highly accurate, and the alchemical method λ-dynamics significantly improves computational cost. Recent progress made simulations of dozens of perturbations at a single site possible, enabling in silico site-saturation mutagenesis with λ-dynamics. Site-saturation mutagenesis may require increased sampling to characterize many mutations and to accommodate structural disruptions around deleterious mutations. We reintroduce the neglected idea of competitive screening with λ-dynamics to address both issues. Traditional landscape flattening tunes two distinct biases to sample all mutations equally in the folded and unfolded states. Competitive screening transfers the unfolded bias to the folded state so that only reasonable mutations are sampled. Competitive screening is demonstrated on four surface sites and four buried sites in protein G and provides improvements for buried sites. Consequently, competitive screening provides new opportunities for molecular design within larger chemical spaces.