Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis

摘要

Motivation: Accurate predictive models for the impact of single amino acid substitutions on protein stability provide insight into protein structure and function. Such models are also valuable for the design and engineering of new proteins. Previously described methods have utilized properties of protein sequence or structure to predict the free energy change of mutants due to thermal (ΔΔG) and denaturant (ΔΔGH2O) denaturations, as well as mutant thermal stability (ΔT_m), through the application of either computational energy-based approaches or machine learning techniques. However, accuracy associated with applying these methods separately is frequently far from optimal.