Prediction of quantitative function of artificially-designed protein from structural information

Abstract

In protein engineering, introducing mutations into natural proteins is a common strategy to enhance desired properties. However, comprehensive evaluation of all potential mutations through large-scale screening is prohibitively expensive. This study proposes a method that leverages small-scale mutant data obtained during initial experiments to predict the performance of unknown mutations and efficiently identify beneficial mutation sites. The method is based on the concept that protein performance fundamentally depends on its three-dimensional structure. Specifically, the proposed approach involves: (1) predicting the 3D structures of mutant proteins using AlphaFold2, followed by energy minimization; (2) applying molecular field mapping to the optimized structures to extract fixed-length vectors that retain physicochemical features; (3) constructing performance prediction models using Partial Least Squares (PLS) regression; and (4) analyzing regression coefficients to visualize key regions contributing to performance. When applied to publicly available channelrhodopsin data used in optogenetics, the method achieved high prediction accuracy and successfully identified mutation candidates that improve performance.