Prediction of protein pK _a with representation learning.

ABSTRACT: The behavior of proteins is closely related to the protonation states of the residues. Therefore, prediction and measurement of pK _a are essential to understand the basic functions of proteins. In this work, we develop a new empirical scheme for protein pK _a prediction that is based on deep representation learning. It combines machine learning with atomic environment vector (AEV) and learned quantum mechanical representation from ANI-2x neural network potential (J. Chem. Theory Comput. 2020, 16, 4192). The scheme requires only the coordinate information of a protein as the input and separately estimates the pK _a for all five titratable amino acid types. The accuracy of the approach was analyzed with both cross-validation and an external

SUBMITTER: Gokcan H 

PROVIDER: S-EPMC8864681 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature