Protein pK(a) Prediction with Machine Learning

[Image: see text] Protein pK(a) prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pK(a) predictor DeepKa, which is trained and validated with the pK(a) values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed ( Y. HuangJ. Chem. Inf. Model.2018, 58, 1372−138329949356). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pK(a) predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pK(a) predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein–ligand binding affinity prediction.