讲座主题：Protein pKa prediction with machine learning

讲座时间：2021年11月25日下午15：00-16：30

讲座地点：腾讯会议  会议号465203842

参加人员：科研人员、研究生

主讲人：黄艳东  主持人：兰海

讲座主要内容：Protein pKa prediction is essential for the investigation of pH-associated relationship between protein structure and function. In this work, we introduce a deep learning based protein pKa predictor DeepKa, which is trained and validated with the pKa 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 (Huang, Harris, and Shen J. Chem. Inf. Model. 2018, 58, 1372-1383). 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 pKa predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning based protein pKa predictors in future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction.

主讲人简介：黄艳东，副教授，2017年入职集美大学计算机工程学院，主要研究方向为分子动力学方法和软件的开发以及新模拟技术在揭示质子耦联蛋白分子机制和蛋白质pKa预测等方面的应用。2013年于厦门大学物理系获得博士学位，2013-3017在马里兰大学药学院从事博士后研究工作。同时参与了两款国际顶尖分子动力学软件Amber和CHARMM的研发，其中作为主要贡献人参与Amber软件2018至2019版本的开发。近5年在Nature Communications，PNAS，ACS Catalysis等期刊发表论文10篇，平均影响因子7.5。在著名系列丛书《Methods in Molecular Biology 》第2302卷《Structure and Function of Membrane Proteins》贡献一个章节。