蛋白质pK_(a)预测模型研究进展

Progress in protein pK_(a)prediction

pH表征溶液的酸碱性,是许多与人类重大疾病密切相关的生命活动的调控因子.pK_(a)决定可滴定基团在一定pH条件下的去质子化平衡,是研究pH调控的生物化学过程的重要参量.然而,由于蛋白质结构的复杂性以及实验条件的限制,蛋白质pK_(a)通常需要借助理论预测.近30年,研究者们开发了各种基于先验知识的pK_(a)预测模型.随着近几年人工智能技术的快速发展,人们开始尝试将人工智能算法应用于蛋白质pK_(a)预测工具的开发.本文介绍pK_(a)理论预测近年来的一些重要研究进展,主要包括恒定pH分子动力学以及基于泊松-玻尔兹曼方程、经验函数和机器学习的pK_(a)预测模型.在此基础上,讨论蛋白质pK_(a)预测模型的未来发展方向和应用前景.

The pH value represents the acidity of the solution and plays a key role in many life events linked to human diseases.For instance,theβ-site amyloid precursor protein cleavage enzyme,BACE1,which is a major therapeutic target of treating Alzheimer’s disease,functions within a narrow pH region around 4.5.In addition,the sodium-proton antiporter NhaA from Escherichia coli is activated only when the cytoplasmic pH is higher than 6.5 and the activity reaches a maximum value around pH 8.8.To explore the molecular mechanism of a protein regulated by pH,it is important to measure,typically by nuclear magnetic resonance,the binding pK_(a)affinities of protons to ionizable key residues,namely values,which determine the deprotonation equilibria under a pH condition.However,wet-lab experiments are often expensive and time consuming.In some cases,pK_(a)pK_(a)owing to the structural complexity of a protein,measurements become difficult,making theoretical predictions in a dry laboratory more advantageous.In the past thirty years,many efforts have been made to pK_(a)accurately and fast predict protein with physics-based methods.Theoretically,constant pH molecular dynamics(CpHMD)method that takes conformational fluctuations into account gives the most accurate predictions,especially the explicit-solvent CpHMD model proposed by Huang and coworkers(2016 J.Chem.Theory Comput.125411)which in principle is applicable to any system that can be described by a force field.However,lengthy molecular simulations are usually necessary for the extensive sampling of conformation.In particular,the computational complexity increases significantly if water molecules are included explicitly in the simulation system.Thus,CpHMD is not suitable for high-throughout computing requested in industry circle.pK_(a)To accelerate prediction,Poisson-Boltzmann(PB)or empirical equation-based schemes,such as H++and pK_(a)PropK_(a),have been developed and widely used where values are obtained via one-structure calculations.pK_(a)Recently,artificial intelligence(AI)is applied to the area of protein prediction,which leads to the pK_(a)development of DeepK_(a)by Huang laboratory(2021 ACS Omega 634823),the first AI-driven predictor.In pK_(a)this paper,we review the advances in protein prediction contributed mainly by CpHMD methods,PB or empirical equation-based schemes,and AI models.Notably,the modeling hypotheses explained in the review pK_(a)would shed light on future development of more powerful protein predictors.