蛋白质p<i>K</i><sub>a</sub>预测模型研究进展

罗方芳; 蔡志涛; 黄艳东

doi:10.7498/aps.72.20231356

摘要

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

关键词:

Abstract

The pH value represents the acidity of the solution and plays a key role in many life events linked to human diseases. For instance, the β-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 affinities of protons to ionizable key residues, namely $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ values, which determine the deprotonation equilibria under a pH condition. However, wet-lab experiments are often expensive and time consuming. In some cases, owing to the structural complexity of a protein, $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ measurements become difficult, making theoretical $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ predictions in a dry laboratory more advantageous. In the past thirty years, many efforts have been made to accurately and fast predict protein $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ 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. 12 5411) 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. To accelerate $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ prediction, Poisson-Boltzmann (PB) or empirical equation-based schemes, such as H++ and PropKa, have been developed and widely used where $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ values are obtained via one-structure calculations. Recently, artificial intelligence (AI) is applied to the area of protein $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ prediction, which leads to the development of DeepKa by Huang laboratory (2021 ACS Omega 6 34823), the first AI-driven $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ predictor. In this paper, we review the advances in protein $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ prediction contributed mainly by CpHMD methods, PB or empirical equation-based schemes, and AI models. Notably, the modeling hypotheses explained in the review would shed light on future development of more powerful protein $ {\mathrm{p}}{K}_{{\mathrm{a}}} $ predictors.

Keywords:

molecular dynamics /
Poisson-Boltzmann equation /
machine learning /
$ {\mathrm{p}}{K}_{{\mathrm{a}}} $ prediction

作者及机构信息

集美大学计算机工程学院, 厦门　361021

通信作者: 黄艳东, yandonghuang@jmu.edu.cn

基金项目: 国家自然科学基金(批准号: 11804114, 62006096)、福建省自然科学基金(批准号: 2023J01329, 2020J05146)、厦门市自然科学基金(批准号: 3502Z20227205)和集美大学校启动金(批准号: ZQ2020027)资助的课题.

Authors and contacts

College of Computer Engineering, Jimei University, Xiamen 361021, China

Corresponding author: Huang Yan-Dong, yandonghuang@jmu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11804114, 62006096), the Natural Science Foundation of Fujian Province, China (Grant Nos. 2023J01329, 2020J05146), the Natural Science Foundation of Xiamen, China (Grant No. 3502Z20227205), and the Scientific Starting Research Foundation of Jimei University, China (Grant No. ZQ2020027).

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施引文献

图 1 BACE1催化中心质子化态和功能的关系　(a) BACE1三维结构及其催化中心酸性二分体D32和D228; (b) D32和D228质子化态和蛋白质活性随pH的变化规律(D是Asp的缩写)

Fig. 1. Relationship between protonation state of BACE1 catalytic center and the function: (a) Crystal structure of BACE1 and the acidic dyad in the catalytic center; (b) protonation states of D32 and D228 and the activity as a function of pH (D is the abbreviation of Asp).

下载: 全尺寸图片幻灯片

图 2 CpHMD模拟框架

Fig. 2. Framework of a CpHMD simulation.

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图 3 互变异构滴定模型的3个质子化态以及状态间的转化　(a)天冬氨酸Asp; (b)组氨酸His

Fig. 3. Three protonation states and their interconversion in the tautomeric titration model: (a) Aspartic acid; (b) histidine.

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图 4 基于C-CpHMD的$ {\mathrm{p}}{K}_{{\mathrm{a}}} $计算　(a)滴定坐标λ和去质子化概率S的轨迹; (b)采用Hill函数拟合S

Fig. 4. The $ \text{p}{{{K}}}_{{\mathrm{a}}} $ calculation based on C-CpHMD: (a) Trajectories of titration coordinate λ and deprotonation fraction S; (b) fitting S to Hill function.

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图 5 相对去质子化自由能计算的热力学循环

Fig. 5. Thermodynamic cycle of relative deprotonation free energy calculation.

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图 6 $ \text{p}{K}_{{\mathrm{a}}} $预测模型性能对比

Fig. 6. Comparison of existing $ \text{p}{K}_{{\mathrm{a}}} $ predictors.

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map

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