基于泊松-波尔兹曼机器学习（PBML）的静电分析模型。

IF 3.2 3区生物学 Q2 BIOPHYSICS

Biophysical journal Pub Date : 2024-09-03 Epub Date: 2024-02-15 DOI:10.1016/j.bpj.2024.02.008

Jiahui Chen, Yongjia Xu, Xin Yang, Zixuan Cang, Weihua Geng, Guo-Wei Wei

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引用次数: 0

摘要

静电学对化学、物理学、生物学和医学至关重要。泊松-波尔兹曼（PB）理论是静电分析的主要模型。然而，由于与 PB 方程相关的非线性、介电跃迁、电荷奇异性和几何复杂性，计算精确的大分子 PB 静电溶解自由能具有很高的挑战性。本研究为生物分子静电分析引入了基于 PB 的机器学习（PBML）模型。经过二阶精确 MIBPB 求解器的训练，发现所提出的 PBML 模型在静电分析中比几种著名的 PB 求解器更精确、更快速。提出的 PBML 模型可以为新的生物大分子或分子动力学产生的新构象提供高精度的 PB 静电溶解自由能，而且计算成本大大降低。

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Poisson-Boltzmann-based machine learning model for electrostatic analysis.

Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation free energies for macromolecules due to the nonlinearity, dielectric jumps, charge singularity, and geometric complexity associated with the PB equation. The present work introduces a PB-based machine learning (PBML) model for biomolecular electrostatic analysis. Trained with the second-order accurate MIBPB solver, the proposed PBML model is found to be more accurate and faster than several eminent PB solvers in electrostatic analysis. The proposed PBML model can provide highly accurate PB electrostatic solvation free energy of new biomolecules or new conformations generated by molecular dynamics with much reduced computational cost.

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来源期刊

Biophysical journal 生物-生物物理

CiteScore

6.10

自引率

5.90%

发文量

3090

审稿时长

2 months

期刊介绍： BJ publishes original articles, letters, and perspectives on important problems in modern biophysics. The papers should be written so as to be of interest to a broad community of biophysicists. BJ welcomes experimental studies that employ quantitative physical approaches for the study of biological systems, including or spanning scales from molecule to whole organism. Experimental studies of a purely descriptive or phenomenological nature, with no theoretical or mechanistic underpinning, are not appropriate for publication in BJ. Theoretical studies should offer new insights into the understanding ofexperimental results or suggest new experimentally testable hypotheses. Articles reporting significant methodological or technological advances, which have potential to open new areas of biophysical investigation, are also suitable for publication in BJ. Papers describing improvements in accuracy or speed of existing methods or extra detail within methods described previously are not suitable for BJ.