南京大学学报（自然科学版）2023，Vol.59Issue(6)：919-927,9.DOI:10.13232/j.cnki.jnju.2023.06.002
机器学习在蛋白质疏水相互作用模型研究中的应用
Application of machine learning in the study of the hydrophobic interaction model of proteins
摘要
Abstract
Hydrophobic interaction is a nonlinear effective interaction with a highly complex many-body feature.This interaction plays a dominant role in protein folding.The solvent-accessible surface area(SASA)of proteins is a typical means to characterize this interaction.To solve the imbalance between computational cost and accuracy in the analytical or numerical methods of the SASA,in this work,we apply the machine learning method to the prediction of protein SASA.Compared with the traditional typical methods,the error is roughly one order smaller,and the calculation speed is nearly two orders faster.In addition,we extend this method to predict the SASA of proteins based on coarse-grained structures.Good predictions are also achieved.These results provide new efficient computational tools for the study of protein physics.关键词
蛋白质折叠/疏水相互作用/溶剂可及表面积(SASA)/机器学习Key words
protein folding/hydrophobic interaction/solvent-accessible surface area(SASA)/machine learning分类
生物科学引用本文复制引用
冯晨博,马维强,程润,王骏..机器学习在蛋白质疏水相互作用模型研究中的应用[J].南京大学学报（自然科学版）,2023,59(6):919-927,9.基金项目
国家自然科学基金(11774157,11934008) （11774157,11934008）
