亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein

力场(虚构) 分子动力学 分子内力 计算机科学 人工神经网络 蛮力 化学 计算化学 人工智能 计算机安全 立体化学
作者
Pan Zhang,Weitao Yang
出处
期刊:Journal of Chemical Physics [American Institute of Physics]
卷期号:159 (2) 被引量:4
标识
DOI:10.1063/5.0142280
摘要

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
fukase发布了新的文献求助10
2秒前
jiangjiang完成签到 ,获得积分10
10秒前
17秒前
科研通AI2S应助科研通管家采纳,获得10
19秒前
tsttst完成签到,获得积分10
36秒前
41秒前
50秒前
55秒前
Qiuyajing完成签到,获得积分10
1分钟前
1分钟前
星辰大海应助兴奋的嘉懿采纳,获得10
1分钟前
祖之微笑发布了新的文献求助10
1分钟前
兴奋的嘉懿完成签到,获得积分20
1分钟前
量子星尘发布了新的文献求助10
1分钟前
1分钟前
1分钟前
1分钟前
1分钟前
葱饼完成签到 ,获得积分10
2分钟前
香蕉觅云应助sunshihaoya采纳,获得10
2分钟前
2分钟前
2分钟前
2分钟前
量子星尘发布了新的文献求助10
3分钟前
机灵自中发布了新的文献求助10
3分钟前
机灵自中完成签到,获得积分10
3分钟前
执着的草丛完成签到,获得积分10
3分钟前
3分钟前
Anna完成签到 ,获得积分10
3分钟前
4分钟前
绝世冰淇淋完成签到 ,获得积分10
4分钟前
科研通AI5应助科研通管家采纳,获得50
4分钟前
breeze完成签到,获得积分10
4分钟前
量子星尘发布了新的文献求助10
4分钟前
4分钟前
顺心蜜粉给远航的求助进行了留言
4分钟前
silence发布了新的文献求助10
4分钟前
zhanglq发布了新的文献求助10
4分钟前
silence完成签到,获得积分10
4分钟前
CipherSage应助zhaoaotao采纳,获得10
4分钟前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3976649
求助须知:如何正确求助?哪些是违规求助? 3520756
关于积分的说明 11204743
捐赠科研通 3257502
什么是DOI,文献DOI怎么找? 1798733
邀请新用户注册赠送积分活动 877897
科研通“疑难数据库(出版商)”最低求助积分说明 806629