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具有二体和四体统计势的晶格蛋白质折叠

Lattice protein folding with two and four-body statistical potentials.

作者信息

Gan H H, Tropsha A, Schlick T

机构信息

Department of Chemistry and Courant Institute of Mathematical Sciences, New York University and the Howard Hughes Medical Institute, 251 Mercer Street, New York, NY 10012, USA.

出版信息

Proteins. 2001 May 1;43(2):161-74. doi: 10.1002/1097-0134(20010501)43:2<161::aid-prot1028>3.0.co;2-f.

Abstract

The cooperative folding of proteins implies a description by multibody potentials. Such multibody potentials can be generalized from common two-body statistical potentials through a relation to probability distributions of residue clusters via the Boltzmann condition. In this exploratory study, we compare a four-body statistical potential, defined by the Delaunay tessellation of protein structures, to the Miyazawa-Jernigan (MJ) potential for protein structure prediction, using a lattice chain growth algorithm. We use the four-body potential as a discriminatory function for conformational ensembles generated with the MJ potential and examine performance on a set of 22 proteins of 30-76 residues in length. We find that the four-body potential yields comparable results to the two-body MJ potential, namely, an average coordinate root-mean-square deviation (cRMSD) value of 8 A for the lowest energy configurations of all-alpha proteins, and somewhat poorer cRMSD values for other protein classes. For both two and four-body potentials, superpositions of some predicted and native structures show a rough overall agreement. Formulating the four-body potential using larger data sets and direct, but costly, generation of conformational ensembles with multibody potentials may offer further improvements. Proteins 2001;43:161-174.

摘要

蛋白质的协同折叠意味着用多体势来描述。这种多体势可以通过玻尔兹曼条件与残基簇的概率分布建立联系,从常见的二体统计势推广而来。在这项探索性研究中,我们使用晶格链生长算法,将由蛋白质结构的德劳内三角剖分定义的四体统计势与宫泽-杰尔尼根(MJ)势进行比较,用于蛋白质结构预测。我们将四体势用作由MJ势生成的构象集合的判别函数,并在一组长度为30 - 76个残基的22种蛋白质上检验性能。我们发现四体势产生的结果与二体MJ势相当,即对于全α蛋白的最低能量构型,平均坐标均方根偏差(cRMSD)值为8 Å,而对于其他蛋白质类别,cRMSD值略差。对于二体和四体势,一些预测结构与天然结构的叠加显示出大致的总体一致性。使用更大的数据集来构建四体势,并直接但成本高昂地用多体势生成构象集合,可能会带来进一步的改进。《蛋白质》2001年;43卷:161 - 174页。

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