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用于预测膜蛋白中螺旋结构的构象偏好函数。

Conformational preference functions for predicting helices in membrane proteins.

作者信息

Juretić D, Lee B, Trinajstić N, Williams R W

机构信息

Natural Sciences and Arts Department, University of Split, Croatia.

出版信息

Biopolymers. 1993 Feb;33(2):255-73. doi: 10.1002/bip.360330208.

Abstract

A suite of FORTRAN programs, PREF, is described for calculating preference functions from the data base of known protein structures and for comparing smoothed profiles of sequence-dependent preferences in proteins of unknown structure. Amino acid preferences for a secondary structure are considered as functions of a sequence environment. Sequence environment of amino acid residue in a protein is defined as an average over some physical, chemical, or statistical property of its primary structure neighbors. The frequency distribution of sequence environments in the data base of soluble protein structures is approximately normal for each amino acid type of known secondary conformation. An analytical expression for the dependence of preferences on sequence environment is obtained after each frequency distribution is replaced by corresponding Gaussian function. The preference for the alpha-helical conformation increases for each amino acid type with the increase of sequence environment of buried solvent-accessible surface areas. We show that a set of preference functions based on buried surface area is useful for predicting folding motifs in alpha-class proteins and in integral membrane proteins. The prediction accuracy for helical residues is 79% for 5 integral membrane proteins and 74% for 11 alpha-class soluble proteins. Most residues found in transmembrane segments of membrane proteins with known alpha-helical structure are predicted to be indeed in the helical conformation because of very high middle helix preferences. Both extramembrane and transmembrane helices in the photosynthetic reaction center M and L subunits are correctly predicted. We point out in the discussion that our method of conformational preference functions can identify what physical properties of the amino acids are important in the formation of particular secondary structure elements.

摘要

描述了一套FORTRAN程序PREF,用于从已知蛋白质结构的数据库中计算偏好函数,并比较未知结构蛋白质中序列依赖性偏好的平滑概况。二级结构的氨基酸偏好被视为序列环境的函数。蛋白质中氨基酸残基的序列环境被定义为其一级结构邻居的某些物理、化学或统计特性的平均值。对于已知二级构象的每种氨基酸类型,可溶性蛋白质结构数据库中序列环境的频率分布近似呈正态分布。在将每个频率分布替换为相应的高斯函数后,得到了偏好对序列环境依赖性的解析表达式。随着埋藏溶剂可及表面积序列环境的增加,每种氨基酸类型对α-螺旋构象的偏好都会增加。我们表明,基于埋藏表面积的一组偏好函数可用于预测α类蛋白质和整合膜蛋白中的折叠基序。对于5种整合膜蛋白,螺旋残基的预测准确率为79%,对于11种α类可溶性蛋白,预测准确率为74%。由于中间螺旋偏好性非常高,在具有已知α-螺旋结构的膜蛋白跨膜区段中发现的大多数残基预计确实处于螺旋构象。光合反应中心M和L亚基中的膜外螺旋和跨膜螺旋均被正确预测。我们在讨论中指出,我们的构象偏好函数方法可以确定氨基酸的哪些物理性质在特定二级结构元件的形成中很重要。

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