A MODEL RECOGNITION APPROACH TO THE PREDICTION OF ALL-HELICAL MEMBRANE-PROTEIN STRUCTURE AND TOPOLOGY
A MODEL RECOGNITION APPROACH TO THE PREDICTION OF ALL-HELICAL MEMBRANE-PROTEIN STRUCTURE AND TOPOLOGY
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DOI：
10.1021/bi00176a037
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发表时间：
1994-03-15
期刊：
影响因子：
2.9
通讯作者：
THORTON, JM
中科院分区：
文献类型：
--
作者：
JONES, DT;TAYLOR, WR;THORTON, JM
本文提出了一种基于拓扑模型识别的膜蛋白二级结构和拓扑预测的新方法。该方法采用了一组统计表（对数似然）编译良好的特征膜蛋白质数据，和一种新的动态规划算法来识别膜拓扑模型的期望最大化。统计表显示了对细胞膜内部、中间和外部的某些氨基酸种类的明确偏倚。使用一组83个完整的膜蛋白序列，从各种细菌，植物和动物物种，和一个严格的折刀程序，其中每个蛋白质（沿着任何可检测的同源物）从用于在预测之前计算表的训练集中去除，该方法成功地预测了83个拓扑中的64个，在37个复杂多生成拓扑中，34个被正确预测。
This paper describes a new method for the prediction of the secondary structure and topology of integral membrane proteins based on the recognition of topological models. The method employs a set of statistical tables (log likelihoods) compiled from well-characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization. The statistical tables show definite biases toward certain amino acid species on the inside, middle, and outside of a cellular membrane. Using a set of 83 integral membrane protein sequences taken from a variety of bacterial, plant, and animal species, and a strict jackknifing procedure, where each protein (along with any detectable homologues) is removed from the training set used to calculate the tables before prediction, the method successfully predicted 64 of the 83 topologies, and of the 37 complex multispanning topologies 34 were predicted correctly.