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用于预测整合膜蛋白中膜埋螺旋的偏好函数。

Preference functions for prediction of membrane-buried helices in integral membrane proteins.

作者信息

Juretić D, Zucić D, Lucić B, Trinajstić N

机构信息

Physics Department, Faculty of Science and Education, University of Split, Croatia.

出版信息

Comput Chem. 1998 Jun 20;22(4):279-94. doi: 10.1016/s0097-8485(97)00070-3.

Abstract

The preference functions method is described for prediction of membrane-buried helices in membrane proteins. Preference for the alpha-helix conformation of amino acid residue in a sequence is a non-linear function of average hydrophobicity of its sequence neighbors. Kyte-Doolittle hydropathy values are used to extract preference functions from a training data set of integral membrane proteins of partially known secondary structure. Preference functions for beta-sheet, turn and undefined conformation are also extracted by including beta-class soluble proteins of known structure in the training data set. Conformational preferences are compared in tested sequence for each residue and predicted secondary structure is associated with the highest preference. This procedure is incorporated in an algorithm that performs accurate prediction of transmembrane helical segments. Correct sequence location and secondary structure of transmembrane segments is predicted for 20 of 21 reference membrane polypeptides with known crystal structure that were not included in the training data set. Comparison with hydrophobicity plots revealed that our preference profiles are more accurate and exhibit higher resolution and less noise. Shorter unstable or movable membrane-buried alpha-helices are also predicted to exist in different membrane proteins with transport function. For instance, in the sequence of voltage-gated ion channels and glutamate receptors, N-terminal parts of known P-segments can be located as characteristic alpha-helix preference peaks. Our e-mail server: predict@drava.etfos.hr, returns a preference profile and secondary structure prediction for a suspected or known membrane protein when its sequence is submitted.

摘要

本文描述了一种预测膜蛋白中膜埋螺旋的偏好函数方法。序列中氨基酸残基对α螺旋构象的偏好是其序列邻域平均疏水性的非线性函数。利用Kyte-Doolittle亲水性值从部分已知二级结构的整合膜蛋白训练数据集中提取偏好函数。通过在训练数据集中纳入已知结构的β类可溶性蛋白,也提取了β折叠、转角和未定义构象的偏好函数。比较测试序列中每个残基的构象偏好,并将预测的二级结构与最高偏好相关联。该过程被纳入一个算法中,该算法可对跨膜螺旋片段进行准确预测。对于21个已知晶体结构且未包含在训练数据集中的参考膜多肽中的20个,预测了跨膜片段的正确序列位置和二级结构。与疏水性图谱的比较表明,我们的偏好图谱更准确,分辨率更高,噪声更小。还预测在具有转运功能的不同膜蛋白中存在较短的不稳定或可移动的膜埋α螺旋。例如,在电压门控离子通道和谷氨酸受体的序列中,已知P段的N端部分可定位为特征性的α螺旋偏好峰。我们的电子邮件服务器:predict@drava.etfos.hr,当提交可疑或已知膜蛋白的序列时,会返回其偏好图谱和二级结构预测结果。

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