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Residue co-evolution helps predict interaction sites in alpha-helical membrane proteins

机译:残留物共同有助于预测α-螺旋膜蛋白中的相互作用位点

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Many integral membrane proteins, just like their globular counterparts, form either transient or permanent multi -subunit complexes to fulfill specific cellular roles. Although numerous interactions between these proteins have been experientially determined, the structural coverage of the complexes is very low. Therefore, the computational identification of the amino acid residues involved in the interaction interfaces is a crucial step towards the functional annotation of all membrane proteins. Here, we present MBPred, a sequence-based method for predicting the interface residues in transmembrane proteins. An unique feature of our method is that it contains separate random forest models for two different use cases: (a) when the location of transmembrane regions is precisely known from a crystal structure, and (b) when it is predicted from sequence. In stark contrast to the aqueous-exposed protein segments, we found that the interaction sites located in the membrane are not enriched for evolutionary conservation, most likely due to their restricted amino acid composition or their random distribution among buried and exposed residues. On the other hand, residue co-evolution proved to be a very informative feature which has not so far been used for predicting interaction sites in individual proteins. MBPred reaches AUC, precision and recall values of 0.79/0.73, 0.69/0.51 and 0.55/0.48 on the cross -validation and independent test dataset, respectively, thus outperforming the previously published method of Bordner as well as all methods trained on globular proteins. Moreover, we show that for the majority of complete interface patches, the method captures more than 50% of the involved residues.
机译:许多整体膜蛋白,就像它们的球状对应物,形成瞬态或永久性多轴轴复合物以满足特定的细胞角色。虽然这些蛋白质之间的许多相互作用已经进行了经验证实确定,但复合物的结构覆盖率非常低。因此,涉及相互作用界面的氨基酸残基的计算鉴定是朝向所有膜蛋白的功能注释的关键步骤。在这里,我们提出了一种基于序列的方法,用于预测跨膜蛋白中的界面残留物。我们的方法的独特特征是它包含两个不同用例的单独的随机林模型:(a)当跨膜区域的位置从晶体结构精确地知道时,并且当从序列预测时,(b)。在与水性暴露的蛋白质段形成鲜明对比中,我们发现位于膜中的相互作用位点不富集用于进化守恒,最有可能由于其受限制的氨基酸组成或其在埋地和暴露残留物中的随机分布而导致的。另一方面,残留物共同被证明是一个非常有信息的特征,迄今为止已经用于预测单个蛋白质中的相互作用位点。 MBPRED分别达到AUC,精度,召回值0.79 / 0.73,0.69 / 0.51和0.55 / 0.48,从而优于先前发布的BORDNER方法以及所有在球状蛋白上培训的方法。此外,我们表明,对于大多数完整的界面补丁,该方法捕获了超过50%的涉及残留物。

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