Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.
Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.
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DOI：
10.1371/journal.pone.0169356
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Wild DL
中科院分区：
文献类型：
--
作者：
Várnai C;Burkoff NS;Wild DL
存储在多序列比对（MSA）中的进化信息已被用于识别蛋白质复合物的相互作用界面，通过测量跨界面的氨基酸残基的共保守或共突变。最近，最大熵相关的相关突变措施（CMM），如直接信息，解耦直接从间接相互作用，已被开发来识别残基对跨蛋白质复合物界面相互作用。这些研究集中在精心选择的蛋白质复合物与大，高质量的MSA。在这项工作中，我们研究了蛋白质复合物与一个更典型的MSA组成的少于400个序列，使用一组79个分子内蛋白质复合物。使用最大熵为基础的CMM在残留水平，我们开发了一个接口级CMM评分用于重新排序对接诱饵。我们表明，我们的接口级CMM评分相比有利的互补性痕迹评分，进化信息为基础的评分测量共保守，当结合界面残基的数量，知识为基础的潜力和个别氨基酸位点的变异性评分。我们还证明，由于MSA中的共突变和共互补性包含正交信息，使用进化信息的最佳预测性能可以通过将CMM的共突变信息与互补性迹分数的共保守信息相结合来实现，预测近天然结构作为41%数据集的最佳预测。所提出的方法并不局限于小的MSA，并可能提高界面预测也与大型和高质量的MSA的配合物。
Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.
影响因子：
2.9
作者：
Alsop, James D.;Mitchell, Julie C.
通讯作者：
Mitchell, Julie C.