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Evolutionary couplings and sequence variation effect predict protein binding sites

机译:进化偶联和序列变异效果预测蛋白结合位点

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摘要

Abstract Binding small ligands such as ions or macromolecules such as DNA, RNA, and other proteins is one important aspect of the molecular function of proteins. Many binding sites remain without experimental annotations. Predicting binding sites on a per‐residue level is challenging, but if 3D structures are known, information about coevolving residue pairs (evolutionary couplings) can predict catalytic residues through mutual information. Here, we predicted protein binding sites from evolutionary couplings derived from a global statistical model using maximum entropy. Additionally, we included information from sequence variation. A simple method using a weighted sum over eight scores substantially outperformed random (F1?=?19.3%? ± 0.7% vs F1?= 2% for random). Training a neural network on these eight scores (along with predicted solvent accessibility and conservation in protein families) improved substantially (F1?=?26.2% ± 0.8%). Although the machine learning was limited by the small data set and possibly wrong annotations of binding sites, the predicted binding sites formed spatial clusters in the protein. The source code of the binding site predictions is available through GitHub: https://github.com/Rostlab/bindPredict .
机译:摘要结合小配体,如离子或大分子,如DNA,RNA和其他蛋白质是蛋白质分子功能的一个重要方面。许多绑定站点仍然没有实验注释。预测每残基水平上的结合位点是挑战性的,但是,如果已知3D结构,则有关辅助残留物对(进化偶联)的信息可以通过相互信息预测催化残基。这里,我们预测使用从全局统计模型的进化偶联使用最大熵的蛋白质结合位点。此外,我们包括序列变异的信息。使用加权和超过八分的简单方法基本上不现当于随机(F1?=Δ= 19.3%?±0.7%Vs f1?= 2%。在这八分(以及蛋白质家族的预测溶剂可获取性和保护以及蛋白质家庭的预测溶剂可访问性和保护)大致改善(F1?= 26.2%±0.8%)。尽管机器学习受到小数据集的限制并且可能是结合位点的错误注释,但是预测的结合位点在蛋白质中形成了空间簇。绑定站点预测的源代码可通过github获得:https://github.com/rostlab/bindpredict。

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