Integrated structure-based protein interface prediction.
Integrated structure-based protein interface prediction.
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DOI：
10.1186/s12859-022-04852-2
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发表时间：
2022-07-25
影响因子：
3
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中科院分区：
文献类型：
--
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识别蛋白质界面可以告知蛋白质如何与其结合伴侣相互作用，揭示控制生物功能的调控机制，并指导新型治疗药物的开发。已经开发了多种计算方法用于从蛋白质的已知序列和结构预测蛋白质的界面残基。使用蛋白质的已知三维结构的方法可以是基于模板的或无模板的。基于模板的方法在预测接口时，具有已知的复杂结构的同系物不能用作模板的成功有限。仅依赖于蛋白质的内在性质的无模板方法的预测性能受到界面预测模型中可以包括的生物相关特征的量的限制。我们描述了蛋白质界面预测（ISPIP）的综合方法的发展，以探索的假设，即蛋白质结合位点的计算预测方法的效力可以通过使用依赖于查询蛋白质的基于正交结构的属性的方法的组合来增强，结合和平衡无模板和基于模板的功能。ISPIP是一种通过简单的线性或逻辑回归模型和更复杂的决策树模型集成这些方法的方法。在156个查询蛋白质的不同测试集上，ISPIP在识别蛋白质结合界面方面优于其各个分类器。集成的方法捕获的最佳性能的个人分类器，并提供了一个改进的接口预测。该方法是鲁棒的，即使当一个单独的分类器在特定的查询蛋白质上表现不佳时，该方法也表现良好。这项工作表明，整合正交方法，依赖于不同的蛋白质结构特性进行更好的界面预测比任何单独的分类器。在线版本包含补充材料，可通过10.1186/s12859-022-04852-2获得。
Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model. We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces. The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone. The online version contains supplementary material available at 10.1186/s12859-022-04852-2.
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