BACKGROUND Protein-protein interactions (PPIs) mediate the vast majority of biological processes, therefore, significant efforts have been directed to investigate PPIs to fully comprehend cellular functions. Predicting complex structures is critical to reveal molecular mechanisms by which proteins operate. Despite recent advances in the development of new methods to model macromolecular assemblies, most current methodologies are designed to work with experimentally determined protein structures. However, because only computer-generated models are available for a large number of proteins in a given genome, computational tools should tolerate structural inaccuracies in order to perform the genome-wide modeling of PPIs. RESULTS To address this problem, we developed eRank(PPI), an algorithm for the identification of near-native conformations generated by protein docking using experimental structures as well as protein models. The scoring function implemented in eRank(PPI) employs multiple features including interface probability estimates calculated by eFindSite(PPI) and a novel contact-based symmetry score. In comparative benchmarks using representative datasets of homo- and hetero-complexes, we show that eRank(PPI) consistently outperforms state-of-the-art algorithms improving the success rate by ~10 %. CONCLUSIONS eRank(PPI) was designed to bridge the gap between the volume of sequence data, the evidence of binary interactions, and the atomic details of pharmacologically relevant protein complexes. Tolerating structure imperfections in computer-generated models opens up a possibility to conduct the exhaustive structure-based reconstruction of PPI networks across proteomes. The methods and datasets used in this study are available at www.brylinski.org/erankppi.

中文翻译：

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预测的结合位点信息可使用实验和计算机生成的目标结构改善蛋白质对接中的模型排名。

背景技术蛋白质-蛋白质相互作用（PPI）介导了绝大多数的生物过程，因此，已经进行了巨大的努力来研究PPI以充分理解细胞功能。预测复杂的结构对于揭示蛋白质运作的分子机制至关重要。尽管在开发用于模拟大分子组装的新方法方面取得了最新进展，但大多数当前的方法仍被设计为可通过实验确定的蛋白质结构使用。但是，由于只有计算机生成的模型可用于给定基因组中的大量蛋白质，因此计算工具应能够容忍结构上的不准确性，以便执行PPI的全基因组建模。结果为了解决此问题，我们开发了eRank（PPI），一种使用实验结构和蛋白质模型识别蛋白质对接产生的近天然构象的算法。在eRank（PPI）中实现的评分功能采用了多种功能，包括由eFindSite（PPI）计算的界面概率估计和一种新颖的基于接触的对称性评分。在使用同构和异构复合体的代表性数据集进行的比较基准测试中，我们显示eRank（PPI）始终优于最新算法，将成功率提高了约10％。结论eRank（PPI）旨在弥合序列数据量，二元相互作用的证据以及药理学相关蛋白复合物的原子细节之间的鸿沟。在计算机生成的模型中容忍结构缺陷为跨蛋白质组进行PPI网络基于结构的详尽重建提供了可能性。本研究中使用的方法和数据集可在www.brylinski.org/erankppi上获得。