Computational Biology Program, The University of Kansas, Lawrence, Kansas.
United Institute of Informatics Problems, National Academy of Sciences, Minsk, Belarus.
Proteins. 2019 Mar;87(3):245-253. doi: 10.1002/prot.25645. Epub 2018 Dec 27.
Structural characterization of protein-protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains-biological process, molecular function, and cellular component (GO-score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO-terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.
蛋白质-蛋白质相互作用的结构特征对于我们在分子水平上研究生命过程的能力至关重要。蛋白质复合物的计算建模（蛋白质对接）很重要，因为它是蛋白质结构的来源，也是理解蛋白质相互作用原理的一种方式。快速发展的比较对接方法利用目标/模板相似性度量，这些度量通常基于蛋白质结构。尽管结构相似性通常能产生良好的性能，但相互作用蛋白质的其他特征（例如功能、生物过程和定位）可能会提高预测质量，尤其是在目标/模板结构相似性较弱的情况下。对于为每个目标对一组模型进行排名，我们测试了对目标和模板蛋白质在三个本体域（生物过程、分子功能和细胞成分）中分配的基因本体 (GO) 术语进行量化相似性的评分函数 (GO 分数)。在对接结合、未结合和建模蛋白质时测试了评分函数。结果表明，结构和 GO 术语功能的组合可以提高评分，尤其是在结构相似性的黄昏区，这是有限精度蛋白质模型的典型特征。
