Yu Jinchao, Andreani Jessica, Ochsenbein Françoise, Guerois Raphaël
Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Univ Paris-Sud, Université Paris-Saclay, Gif-sur-Yvette cedex, F-91198, France.
Proteins. 2017 Mar;85(3):378-390. doi: 10.1002/prot.25180. Epub 2016 Oct 24.
Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1 by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc.
计算蛋白质-蛋白质对接对于在结构层面理解蛋白质相互作用非常重要。蛋白质相互作用预测关键评估(CAPRI)实验为蛋白质对接领域提供了一个独特的机会,能够基于实际案例对方法进行盲测,并有助于加速方法学的发展。对于CAPRI第28 - 35轮,我们使用了一个自动对接流程,该流程整合了基于粗粒度共进化的势能InterEvScore。开发这个分数是为了利用结合伙伴多序列比对中包含的信息,并选择性地识别共同进化的界面。结合用于刚体对接的Zdock/Frodock、用于原子势能的SOAP - PP以及用于结构优化的Rosetta应用,这个流程在大多数目标上都达到了高性能。对于蛋白质-肽对接和界面水位置预测,我们还探索了考虑进化信息的不同方法。总体而言,我们团队通过正确预测10个目标排名第一,其中包括1个高可信度、7个中等可信度和2个可接受度预测。两个水预测目标分别达到了优秀和卓越的准确率水平。总共,在18个目标中的15个目标中,通过共进化或保守性分析的进化信息能够提供关键约束,以指导建模朝着最可能的组装方向进行。这些结果为进化信息如何有助于提高对接预测准确性开辟了有前景的前景。《蛋白质》2017年;85:378 - 390。© 2016威利期刊公司。