背景:正确评分蛋白质-蛋白质对接模型以找出正确的模型是一个开放的挑战,也是CAPRI(预测相互作用的批判性评估)中的评估对象,社区范围内的盲目对接实验。我们在现场介绍了CONSRNK(CONSensusRANKing),第一种纯共识方法。也可作为Web服务器,CONSRank根据对接模型匹配其中最常见的残基间接触的能力对对接模型进行排序。在所有最新的CAPRI回合中,我们一直在盲目地测试CONSRank,我们展示了它与最先进的能量和基于知识的评分功能竞争。最近,我们开发了Clust-CONSRANK,一种引入基于联系人的模型聚类的算法,作为CONSRANK评分过程的初步步骤。在最新的CASP13-CAPRI联合实验中,我们作为得分手参加了一个新颖的管道,结合我们的评分工具,CONSRank和Clust-CONSRank,使用我们的接口分析工具COCOMAPS。正在形成的共识的力量指导了10个提交模型的选择,他们的最终排名得到了界面分析结果的帮助。
结果:由于上述方法,到目前为止,我们是CASP13-CAPRI排名第一的得分手,在大多数目标中,高/中质量模型都排在前1位(总共19个中有11个)。我们也是排名前十的第一个得分手,与另一组相当,也是前5名的第二得分手。Further,相对于绑定接口的预测,我们排名最高,在所有得分手和预测者中。使用CASP13-CAPRI目标作为案例研究,我们在这里详细说明我们采用的方法。
结论:在最终模型选择和排名中引入了一些灵活性,以及根据目标区分采用的评分方法是我们非常成功的业绩的关键资产,与之前的CAPRI轮相比。我们提出的方法完全基于社区可用的方法,因此可以由任何用户复制。
BACKGROUND: Properly scoring protein-protein docking models to single out the correct ones is an open challenge, also object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), a community-wide blind docking experiment. We introduced in the field CONSRANK (CONSensus RANKing), the first pure consensus method. Also available as a web server, CONSRANK ranks docking models in an ensemble based on their ability to match the most frequent inter-residue contacts in it. We have been blindly testing CONSRANK in all the latest CAPRI rounds, where we showed it to perform competitively with the state-of-the-art energy and knowledge-based scoring functions. More recently, we developed Clust-CONSRANK, an algorithm introducing a contact-based clustering of the models as a preliminary step of the CONSRANK scoring process. In the latest CASP13-CAPRI joint experiment, we participated as scorers with a novel pipeline, combining both our scoring tools, CONSRANK and Clust-CONSRANK, with our interface analysis tool COCOMAPS. Selection of the 10 models for submission was guided by the strength of the emerging consensus, and their final ranking was assisted by results of the interface analysis.
RESULTS: As a result of the above approach, we were by far the first scorer in the CASP13-CAPRI top-1 ranking, having high/medium quality models ranked at the top-1 position for the majority of targets (11 out of the total 19). We were also the first scorer in the top-10 ranking, on a par with another group, and the second scorer in the top-5 ranking. Further, we topped the ranking relative to the prediction of binding interfaces, among all the scorers and predictors. Using the CASP13-CAPRI targets as
case studies, we illustrate here in detail the approach we adopted.
CONCLUSIONS: Introducing some flexibility in the final model selection and ranking, as well as differentiating the adopted scoring approach depending on the targets were the key assets for our highly successful performance, as compared to previous CAPRI rounds. The approach we propose is entirely based on methods made available to the community and could thus be reproduced by any user.