Christoffer Charles, Kagaya Yuki, Verburgt Jacob, Terashi Genki, Shin Woong-Hee, Jain Anika, Sarkar Daipayan, Aderinwale Tunde, Maddhuri Venkata Subramaniya Sai Raghavendra, Wang Xiao, Zhang Zicong, Zhang Yuanyuan, Kihara Daisuke
Department of Computer Science, Purdue University, West Lafayette, Indiana, USA.
Rosen Center for Advanced Computing, Purdue University, West Lafayette, Indiana, USA.
Proteins. 2025 Mar 17. doi: 10.1002/prot.26818.
We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.
我们报告了我们团队蛋白质复合物预测方法的性能，以及它们在CAPRI第47 - 55轮中的结果，不包括联合CASP第50轮和第54轮，以及特殊的COVID - 19第51轮。我们的方法整合了我们团队开发的经典流程以及最近开发的深度学习流程。在人类组预测的情况下，我们查阅文献以找到可整合到建模中的信息，例如经检测的界面残基。除了任何文献信息外，生成的复合物模型通过统计评分函数的排名聚合、生成模型置信度或专家检查来选择。在这些CAPRI轮次中，我们的人类组成功地对八个界面进行了建模，并在所有提交的结果中达到了最高质量水平，其中包括两个没有其他团队做到的。我们注意到，我们建模流程的组件在深度学习方法中越来越趋于统一。最后，我们讨论了几个案例研究，这些案例说明了使用我们的方法进行建模的成功与失败情况。
