Gowthaman Ragul, Park Minjae, Yin Rui, Guest Johnathan D, Pierce Brian G
University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, Maryland, USA.
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, USA.
Proteins. 2025 Jan 20. doi: 10.1002/prot.26801.
Accurate modeling of the structures of protein-protein complexes and other biomolecular interactions represents a longstanding and important challenge for computational biology. The Critical Assessment of PRedicted Interactions (CAPRI) experiment has served for over two decades as a key means to assess and compare current approaches and methods through blind predictive scenarios, highlighting useful strategies, and new developments. Here we describe the performance of our laboratory's team in recent CAPRI rounds, which included submissions for 10 modeling targets. Our team utilized a range of docking and modeling approaches, including ZDOCK, Rosetta, and ZRANK, to model, refine, and score protein-protein and protein-DNA complexes. For recent targets we utilized adaptations of AlphaFold to generate models, leading to near-native models for an antibody-peptide target, and a highly accurate (but low ranked) model for an antibody-MHC complex. These results underscore the utility of AlphaFold-based protocols for predictive protein complex modeling, including for immune recognition, and highlight considerations regarding the use of AlphaFold confidence metrics in model selection.
准确模拟蛋白质-蛋白质复合物的结构以及其他生物分子相互作用，是计算生物学长期以来面临的一项重要挑战。蛋白质相互作用预测关键评估（CAPRI）实验二十多年来一直是通过盲预测场景评估和比较当前方法及手段的关键途径，突出了有用的策略和新进展。在此，我们描述了我们实验室团队在最近几轮CAPRI中的表现，其中包括对10个建模目标的提交。我们的团队利用了一系列对接和建模方法，包括ZDOCK、Rosetta和ZRANK，对蛋白质-蛋白质和蛋白质-DNA复合物进行建模、优化和评分。对于最近的目标，我们利用AlphaFold的改编版本来生成模型，从而为一个抗体-肽目标生成了接近天然的模型，并为一个抗体-MHC复合物生成了高度准确（但排名较低）的模型。这些结果强调了基于AlphaFold的协议在预测蛋白质复合物建模中的实用性，包括在免疫识别方面，并突出了在模型选择中使用AlphaFold置信度指标的相关考虑因素。
