PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction.

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Shuya Li, Tingzhong Tian, Ziting Zhang, Ziheng Zou, Dan Zhao, Jianyang Zeng
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引用次数: 0

Abstract

Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.

Abstract Image

PocketAnchor:学习基于结构的口袋表示,用于蛋白质-配体相互作用预测。
蛋白质-配体相互作用对细胞活动和药物发现过程至关重要。适当和有效地表示蛋白质特征对于开发用于预测蛋白质-配体相互作用的计算方法,特别是数据驱动方法至关重要。然而,现有的方法可能无法充分研究蛋白质口袋中配体占据区域的特征。在这里,我们设计了一种基于结构的蛋白质表示方法,命名为PocketAnchor,用于捕获蛋白质口袋的局部环境和空间特征,以促进蛋白质-配体相互作用相关的学习任务。我们将“锚点”定义为到达空腔和位于蛋白质表面附近的探针点,并设计了一种特定的信息传递策略,用于从这些锚点附近的原子和表面收集局部信息。综合评价表明,我们的方法成功应用于口袋检测和结合亲和力预测,这表明我们基于锚定的方法可以提供有效的蛋白质特征表示,以提高蛋白质-配体相互作用的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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