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连续评估配体蛋白预测:药物对接的每周社区挑战。

Continuous Evaluation of Ligand Protein Predictions: A Weekly Community Challenge for Drug Docking.

机构信息

Drug Design Data Resource, University of California San Diego, La Jolla, CA 92093, USA.

RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.

出版信息

Structure. 2019 Aug 6;27(8):1326-1335.e4. doi: 10.1016/j.str.2019.05.012. Epub 2019 Jun 27.

Abstract

Docking calculations can accelerate drug discovery by predicting the bound poses of ligands for a targeted protein. However, it is not clear which docking methods work best. Furthermore, predicting poses requires steps outside the docking algorithm itself, such as preparation of the protein and ligand, and it is not known which components are most in need of improvement. The Continuous Evaluation of Ligand Protein Predictions (CELPP) is a blinded prediction challenge designed to address these issues. Participants create a workflow to predict protein-ligand binding poses, which is then tasked with predicting 10-100 new protein-ligand crystal structures each week. CELPP evaluates the accuracy of each workflow's predictions and posts the scores online. The results can be used to identify the strengths and weaknesses of current approaches, help map docking problems to the algorithms most likely to overcome them, and illuminate areas of unmet need in structure-guided drug design.

摘要

对接计算可以通过预测配体与靶标蛋白的结合构象来加速药物发现。然而,目前尚不清楚哪种对接方法效果最好。此外,预测构象需要对接算法本身以外的步骤,例如蛋白质和配体的准备,并且尚不清楚哪些组件最需要改进。连续评估配体-蛋白质预测(CELPP)是一项盲法预测挑战,旨在解决这些问题。参与者创建一个工作流程来预测蛋白质-配体结合构象,然后每周预测 10-100 个新的蛋白质-配体晶体结构。CELPP 评估每个工作流程预测的准确性,并在线发布分数。结果可用于识别当前方法的优缺点,帮助将对接问题映射到最有可能克服这些问题的算法,并阐明结构导向药物设计中未满足需求的领域。

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