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通过 TrioSA 提高几何验证指标并确保与实验数据的一致性:一种 NMR 精修协议。

Improving Geometric Validation Metrics and Ensuring Consistency with Experimental Data through TrioSA: An NMR Refinement Protocol.

机构信息

Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon 34141, Republic of Korea.

Disease Target Structure Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon 34141, Republic of Korea.

出版信息

Int J Mol Sci. 2023 Aug 28;24(17):13337. doi: 10.3390/ijms241713337.

DOI:10.3390/ijms241713337
PMID:37686144
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10487420/
Abstract

Protein model refinement a the crucial step in improving the quality of a predicted protein model. This study presents an NMR refinement protocol called TrioSA (torsion-angle and implicit-solvation-optimized simulated annealing) that improves the accuracy of backbone/side-chain conformations and the overall structural quality of proteins. TrioSA was applied to a subset of 3752 solution NMR protein structures accompanied by experimental NMR data: distance and dihedral angle restraints. We compared the initial NMR structures with the TrioSA-refined structures and found significant improvements in structural quality. In particular, we observed a reduction in both the maximum and number of NOE (nuclear Overhauser effect) violations, indicating better agreement with experimental NMR data. TrioSA improved geometric validation metrics of NMR protein structure, including backbone accuracy and the secondary structure ratio. We evaluated the contribution of each refinement element and found that the torsional angle potential played a significant role in improving the geometric validation metrics. In addition, we investigated protein-ligand docking to determine if TrioSA can improve biological outcomes. TrioSA structures exhibited better binding prediction compared to the initial NMR structures. This study suggests that further development and research in computational refinement methods could improve biomolecular NMR structural determination.

摘要

蛋白质模型精修是提高预测蛋白质模型质量的关键步骤。本研究提出了一种称为 TrioSA(扭转角和隐溶剂优化模拟退火)的 NMR 精修方案,该方案可提高骨架/侧链构象的准确性和蛋白质的整体结构质量。TrioSA 应用于一组包含实验 NMR 数据(距离和二面角约束)的 3752 个溶液 NMR 蛋白质结构的子集。我们将初始 NMR 结构与 TrioSA 精修结构进行了比较,发现结构质量有了显著提高。特别是,我们观察到 NOE(核 Overhauser 效应)违反数量和最大值都减少了,这表明与实验 NMR 数据的一致性更好。TrioSA 提高了 NMR 蛋白质结构的几何验证指标,包括骨架准确性和二级结构比。我们评估了每个精修元素的贡献,发现扭转角势在提高几何验证指标方面起着重要作用。此外,我们还研究了蛋白质-配体对接,以确定 TrioSA 是否可以改善生物学结果。与初始 NMR 结构相比,TrioSA 结构的结合预测更好。这项研究表明,进一步开发和研究计算精修方法可以提高生物分子 NMR 结构测定的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a630/10487420/a3f0b36bf1d5/ijms-24-13337-g006.jpg
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