二面角粘附:在缺乏实验数据的情况下评估蛋白质结构预测

Musa Azeem, Homayoun Valafar
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引用次数: 0

摘要

确定蛋白质的三维结构对于了解它们在细胞环境中的行为至关重要。预测蛋白质结构的计算方法不断进步,但评估预测准确性仍是一项挑战。传统方法 RMSD 依赖于实验确定的结构,缺乏对预测改进领域的洞察力。我们提出了一种替代方法:分析二面角,绕过了对被评估蛋白质参考结构的需求。我们的方法将蛋白质分割成氨基酸序列,然后搜索匹配的蛋白质,比较大量蛋白质的二面角,利用马哈拉诺比距离计算出一个度量值。在对大量预测进行评估后,我们的方法与 RMSD 相关联,并确定了预测增强的领域。这种方法为准确的蛋白质结构预测评估和改进提供了一条很有前景的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dihedral Angle Adherence: Evaluating Protein Structure Predictions in the Absence of Experimental Data
Determining the 3D structures of proteins is essential in understanding their behavior in the cellular environment. Computational methods of predicting protein structures have advanced, but assessing prediction accuracy remains a challenge. The traditional method, RMSD, relies on experimentally determined structures and lacks insight into improvement areas of predictions. We propose an alternative: analyzing dihedral angles, bypassing the need for the reference structure of an evaluated protein. Our method segments proteins into amino acid subsequences and searches for matches, comparing dihedral angles across numerous proteins to compute a metric using Mahalanobis distance. Evaluated on many predictions, our approach correlates with RMSD and identifies areas for prediction enhancement. This method offers a promising route for accurate protein structure prediction assessment and improvement.
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