BDM: An Assessment Metric for Protein Complex Structure Models Based on Distance Difference Matrix.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jiaqi Zhai, Wenda Wang, Ranxi Zhao, Daiwen Sun, Da Lu, Xinqi Gong
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引用次数: 0

Abstract

Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD). BDM overcomes limitations associated with receptor-ligand differentiation and eliminates the requirement for structure alignment, making it a more effective and efficient metric. Evaluation of BDM using CASP14 and CASP15 test sets demonstrates superior performance compared to the official CASP scoring. BDM provides accurate and reasonable assessments of predicted protein complexes, wide adoption of BDM has the potential to advance protein complex structure prediction and facilitate related researches across scientific domains. Code is available at http://mialab.ruc.edu.cn/BDMServer/ .

Abstract Image

BDM:基于距离差矩阵的蛋白质复合结构模型评估指标。
蛋白质复合体结构预测是计算生物学中的一个重要问题。虽然在蛋白质单体方面取得了重大进展,但准确评估蛋白质复合物仍然具有挑战性。CASP 中的现有评估方法缺乏评估复合物的专用指标。DockQ 是一种广泛使用的指标,但也存在一些局限性。在本研究中,我们提出了一种名为 BDM(基于距离差矩阵)的新指标,用于评估蛋白质复合物预测结构。我们的方法利用通过比较真实和预测的蛋白质结构得出的距离差矩阵,与均方根偏差(RMSD)建立线性相关。BDM 克服了与受体-配体区分相关的限制,并消除了结构对齐的要求,使其成为一种更有效、更高效的指标。使用 CASP14 和 CASP15 测试集对 BDM 进行的评估表明,它的性能优于 CASP 官方评分。BDM 能对预测的蛋白质复合物进行准确合理的评估,广泛采用 BDM 有可能推动蛋白质复合物结构预测的发展,促进各科学领域的相关研究。代码见 http://mialab.ruc.edu.cn/BDMServer/ 。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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