IF 6.4 1区 生物学 Q1 BIOLOGY
eLife Pub Date : 2025-03-28 DOI:10.7554/eLife.105005
Manming Xu, Sarath Chandra Dantu, James A Garnett, Robert A Bonomo, Alessandro Pandini, Shozeb Haider
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引用次数: 0

摘要

蛋白质动力学与功能之间的关系对于了解生物过程和开发有效疗法至关重要。蛋白质中的功能位点对于底物结合、催化和结构变化等活动至关重要。预测功能残基的现有计算方法是根据序列、结构和实验数据训练出来的,但它们并没有明确模拟进化对蛋白质动力学的影响。这一被忽视的贡献至关重要,因为众所周知,进化可以通过补偿性突变对蛋白质动力学进行微调,从而改善蛋白质的性能或使其功能多样化,同时保持相同的结构支架。为了模拟这一关键贡献,我们引入了 DyNoPy,这是一种将残基协同进化分析与分子动力学模拟相结合的计算方法,可以揭示功能位点之间隐藏的相关性。DyNoPy 构建了一个残基-残基相互作用图模型,识别了关键残基群落,并根据其作用注释了关键位点。通过利用共同进化动态耦合的概念--在进化过程中保留了关键动态相互作用的残基对--DyNoPy 提供了一种预测和分析蛋白质进化和动态的强大方法。我们展示了 DyNoPy 在 SHV-1 和 PDC-3(与抗生素耐药性有关的染色体编码 β-内酰胺酶)上的有效性,突出了它在为药物设计提供信息和应对紧迫的医疗挑战方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Functionally important residues from graph analysis of coevolved dynamic couplings.

The relationship between protein dynamics and function is essential for understanding biological processes and developing effective therapeutics. Functional sites within proteins are critical for activities such as substrate binding, catalysis, and structural changes. Existing computational methods for the predictions of functional residues are trained on sequence, structural, and experimental data, but they do not explicitly model the influence of evolution on protein dynamics. This overlooked contribution is essential as it is known that evolution can fine-tune protein dynamics through compensatory mutations either to improve the proteins' performance or diversify its function while maintaining the same structural scaffold. To model this critical contribution, we introduce DyNoPy, a computational method that combines residue coevolution analysis with molecular dynamics simulations, revealing hidden correlations between functional sites. DyNoPy constructs a graph model of residue-residue interactions, identifies communities of key residue groups, and annotates critical sites based on their roles. By leveraging the concept of coevolved dynamical couplings-residue pairs with critical dynamical interactions that have been preserved during evolution-DyNoPy offers a powerful method for predicting and analysing protein evolution and dynamics. We demonstrate the effectiveness of DyNoPy on SHV-1 and PDC-3, chromosomally encoded β-lactamases linked to antibiotic resistance, highlighting its potential to inform drug design and address pressing healthcare challenges.

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来源期刊
eLife
eLife BIOLOGY-
CiteScore
12.90
自引率
3.90%
发文量
3122
审稿时长
17 weeks
期刊介绍: eLife is a distinguished, not-for-profit, peer-reviewed open access scientific journal that specializes in the fields of biomedical and life sciences. eLife is known for its selective publication process, which includes a variety of article types such as: Research Articles: Detailed reports of original research findings. Short Reports: Concise presentations of significant findings that do not warrant a full-length research article. Tools and Resources: Descriptions of new tools, technologies, or resources that facilitate scientific research. Research Advances: Brief reports on significant scientific advancements that have immediate implications for the field. Scientific Correspondence: Short communications that comment on or provide additional information related to published articles. Review Articles: Comprehensive overviews of a specific topic or field within the life sciences.
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