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利用剩余偶极耦合数据对蛋白质的多个构象状态进行计算建模。

Computational modeling multiple conformational states of proteins with residual dipolar coupling data.

作者信息

Abdollahi Hamed, Prestegard James H, Valafar Homayoun

机构信息

Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, USA.

Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA.

出版信息

Curr Opin Struct Biol. 2023 Oct;82:102655. doi: 10.1016/j.sbi.2023.102655. Epub 2023 Jul 14.

Abstract

Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.

摘要

溶液核磁共振光谱为研究生物分子在水环境中的结构和动力学提供了独特的机会。虽然自旋弛豫方法因其探测运动时间尺度的能力而得到广泛认可,但残余偶极耦合(RDCs)能提供有关运动幅度和方向的信息,这些特征对这些分子的功能很重要。尽管RDCs在20世纪60年代就已被观测到,但近年来其采集和计算分析取得了显著进展,尤其是在蛋白质运动方面的应用越来越多。随着RDCs能够轻松利用新计算方法(如AlphaFold)产生的结构来生成功能描述,这一趋势很可能会持续下去。在本报告中,我们提供了一些例子,并总结了RDCs用于确认内部动力学的存在、表征动力学类型以及恢复定义完整构象采样范围的原子尺度结构集合的方法。

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