杠杆(统计)
生物分子
纳米技术
计算机科学
数据科学
物理
计算生物学
生物
人工智能
材料科学
作者
Emiliano Brini,Carlos Simmerling,Ken A. Dill
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2020-11-27
卷期号:370 (6520)
被引量:72
标识
DOI:10.1126/science.aaz3041
摘要
Understanding what drives proteins Computational molecular physics (CMP) aims to leverage the laws of physics to understand not just static structures but also the motions and actions of biomolecules. Applying CMP to proteins has required either simplifying the physical models or running simulations that are shorter than the time scale of the biological activity. Brini et al. reviewed advances that are moving CMP to time scales that match biological events such as protein folding, ligand unbinding, and some conformational changes. They also highlight the role of blind competitions in driving the field forward. New methods such as deep learning approaches are likely to make CMP an increasingly powerful tool in describing proteins in action. Science , this issue p. eaaz3041
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