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Enhanced conformational sampling to visualize a free-energy landscape of protein complex formation

期刊

BIOCHEMICAL JOURNAL
卷 473, 期 -, 页码 1651-1662

出版社

PORTLAND PRESS LTD
DOI: 10.1042/BCJ20160053

关键词

all-atom model; free energy; generalized ensemble; molecular binding; molecular dynamics; molecular interaction

资金

  1. Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan [21113006]
  2. development of core technologies for innovative drug development based on IT from the Japan Agency for Medical Research and Development (AMED)
  3. Japan Society for the Promotion of Science KAKENHI [24118001]
  4. Grants-in-Aid for Scientific Research [24118001, 16K05517, 21113006] Funding Source: KAKEN

向作者/读者索取更多资源

We introduce various, recently developed, generalized ensemble methods, which are useful to sample various molecular configurations emerging in the process of protein-protein or protein-ligand binding. The methods introduced here are those that have been or will be applied to biomolecular binding, where the biomolecules are treated as flexible molecules expressed by an all-atom model in an explicit solvent. Sampling produces an ensemble of conformations (snapshots) that are thermodynamically probable at room temperature. Then, projection of those conformations to an abstract low-dimensional space generates a free-energy landscape. As an example, we show a landscape of homo-dimer formation of an endothelin-1-like molecule computed using a generalized ensemble method. The lowest free-energy cluster at room temperature coincided precisely with the experimentally determined complex structure. Two minor clusters were also found in the landscape, which were largely different from the native complex form. Although those clusters were isolated at room temperature, with rising temperature a pathway emerged linking the lowest and second-lowest free-energy clusters, and a further temperature increment connected all the clusters. This exemplifies that the generalized ensemble method is a powerful tool for computing the free-energy landscape, by which one can discuss the thermodynamic stability of clusters and the temperature dependence of the cluster networks.

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