期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 102, 期 7, 页码 2362-2367出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0408885102
关键词
langevin dynamics; mesoscopic models; restricted free energy
资金
- FIC NIH HHS [R03 TW001064] Funding Source: Medline
- NIGMS NIH HHS [GM-14312, R01 GM014312] Funding Source: Medline
We report the application of Langevin dynamics to the physics-based united-residue (UNRES) force field developed in our laboratory. Ten trajectories were run on seven proteins [PDB ID codes 1BDD (alpha; 46 residues), 1GAB (alpha; 47 residues), 1LQ7 (alpha; 67 residues), 1CLB (alpha; 75 residues), 1E0L (beta; 28 residues), and 11E0G (alpha+beta; 48 residues), and 1IGD (alpha+beta; 61 residues)] with the UNRES force field parameterized by using our recently developed method for obtaining a hierarchical structure of the energy landscape. All alpha-helical proteins and 1E0G folded to the native-like structures, whereas 1IGD and 1E0L yielded mostly nonnative alpha-helical folds although the native-like structures are lowest in energy for these two proteins, which can be attributed to neglecting the entropy factor in the current parameterization of UNRES. Average folding times for successful folding simulations were of the order of nanoseconds, whereas even the ultrafast-folding proteins fold only in microseconds, which implies that the UNRES time scale is approximately three orders of magnitude larger than the experimental time scale because the fast motions of the secondary degrees of freedom are averaged out. Folding with Langevin dynamics required 2-10 h of CPU time on average with a single AMD Athlon MP 2800+ processor depending on the size of the protein. With the advantage of parallel processing, this process leads to the possibility to explore thousands of folding pathways and to predict not only the native structure but also the folding scenario of a protein together with its quantitative kinetic and thermodynamic characteristics.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据