文章基本信息

标题：Self-consistently optimized energy functions for protein structure prediction by molecular dynamics
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作者：Kristin K. Koretke ; Zaida Luthey-Schulten ; Peter G. Wolynes 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：1998
卷号：95
期号：6
页码：2932-2937
DOI：10.1073/pnas.95.6.2932
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：The protein energy landscape theory is used to obtain optimal energy functions for protein structure prediction via simulated annealing. The analysis here takes advantage of a more complete statistical characterization of the protein energy landscape and thereby improves on previous approximations. This schema partially takes into account correlations in the energy landscape. It also incorporates the relationships between folding dynamics and characteristic energy scales that control the collapse of the proteins and modulate rigidity of short-range interactions. Simulated annealing for the optimal energy functions, which are associative memory hamiltonians using a database of folding patterns, generally leads to quantitatively correct structures. In some cases the algorithm achieves "creativity," i.e., structures result that are better than any homolog in the database.
关键词：protein folding ; energy landscape