Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins

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• 作者：Hai Nguyen ; Alberto P茅rez ; Sherry Bermeo ; Carlos Simmerling
• 刊名：Journal of Chemical Theory and Computation
• 出版年：2015
• 出版时间：August 11, 2015
• 年：2015
• 卷：11
• 期：8
• 页码：3714-3728
• 全文大小：690K
• ISSN：1549-9626

文摘

The Generalized Born (GB) implicit solvent model has undergone significant improvements in accuracy for modeling of proteins and small molecules. However, GB still remains a less widely explored option for nucleic acid simulations, in part because fast GB models are often unable to maintain stable nucleic acid structures or they introduce structural bias in proteins, leading to difficulty in application of GB models in simulations of protein鈥搉ucleic acid complexes. Recently, GB-neck2 was developed to improve the behavior of protein simulations. In an effort to create a more accurate model for nucleic acids, a similar procedure to the development of GB-neck2 is described here for nucleic acids. The resulting parameter set significantly reduces absolute and relative energy error relative to Poisson鈥揃oltzmann for both nucleic acids and nucleic acid鈥損rotein complexes, when compared to its predecessor GB-neck model. This improvement in solvation energy calculation translates to increased structural stability for simulations of DNA and RNA duplexes, quadruplexes, and protein鈥搉ucleic acid complexes. The GB-neck2 model also enables successful folding of small DNA and RNA hairpins to near native structures as determined from comparison with experiment. The functional form and all required parameters are provided here and also implemented in the AMBER software.