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Computational Prediction of Solvation Free Energies of Amino Acids with Genetic Algorithm
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  • Computational Prediction of Solvation Free Energies of Amino Acids with Genetic Algorithm
  • Computational Prediction of Solvation Free Energies of Amino Acids with Genetic Algorithm
저자명
Park. Jung-Hum,Lee. Jin-Won,Park. Hwang-Seo
간행물명
Bulletin of the Korean Chemical Society
권/호정보
2010년|31권 5호|pp.1247-1251 (5 pages)
발행정보
대한화학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

We propose an improved solvent contact model to estimate the solvation free energies of amino acids from individual atomic contributions. The modification of the solvation model involves the optimization of three kinds of parameters in the solvation free energy function: atomic fragmental volume, maximum atomic occupancy, and atomic solvation parameters. All of these atomic parameters for 17 atom types are developed by the operation of a standard genetic algorithm in such a way to minimize the difference between experimental and calculated solvation free energies. The present solvation model is able to predict the experimental solvation free energies of amino acids with the squared correlation coefficients of 0.94 and 0.93 for the parameterization with Gaussian and screened Coulomb potential as the envelope functions, respectively. This result indicates that the improved solvent contact model with the newly developed atomic parameters would be a useful tool for the estimation of the molecular solvation free energy of a protein in aqueous solution.