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气相、溶剂化及蛋白质络合过程中的配体熵。基于拟牛顿海森矩阵的快速估算

Ligand Entropy in Gas-Phase, Upon Solvation and Protein Complexation. Fast Estimation with Quasi-Newton Hessian.

作者信息

Wlodek S, Skillman A G, Nicholls A

机构信息

OpenEye Scientific Software Incorporated, 9 Bisbee Court, Suite D, Santa Fe, New Mexico 87508.

出版信息

J Chem Theory Comput. 2010 Jul 13;6(7):2140-52. doi: 10.1021/ct100095p.

Abstract

A method of rapid entropy estimation for small molecules in vacuum, solution, and inside a protein receptor is proposed. We show that the Hessian matrix of second derivatives built by a quasi-Newton optimizer during geometry optimization of a molecule with a classical molecular potential in these three environments can be used to predict vibrational entropies. We also show that a simple analytical solvation model allows for no less accurate entropy estimation of molecules in solution than a physically rigorous but computationally more expensive model based on Poisson's equation. Our work also suggests that scaled particle theory more precisely estimates the hydrophobic part of solvation entropy than the using a simple surface area term.

摘要

提出了一种在真空、溶液和蛋白质受体内部对小分子进行快速熵估计的方法。我们表明,在这三种环境中使用经典分子势对分子进行几何优化时,由拟牛顿优化器构建的二阶导数海森矩阵可用于预测振动熵。我们还表明,一个简单的解析溶剂化模型在估计溶液中分子的熵时,其准确性不低于基于泊松方程的物理上严格但计算成本更高的模型。我们的工作还表明,与使用简单表面积项相比,定标粒子理论能更精确地估计溶剂化熵的疏水部分。

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