Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, USA.
Proteins. 2010 Nov 1;78(14):2950-60. doi: 10.1002/prot.22817.
We extend PRIME, an intermediate-resolution protein model previously used in simulations of the aggregation of polyalanine and polyglutamine, to the description of the geometry and energetics of peptides containing all 20 amino acid residues. The 20 amino acid side chains are classified into 14 groups according to their hydrophobicity, polarity, size, charge, and potential for side chain hydrogen bonding. The parameters for extended PRIME, called PRIME 20, include hydrogen-bonding energies, side chain interaction range and energy, and excluded volume. The parameters are obtained by applying a perceptron-learning algorithm and a modified stochastic learning algorithm that optimizes the energy gap between 711 known native states from the PDB and decoy structures generated by gapless threading. The number of independent pair interaction parameters is chosen to be small enough to be physically meaningful yet large enough to give reasonably accurate results in discriminating decoys from native structures. The most physically meaningful results are obtained with 19 energy parameters.
我们将 PRIME 扩展到可以描述含有 20 种氨基酸残基的肽的几何形状和能量，PRIME 是一种以前用于模拟多聚丙氨酸和多聚谷氨酰胺聚集的中分辨率蛋白质模型。20 种氨基酸侧链根据疏水性、极性、大小、电荷和侧链氢键形成的潜力分为 14 组。扩展后的 PRIME 参数，称为 PRIME 20，包括氢键能、侧链相互作用范围和能量以及排除体积。这些参数是通过应用感知机学习算法和一种改进的随机学习算法得到的，该算法优化了 711 个已知来自 PDB 的天然结构和无间隙穿线生成的诱饵结构之间的能量间隙。独立对相互作用参数的数量选择得足够小，以便在物理上有意义，但又足够大，以便在区分诱饵和天然结构方面得到相当准确的结果。用 19 个能量参数可以得到最符合物理意义的结果。
