Extending the PRIME model for protein aggregation to all 20 amino acids.
Extending the PRIME model for protein aggregation to all 20 amino acids.
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DOI：
10.1002/prot.22817
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发表时间：
2010-11-01
影响因子：
2.9
通讯作者：
Hall, Carol K.
中科院分区：
文献类型：
--
作者：
Cheon, Mookyung;Chang, Iksoo;Hall, Carol K.
我们扩展PRIME，一个中间分辨率的蛋白质模型，以前用于模拟聚丙氨酸和聚谷氨酰胺的聚集，描述的几何形状和能量的肽含有所有20个氨基酸残基。20个氨基酸侧链根据其疏水性、极性、大小、电荷和侧链氢键结合的可能性分为14组。扩展PRIME的参数，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。通过应用感知器学习算法和改进的随机学习算法来获得参数，所述改进的随机学习算法优化来自PDB的711个已知原生状态与通过无间隙线程生成的诱饵结构之间的能隙。独立的配对相互作用参数的数量被选择为足够小，是物理上有意义的，但又足够大，以给出合理的准确的结果，从本地结构中区分诱饵。用19个能量参数得到了最有物理意义的结果。
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 twenty 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.
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DOI：
10.1073/pnas.0502006102
发表时间：
2005-04-26
影响因子：
11.1
作者：
Borreguero, JM;Urbanc, B;Stanley, HE
通讯作者：
Stanley, HE
影响因子：
5.6
作者：
HINDS, DA;LEVITT, M
通讯作者：
LEVITT, M
影响因子：
3.4
作者：
Dehouck, Y;Gilis, D;Rooman, M
通讯作者：
Rooman, M
影响因子：
6.8
作者：
Jernigan, RL;Bahar, I
通讯作者：
Bahar, I
影响因子：
8
作者：
Keskin, O;Bahar, I;Jernigan, RL
通讯作者：
Jernigan, RL