Factor analysis scales of generalized amino acid information as applied in predicting interactions between the human amphiphysin-1 SH3 domains and their peptide Ligands
Factor analysis scales of generalized amino acid information as applied in predicting interactions between the human amphiphysin-1 SH3 domains and their peptide Ligands
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DOI：
10.1111/j.1747-0285.2008.00641.x
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发表时间：
2008-04-01
影响因子：
3
通讯作者：
Li, Zhiliang
中科院分区：
文献类型：
--
作者：
Liang, Guizhao;Chen, Guohua;Li, Zhiliang
提出了包含疏水性、α -螺旋和β -转倾向、体积特性、组成特性、局部柔韧性和电子特性的广义氨基酸信息因子分析量表（FASGAI）来表示结合人amphiphysin-1 SH3结构域的十肽的结构。采用遗传算法选择与肽- sh3结构域相互作用有关的参数，建立了基于偏最小二乘法的定量结构-亲和关系（QSAR）模型来预测肽- sh3结构域相互作用。十肽（p4p3p2p1p0 - 1p - 2p - 3p - 4p -5）的P-2和P-3之间的残基（包括P-2和P-3）的不同性质可能对SH3结构域与十肽的相互作用有显著影响。特别是，P2的电子性质可能对相互作用提供相对较大的正贡献，相反，P2的疏水性可能在很大程度上对相互作用产生负贡献。这些结果表明，FASGAI载体可以很好地反映十肽的结构特征。此外，所获得的模型计算复杂度较低，将FASGAI描述符与结合亲和力相关联，表明FASGAI载体也可用于多肽的QSAR研究。
Factor analysis scales of generalized amino acid information (FASGAI) involving hydrophobicity, alpha-helix and beta-turn propensities, bulky properties, compositional characteristics, local flexibility, and electronic properties, was proposed to represent the structures of the decapeptides binding the human amphiphysin-1 SH3 domains. Parameters being responsible for the binding affinities were selected by genetic algorithm, and a quantitative structure-affinity relationship (QSAR) model by partial least square was established to predict the peptide-SH3 domain interactions. Diversified properties of the residues between P-2 and P-3 (including P-2 and P-3) of the decapeptide (P4P3P2P1P0P-1P-2P-3P-4P-5) may contribute remarkable effect to the interactions between the SH3 domain and the decapeptide. Particularly, electronic properties of P2 may provide relatively large positive contributions to the interactions, and reversely, hydrophobicity of P2 may be largely negative to the interactions. These results showed that FASGAI vectors can well represent the structural characteristics of the decapeptides. Furthermore, the model obtained, which showed low computational complexity, correlated FASGAI descriptors with the binding affinities as well as that FASGAI vectors may also be applied in QSAR studies of peptides.