An index for characterization of natural and non-natural amino acids for peptidomimetics.

An index for characterization of natural and non-natural amino acids for peptidomimetics.
复制标题

肽仪的天然和非天然氨基酸表征的指数。

DOI:
10.1371/journal.pone.0067844
复制
发表时间:
2013
期刊:
影响因子:
3.7
通讯作者:
Zheng J
Zheng J
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Liang G;Liu Y;Shi B;Zhao J;Zheng J

文献摘要

参考文献

被引文献

相似文献

生物活性肽和拟肽在细胞凋亡、宿主防御和生物矿化等许多生物过程的调控中发挥着关键作用。在这项工作中,我们开发了一个新的结构矩阵,即天然和非天然氨基酸指数(NNAAIndex),系统地表征了22种天然氨基酸和593种非天然氨基酸的155种理化性质,然后将结构矩阵聚类为6种具有代表性的性质模式,包括几何特征、氢键、连通性、可达表面积、完整性矩指数、体积和形状。作为原理证明,nnaindex结合偏最小二乘回归或线性判别分析,使用三种不同的肽数据集,即48种苦味二肽,58种血管紧张素转换酶抑制剂和20种无机结合肽,建立了不同的QSAR模型,用于设计新的肽模拟物。与其他QSAR技术的对比分析表明,nnaindex方法为大规模设计具有理想生物活性的天然和非天然肽提供了一种稳定的预测建模技术,具有广泛的应用前景。
Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications.
血管紧张素转换酶(ACE)基因插入/缺失多态性和ACE抑制剂相关的咳嗽:荟萃分析。
DOI: 10.1371/journal.pone.0037396
发表时间: 2012
期刊: PloS one
影响因子: 3.7
作者:
Li YF;Zhu XM;Liu F;Xiao CS;Bian YF;Li H;Cai J;Li RS;Yang XC
通讯作者: Yang XC
DOI: 10.1093/bioinformatics/bth322
发表时间: 2004-11-01
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Kaur, H;Raghava, GPS
通讯作者: Raghava, GPS
DOI: 10.2174/1568026023392887
发表时间: 2002-12-01
影响因子: 3.4
作者:
Akamatsu, Miki
通讯作者: Akamatsu, Miki
DOI: 10.1021/pr0502267
发表时间: 2006-01-01
影响因子: 4.4
作者:
Hou, TJ;McLaughlin, W;Wang, W
通讯作者: Wang, W
DOI: 10.1021/jm050876m
发表时间: 2006-04-06
影响因子: 7.3
作者:
Doytchinova, IA;Flower, DR
通讯作者: Flower, DR