物理化学学报 >> 2014, Vol. 30 >> Issue (11): 2157-2167.doi: 10.3866/PKU.WHXB201409171

生物物理化学 上一篇    下一篇

ω-芋螺毒素的定量构效关系与虚拟筛选

丁俊杰, 丁晓琴, 李大禹, 潘里, 陈冀胜   

  1. 北京药物化学研究所, 国民核生化灾害防护国家重点实验室, 北京 102205
  • 收稿日期:2014-07-16 修回日期:2014-09-16 发布日期:2014-10-30
  • 通讯作者: 丁俊杰, 丁晓琴 E-mail:djj224@163.com;dingxiaoqin2008@126.com

Quantitative Structure-Activity Relationship and Virtual Screening of ω-Conotoxins

DING Jun-Jie, DING Xiao-Qin, LI Da-Yu, PAN Li, CHEN Ji-Sheng   

  1. State Key Laboratory of NBC Protection for Civilian, Beijing Institute of Pharmaceutical Chemistry, Beijing 102205, P. R. China
  • Received:2014-07-16 Revised:2014-09-16 Published:2014-10-30
  • Contact: DING Jun-Jie, DING Xiao-Qin E-mail:djj224@163.com;dingxiaoqin2008@126.com

摘要:

ω-芋螺毒素属于海洋生物活性多肽, 由24-31 个氨基酸残基组成. 特异性作用于电压敏感的钙离子通道(VGCCs), 能够直接开发成药物或作为先导化合物进行新药开发. 本文应用新型氨基酸残基结构描述符cscales和遗传偏最小二乘算法, 对ω-芋螺毒素进行定量构效关系(QSAR)研究, 并设计、构建了容量为2244 个化合物的N-型和P/Q-型VGCC拮抗剂虚拟组合多肽库, 然后分别采用QSAR模型预测和相似性搜索方法对组合多肽库进行了虚拟筛选. 研究结果表明, 建立的N-型和P/Q-型VGCC拮抗剂QSAR模型均具有较好的预测能力, 交叉验证相关系数(CV-r2)均大于0.89. 主成分分析和聚类分析结果表明, 虚拟组合多肽库中化合物具有较好的结构多样性和差异性. 通过虚拟筛选, 得到了具有高预测活性的6 个N-型和19 个P/Q-型钙离子通道拮抗剂, 为进一步的合成和活性评价奠定了理论基础. 同时, 本文建立的多肽QSAR预测模型和虚拟筛选策略, 为其它多肽类化合物的定量构效关系研究和虚拟筛选提供了参考.

关键词: ω-芋螺毒素, 钙离子通道拮抗剂, QSAR, 虚拟筛选

Abstract:

ω-Conotoxins are active peptides composed of 24-31 amino acids isolated from venomous marine predatory cone snails. ω-Conotoxins selectively inhibit voltage-gated calcium channels (VGCCs) in nociceptors, so are considered attractive molecules for drug design. In this study, based on a set of new amino acid structure descriptors (c-scales) and genetic partial least squares (G/PLS) regression method, quantitative structureactivity relationship (QSAR) models for N-type and P/Q-type VGCC antagonists of ω-conotoxins were developed. Two virtual polypeptide libraries with 2244 peptides were designed and established for N-type and P/Q-type VGCC antagonists, respectively. Then, based on the biological activities predicted from the constructed QSAR models and chemical similarities to the probes MVIIA and MVIIC, the polypeptide libraries were virtually screened. As a result, the established QSAR models had good predictability (cross- validated correlation coefficient CV-r2>0.89). The structural diversity of the libraries was validated using principal component analysis (PCA) and hierarchical cluster analysis (HCA) approaches. Six N-type and nineteen P/Q-type VGCC antagonists with high selectivity and activity were identified by virtual screening. The results of this study will be valuable for finding highly active polypeptide and non-peptide mimetics. Furthermore, the established polypeptide QSAR models and virtual screening strategy can also be applied to other peptide systems.

Key words: ω-Conotoxin, Calcium channel antagonist, QSAR, Virtual screening

MSC2000: 

  • O641