Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level...Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level of whole research to a large extent and guide the discovery of new drugs. In this paper, Melittin and amoebapore three-dimensional quantitative structureactivity relationship(3D-QSAR) models were established by using comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) method. The result shows that, the correlation coefficient(q^2) was 0.583 and non-cross-validation correlation coefficient(r^2) was 0.972 for the melittin CoMFA model. The q^2 and r^2 were 0.630 and 0.995 for the best CoMSIA model, 0.645 and 0.993 for the amoebapore CoMFA model, and 0.738 and 0.996 for the best CoMSIA model. The statistical parameters demonstrated that the CoMFA and CoMSIA models had both good predictive ability and high statistical stability, and can provide theoretical basis for designing new high activity polypeptide drugs.展开更多
基金Supported by the National Natural Science Foundation of China(21475081)Natural Science Foundation of Shaanxi Province of China(2015JM2057)Graduate Innovation Fund of Shaanxi University of Science and Technology
文摘Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level of whole research to a large extent and guide the discovery of new drugs. In this paper, Melittin and amoebapore three-dimensional quantitative structureactivity relationship(3D-QSAR) models were established by using comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) method. The result shows that, the correlation coefficient(q^2) was 0.583 and non-cross-validation correlation coefficient(r^2) was 0.972 for the melittin CoMFA model. The q^2 and r^2 were 0.630 and 0.995 for the best CoMSIA model, 0.645 and 0.993 for the amoebapore CoMFA model, and 0.738 and 0.996 for the best CoMSIA model. The statistical parameters demonstrated that the CoMFA and CoMSIA models had both good predictive ability and high statistical stability, and can provide theoretical basis for designing new high activity polypeptide drugs.
