Integration of genetic virtual screening patterns and latent multivariate modeling techniques for QSAR optimization based on combinations and/or interactions between peptides and proteins

• 来源期刊: 中国科学B辑（英文版）
• 年，卷（期）: 2008,（5）
• 页码范围: 487-496
• 页数: 10页
• 语种: 英文
• DOI: 10.1007/s11426-007-0125-6

摘要

Both the concept and the model of snug quantitative structure-activity relationship (QSAR) were pro-posed and developed for molecular design through constructing QSAR based on some known mode of receptor/ligand interactions. Many disadvantages of traditional models can be avoided by using the proposed method because the traditional models only determined upon molecular structural features in sample sets themselves. A genetic virtual screening of peptide/protein combinations (GVSPPC) is proposed for the first time by utilizing this idea to examine peptide/protein affinity activities. A genetic algorithm (GA) was developed for screening combinative targets with an interaction mode for virtual receptors. GVSPPC succeeds in disposing difficulties in rational QSAR, in order to search for the ligand/receptor interactions on conditions of unknown structures. Some bioactive oligo-/poly-peptide systems covering 58 angiotensin converting enzyme (ACE) inhibitors and 18 double site mutation residues in camel antibody protein cAb-Lys3 were investigated by GVSPPC with satisfactory results (Rcu2 ＞ 0.91, Qcv2 0.86, ERMS = 0.19-0.95), respectively, which demonstrates that GVSPPC is more inter-pretable in the ligand-receptor interaction than the traditional QSAR method.