Evaluating different descriptors for model design of antimicrobial peptides with enhanced activity toward P-aeruginosa
Evaluating different descriptors for model design of antimicrobial peptides with enhanced activity toward P-aeruginosa
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DOI：
10.1111/j.1747-0285.2007.00543.x
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发表时间：
2007-08-01
影响因子：
3
通讯作者：
Hancock, Robert E. W.
中科院分区：
文献类型：
--
作者：
Jenssen, Havard;Lejon, Tore;Hancock, Robert E. W.
在过去的几十年里，分离的耐药病原微生物的数量急剧增加，表明迫切需要新的治疗干预措施。长期以来，抗菌肽一直被视为药物优化的有趣模板。然而，使用大的多肽库来优化多肽的抗微生物活性和特异性的过程既繁琐又昂贵。在这里，我们描述了一个数学模型的构建，用于在合成之前预测多肽对铜绿假单胞菌的抗菌活性。通过使用新的量化相邻氨基酸之间的接触能的描述符以及一组诱导性和常规的定量构效关系描述符，我们能够对多肽的抗菌活性进行建模。实现的描述符值的相互关联和优化使我们能够建立一个模型(BAK2a-#2)，该模型能够在先前测量的相应IC50值的两倍偏差窗口内正确地预测84%的测试肽的活性。预测能力平均为10个子模型，每个子模型预测20个随机排除的多肽的活性，预测成功率为16.7+/-1.6个多肽。该模型也被证明比一个更简单的模型(Bac2a-#1)要准确得多，后者排除了归纳和传统的定量结构-活性关系描述符。
The number of isolated drug-resistant pathogenic microbes has increased drastically over the past decades, demonstrating an urgent need for new therapeutic interventions. Antimicrobial peptides have for a long time been looked upon as an interesting template for drug optimization. However, the process of optimizing peptide antimicrobial activity and specificity, using large peptide libraries is both tedious and expensive. Here, we describe the construction of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward Pseudomonas aeruginosa. By use of novel descriptors quantifying the contact energy between neighboring amino acids in addition to a set of inductive and conventional quantitative structure-activity relationship descriptors, we are able to model the peptides antibacterial activity. Cross-correlation and optimization of the implemented descriptor values have enabled us to build a model (Bac2a-#2) that was able to correctly predict the activity of 84% of the tested peptides, within a twofold deviation window of the corresponding IC50 Values, measured earlier. The predictive power, is an average of 10 submodels, each predicting the activity of 20 randomly excluded peptides, with a predictive success of 16.7 +/- 1.6 peptides. The model has also been proven significantly more accurate than a simpler model (Bac2a- #1), where the inductive and conventional quantitative structure-activity relationship descriptors were excluded.