Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning
Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning
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DOI：
10.1021/jm8015365
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发表时间：
2009-04-09
影响因子：
7.3
通讯作者：
Cherkasov, Artem
中科院分区：
文献类型：
--
作者：
Fjell, Christopher D.;Jenssen, Havard;Cherkasov, Artem
抗生素耐药性病原体的增加是最紧迫的全球健康问题之一。发现新的抗生素类别并没有跟上步伐;新的代理人往往遭受交叉耐药性的现有代理人的类似结构。具有抗微生物活性的短的阳离子肽对于许多生物体的宿主防御是必不可少的，并且代表了一类有前途的新型抗微生物剂。本文报道了使用QSAR和机器学习技术的组合成功地在计算机上筛选有效的抗生素肽。在超过1400个随机肽的活性的初始高通量测量的基础上，使用QSAR描述符构建人工神经网络模型，随后用于筛选约100，000个肽的计算机文库。模型的体外验证表明，在识别高活性肽方面的准确率为94%。通过筛选鉴定的最佳肽被发现具有与四种常规抗生素相当或上级的活性，并且上级于针对广泛的多重耐药人类病原体的临床开发中最先进的肽。
The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to those of four conventional antibiotics and superior to the peptide most advanced in clinical development against a broad array of multiresistant human pathogens.