Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria
Predictive Model of Linear Antimicrobial Peptides Active against Gram-Negative Bacteria
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DOI：
10.1021/acs.jcim.8b00118
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发表时间：
2018-05-01
影响因子：
5.6
通讯作者：
Pirtskhalava, Malak
中科院分区：
文献类型：
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作者：
Vishnepolsky, Boris;Gabrielian, Andrei;Pirtskhalava, Malak
抗菌肽（AMP）已被确定为一类潜在的新型抗感染药物，可用于药物开发。有很多计算方法试图预测 AMP。他们中的大多数只能预测肽是否会显示任何抗菌效力，但据我们所知，没有工具可以预测针对特定菌株的抗菌效力。在这里，我们提出了一个线性 AMP 对特定革兰氏阴性菌株具有活性的预测模型，该模型依赖于半监督机器学习方法和基于密度的聚类算法。该算法可以很好地区分对特定菌株具有活性的肽与其他可能具有活性但对所考虑的菌株不具有活性的肽。可用的 AMP 预测工具无法执行此任务。基于本文建议的算法的预测工具可在 https://dbaasp.org 上找到
Antimicrobial peptides (AMPs) have been identified as a potential new class of anti-infectives for drug development. There are a lot of computational methods that try to predict AMPs. Most of them can only predict if a peptide will show any antimicrobial potency, but to the best of our knowledge, there are no tools which can predict antimicrobial potency against particular strains. Here we present a predictive model of linear AMPs being active against particular Gram-negative strains relying on a semi-supervised machine-learning approach with a density-based clustering algorithm. The algorithm can well distinguish peptides active against particular strains from others which may also be active but not against the considered strain. The available AMP prediction tools cannot carry out this task. The prediction tool based on the algorithm suggested herein is available on https://dbaasp.org