Recent trends in antimicrobial peptide prediction using machine learning techniques.
Recent trends in antimicrobial peptide prediction using machine learning techniques.
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DOI：
10.6026/97320630013415
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发表时间：
2017
期刊：
影响因子：
1.9
通讯作者：
Valadi JK
中科院分区：
文献类型：
--
作者：
Shah Y;Sehgal D;Valadi JK
近年来，由于微生物耐药性的增加，开发已知抗生素的有效替代品的重要性日益增强。因此，抗菌肽（Antimicrobial Peptides, AMPs）的预测、设计和计算模型具有重要意义。amp是具有不同大小（从5到超过100个残基）的寡肽，在先天免疫中起关键作用。因此，AMPs作为新型治疗剂的开发潜力是显而易见的。它们的作用是通过抑制细胞外聚合物合成或改变细胞内聚合物功能来破坏微生物膜，从而导致细胞死亡。amp具有广谱活性，是抵御所有类型微生物的第一道防线，包括病毒、细菌、寄生虫、真菌以及癌症（不受控制的细胞分裂）的进展。amp的大规模鉴定和提取通常是不平凡的、昂贵的和耗时的。因此，有必要开发模型来预测amp作为治疗药物。我们记录了AMP预测的最新趋势和进展。
The importance to develop effective alternatives to known antibiotics due to increased microbial resistance is gaining momentum in recent years. Therefore, it is of interest to predict, design and computationally model Antimicrobial Peptides (AMPs). AMPs are oligopeptides with varying size (from 5 to over100 residues) having key role in innate immunity. Thus, the potential exploitation of AMPs as novel therapeutic agents is evident. They act by causing cell death either by disrupting the microbial membrane by inhibiting extracellular polymer synthesis or by altering intra cellular polymer functions. AMPs have broad spectrum activity and act as first line of defense against all types of microorganisms including viruses, bacteria, parasites, fungi and as well as cancer (uncontrolled celldivision) progression. Large-scale identification and extraction of AMPs is often non-trivial, expensive and time consuming. Hence, there is a need to develop models to predict AMPs as therapeutics. We document recent trends and advancement in the prediction of AMP.