Abstract：
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in clinical use. To accelerate the discovery of new antibiotics, it is useful to predict novel AMPs from the sequenced genomes of various organisms. The antimicrobial peptide database (APD) provided the first empirical peptide prediction program. It also facilitated the testing of the first machine-learning algorithms. This chapter provides an overview of machine-learning predictions of AMPs. Most of the predictors, such as AntiBP, CAMP, and iAMPpred, involve a single-label prediction of antimicrobial activity. This type of prediction has been expanded to antifungal, antiviral, antibiofilm, anti-TB, hemolytic, and anti-inflammatory peptides. The multiple functional roles of AMPs annotated in the APD also enabled multi-label predictions (iAMP-2L, MLAMP, and AMAP), which include antibacterial, antiviral, antifungal, antiparasitic, antibiofilm, anticancer, anti-HIV, antimalarial, insecticidal, antioxidant, chemotactic, spermicidal activities, and protease inhibiting activities. Also considered in predictions are peptide posttranslational modification, 3D structure, and microbial species-specific information. We compare important amino acids of AMPs implied from machine learning with the frequently occurring residues of the major classes of natural peptides. Finally, we discuss advances, limitations, and future directions of machine-learning predictions of antimicrobial peptides. Ultimately, we may assemble a pipeline of such predictions beyond antimicrobial activity to accelerate the discovery of novel AMP-based antimicrobials.
摘要：
抗生素耐药性构成全球威胁，并可能导致未来的大流行。一种策略是开发新一代的抗微生物剂。天然存在的抗微生物肽(AMP)是公认的模板,并且一些已经在临床上使用。为了加速新抗生素的发现，从各种生物体的测序基因组中预测新的AMP是有用的。抗微生物肽数据库(APD)提供了第一个经验肽预测程序。它还促进了第一批机器学习算法的测试。本章概述了AMPs的机器学习预测。大多数预测因子，比如AntiBP,CAMP,和iAMPpred，涉及抗菌活性的单标签预测。这种类型的预测已经扩展到抗真菌药，抗病毒，抗生物膜,抗结核,溶血,和抗炎肽。APD中注释的AMP的多个功能角色也启用了多标签预测(iAMP-2L，MLAMP,和AMAP），其中包括抗菌，抗病毒，抗真菌药,抗寄生虫，抗生物膜,抗癌，抗艾滋病毒，抗疟药,杀虫,抗氧化剂，趋化,杀精子活性，和蛋白酶抑制活性。在预测中还考虑了肽翻译后修饰，3D结构,和微生物物种特异性信息。我们将机器学习中隐含的AMP的重要氨基酸与主要天然肽类的频繁残基进行了比较。最后,我们讨论进步，局限性,以及抗菌肽的机器学习预测的未来方向。最终,除了抗菌活性之外,我们可能会收集一系列此类预测,以加速新型AMP基抗菌药物的发现.
