Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

Abstract

Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

Structure–activity relationships of antibacterial peptides

“…On the contrary, antibacterial peptide materials are less susceptible to drug resistance due to their different mechanisms of action and their chemical–physical features. Unfortunately, the production of natural antibacterial peptides still falls in the realm of high‐cost compounds with significant production times concerning the expectancies of high clinical demand (Yan et al, 2022 ). Thus, optimization of their pharmacokinetic and pharmacodynamic characteristics is necessary for the design and synthesis of new feasible antibiotic candidates.…”

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products

“…Experimental results showed that 181 of the 216 identified candidate AMPs showed antimicrobial activity (positive rate of >83%). For a comprehensive review of MLenabled AMP discovery and design, we refer readers to the review by Yan et al 50 Other In a recent study, Rios-Martinez et al pioneered the usage of a self-supervised neural network masked language model called BiGCARP 54 that contains the ByteNet encoder-dilated CNN architecture 55 with linear input embedding and outputdecoding layers for predicting and classifying BGCs from microbial genomes. Trained on 127,000 BGC sequences represented as ESM-1b-pretrained embeddings of a protein family domain, 56 BiGCARP can capture meaningful patterns in BGCs with area under the receiver operating characteristic (AUROC) scores ranging from 0.936 to 0.950 and outperforms DeepBGC on classifying four out of seven product classes.…”

Strategies to Mitigate and Treat Orthopaedic Device-Associated Infections

“…The inclusion of AMPs in device coatings has also been proposed to combat infection. These compounds, described as next-generation antibiotics because they are far less susceptible to the development of pathogen resistance compared to conventional antibiotics, are potent and exhibit rapid and broad-spectrum antibacterial activity [ 112 , 113 ]. AMPs play a key role in natural defence mechanisms and have been isolated from various natural sources including bacteria, fungi, viruses, plants, and animals.…”