Progress in the Development of Antimicrobial Peptide Prediction Tools

Title:Progress in the Development of Antimicrobial Peptide Prediction Tools

Volume: 22 Issue: 3

Author(s): Chunyan Ao , Yu Zhang , Dapeng Li , Yuming Zhao * Quan Zou *

Affiliation:

• Information and Computer Engineering College, Northeast Forestry University, Harbin, Heilongjiang, 150001,China

• Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu,China

Keywords: Antimicrobial peptides, machine learning, support vector machine, random forest, artificial neural network, AMPs.

Abstract: Antimicrobial peptides (AMPs) are natural polypeptides with antimicrobial activities and are found in most organisms. AMPs are evolutionarily conservative components that belong to the innate immune system and show potent activity against bacteria, fungi, viruses and in some cases display antitumor activity. Thus, AMPs are major candidates in the development of new antibacterial reagents. In the last few decades, AMPs have attracted significant attention from the research community. During the early stages of the development of this research field, AMPs were experimentally identified, which is an expensive and time-consuming procedure. Therefore, research and development (R&D) of fast, highly efficient computational tools for predicting AMPs has enabled the rapid identification and analysis of new AMPs from a wide range of organisms. Moreover, these computational tools have allowed researchers to better understand the activities of AMPs, which has promoted R&D of antibacterial drugs. In this review, we systematically summarize AMP prediction tools and their corresponding algorithms used.

Cite this article as:

Ao Chunyan , Zhang Yu , Li Dapeng , Zhao Yuming *, Zou Quan *, Progress in the Development of Antimicrobial Peptide Prediction Tools, Current Protein & Peptide Science 2021; 22 (3) . https://dx.doi.org/10.2174/1389203721666200117163802

DOI https://dx.doi.org/10.2174/1389203721666200117163802	Print ISSN 1389-2037
Publisher Name Bentham Science Publisher	Online ISSN 1875-5550