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.
