Characterization and identification of antimicrobial peptides with different functional activities

Chia-Ru Chung; Ting-Rung Kuo; Li-Ching Wu; Tzong-Yi Lee; Jorng-Tzong Horng

doi:10.1093/bib/bbz043

Characterization and identification of antimicrobial peptides with different functional activities

Brief Bioinform. 2019 Jun 3:bbz043. doi: 10.1093/bib/bbz043. Online ahead of print.

Authors

Chia-Ru Chung¹, Ting-Rung Kuo¹, Li-Ching Wu², Tzong-Yi Lee^{3

4

5}, Jorng-Tzong Horng^{1

6}

Affiliations

¹ Department of Computer Science and Information Engineering, National Central University, Taoyuan, Taiwan.
² Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan.
³ School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.
⁴ School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen, China.
⁵ Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, China.
⁶ Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.

PMID: 31155657
DOI: 10.1093/bib/bbz043

Abstract

In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues of proteins are dominant against infections. Importantly, AMPs are produced by virtually all known living organisms and are able to target a wide range of pathogenic microorganisms, including viruses, parasites, bacteria and fungi. Although several studies have proposed different machine learning methods to predict peptides as being AMPs, most do not consider the diversity of AMP activities. On this basis, we specifically investigated the sequence features of AMPs with a range of functional activities, including anti-parasitic, anti-viral, anti-cancer and anti-fungal activities and those that target mammals, Gram-positive and Gram-negative bacteria. A new scheme is proposed to systematically characterize and identify AMPs and their functional activities. The 1st stage of the proposed approach is to identify the AMPs, while the 2nd involves further characterization of their functional activities. Sequential forward selection was employed to extract potentially informative features that are possibly associated with the functional activities of the AMPs. These features include hydrophobicity, the normalized van der Waals volume, polarity, charge and solvent accessibility-all of which are essential attributes in classifying between AMPs and non-AMPs. The results revealed the 1st stage AMP classifier was able to achieve an area under the receiver operating characteristic curve (AUC) value of 0.9894. During the 2nd stage, we found pseudo amino acid composition to be an informative attribute when differentiating between AMPs in terms of their functional activities. The independent testing results demonstrated that the AUCs of the multi-class models were 0.7773, 0.9404, 0.8231, 0.8578, 0.8648, 0.8745 and 0.8672 for anti-parasitic, anti-viral, anti-cancer, anti-fungal AMPs and those that target mammals, Gram-positive and Gram-negative bacteria, respectively. The proposed scheme helps facilitate biological experiments related to the functional analysis of AMPs. Additionally, it was implemented as a user-friendly web server (AMPfun, http://fdblab.csie.ncu.edu.tw/AMPfun/index.html) that allows individuals to explore the antimicrobial functions of peptides of interest.

Keywords: antimicrobial peptide; functional activity; machine learning; prediction tools.