Proteomic Screening for Prediction and Design of Antimicrobial Peptides with AmpGram.

Michał Burdukiewicz,Dominik Rafacz,Jarosław Chilimoniuk,Stefan Rödiger,Katarzyna Sidorczuk,Przemysław Gagat,Filip Pietluch

doi:10.3390/ijms21124310

Abstract

Antimicrobial peptides (AMPs) are molecules widespread in all branches of the tree of life that participate in host defense and/or microbial competition. Due to their positive charge, hydrophobicity and amphipathicity, they preferentially disrupt negatively charged bacterial membranes. AMPs are considered an important alternative to traditional antibiotics, especially at the time when multidrug-resistant bacteria being on the rise. Therefore, to reduce the costs of experimental research, robust computational tools for AMP prediction and identification of the best AMP candidates are essential. AmpGram is our novel tool for AMP prediction; it outperforms top-ranking AMP classifiers, including AMPScanner, CAMPR3R and iAMPpred. It is the first AMP prediction tool created for longer AMPs and for high-throughput proteomic screening. AmpGram prediction reliability was confirmed on the example of lactoferrin and thrombin. The former is a well known antimicrobial protein and the latter a cryptic one. Both proteins produce (after protease treatment) functional AMPs that have been experimentally validated at molecular level. The lactoferrin and thrombin AMPs were located in the antimicrobial regions clearly detected by AmpGram. Moreover, AmpGram also provides a list of shot 10 amino acid fragments in the antimicrobial regions, along with their probability predictions; these can be used for further studies and the rational design of new AMPs. AmpGram is available as a web-server, and an easy-to-use R package for proteomic analysis at CRAN repository.

Highlights

Abuse and overuse of antibiotics in human health care and animal breeding has greatly contributed to a worldwide resistance to antibiotics
AmpGram is a novel antimicrobial peptides (AMPs) predictor that uses n-grams to represent information hidden in amino acid sequences and random forests as the classification algorithm
To the best of our knowledge, AmpGram is the first AMP classifier created for the prediction of longer AMPs and high-throughput proteomic screening

Summary

Introduction

Abuse and overuse of antibiotics in human health care and animal breeding has greatly contributed to a worldwide resistance to antibiotics. Multidrug-resistant bacteria, the so-called ’superbugs’, threaten our ability to tackle even common infectious diseases, resulting in prolonged illnesses and death of tens of thousands of people only in Europe and the United States [3,4]. AMPs, known as cationic host defense peptides, are molecules that are widespread in all branches of the tree of life [5]. In multicellular organisms, they participate in the first line of defense against bacteria, fungi, protozoans and viruses, and can even target cancer cells [6,7]. I.e., unicellular eukaryotes, bacteria and archaeans, they are used for self-protection and microbial competition [11,12,13]

Objectives

Methods

Results

Conclusion