Novel 3D Structure Based Model for Activity Prediction and Design of Antimicrobial Peptides

Shicai Liu,Xingzhen Lao,Jingxiao Bao,Heng Zheng

doi:10.1038/s41598-018-29566-5

Shicai Liu, Xingzhen Lao + Show 2 more

Open Access

https://doi.org/10.1038/s41598-018-29566-5

Copy DOI

Journal: Scientific Reports	Publication Date: Jul 25, 2018
Citations: 39	License type: open-access

Affiliation: China Pharmaceutical University

Abstract

The emergence and worldwide spread of multi-drug resistant bacteria makes an urgent challenge for the development of novel antibacterial agents. A perspective weapon to fight against severe infections caused by drug-resistant microorganisms is antimicrobial peptides (AMPs). AMPs are a diverse class of naturally occurring molecules that are produced as a first line of defense by all multi-cellular organisms. Limited by the number of experimental determinate 3D structure, most of the prediction or classification methods of AMPs were based on 2D descriptors, including sequence, amino acid composition, peptide net charge, hydrophobicity, amphiphilic, etc. Due to the rapid development of structural simulation methods, predicted models of proteins (or peptides) have been successfully applied in structure based drug design, for example as targets of virtual ligand screening. Here, we establish the activity prediction model based on the predicted 3D structure of AMPs molecule. To our knowledge, it is the first report of prediction method based on 3D descriptors of AMPs. Novel AMPs were designed by using the model, and their antibacterial effect was measured by in vitro experiments.

Highlights

There are more than 2,500 antimicrobial peptides (AMPs) found in nature[10], such as single-celled organisms, plants, insects, animals
Molecular dynamics simulations for 84 peptides have been performed, and we establish the activity prediction model based on the predicted 3D structure of the AMPs molecule
The performance of mean decrease in accuracy (MDA)-support vector machine (SVM) with CDHit (7) based model was measured with an accuracy of 92.59% and a Mathew’s Correlation Coefficient (MCC) of 0.84 on the training and testing dataset

Summary

Introduction

There are more than 2,500 AMPs found in nature[10], such as single-celled organisms, plants, insects, animals. Most of the activity prediction models are established based on the primary structure of AMPs14–18, the amino acid composition, peptide net charge, hydrophobicity, amphiphilic, helix and other structural parameters are all critical for AMPs’ activity. Molecular dynamics simulations for 84 peptides have been performed, and we establish the activity prediction model based on the predicted 3D structure of the AMPs molecule. To our knowledge, it is the first report of prediction method based on 3D descriptors of AMPs. Novel AMPs were designed by using the model, and their antibacterial effect was measured by in vitro experiments

Methods

Results

Conclusion