Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Abstract

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called “AntiMPmod” which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).

Computing wide range of protein/peptide features from their sequence and structure

“…Recently, a method has been developed for predicting antimicrobial activity of chemically modified peptides (Agrawal and Raghava, 2018). In this study, atom and bond profiles have been used for creating features for chemically modified peptides.…”

“…Likewise, several methods to predict therapeutic properties of a protein or peptide such as anti-cancer, anti-microbial, anti-bacterial, anti-fungal, anti-tubercular, anti-hypertensive, toxic, tumor homing etc. have been developed (Manavalan et al, 2017;Tyagi et al, 2013;Meher et al, 2017;Lata, Mishra, and G. P. S. Raghava, 2010;Agrawal et al, 2018;Usmani, Bhalla, et al, 2018;Kumar et al, 2015;Manavalan et al, 2018;Gupta et al, 2013;Sharma et al, 2013). These peptides/proteins have therapeutic properties thus play vital role in designing proteins/peptide-based drugs and vaccines (Dhanda et al, 2017;Usmani, Kumar, et al, 2018;Nagpal et al, 2017).…”

“…Thus, computing descriptors of these chemically modified peptides is a challenge as amino acid sequence information is not adequate to represent these peptides. In past methods like AntiMPmod (Agrawal and Raghava, 2018), CellPPDmod have been developed for predicting therapeutic properties of chemically modified peptides. In these methods, structural and chemical based descriptors have been used for developing prediction models.…”

SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

“…For example, residue 'A' was represented by 1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0; which contains 20 amino acids and one dummy amino acid 'X'. X was represented by 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 (Agrawal, Kumar, et al, 2019;Agrawal and Raghava, 2018).…”

Scrutinizing surface glycoproteins and poxin-schlafen protein to design a heterologous recombinant vaccine against monkeypox virus

“…In-silico study is also necessary for immunoinformatics approach, reducing cost and time in development process of drug (Hasan et al, 2019a;Adhikari et al, 2018;Mohammed et al, 2017;Dash et al, 2017). Different genome based innovations have empowered practically dazzle choice of vaccine candidates and permitted expectation of antigens without the necessity to develop the pathogen in vitro (Rappuoli, 2000;Sette and Fikes, 2003;Agrawal and Raghava, 2018). Subsequently, we in this connected a few bioinformatics database, tools and computer program to plan a thermostable and profoundly immunogenic, multi-epitope peptide vaccine against Monkeypox virus utilizing their preserved locale protein arrangements.…”