Title

Recent trends in antimicrobial peptide prediction using machine learning techniques

Authors

Yash Shah¹, Deepak Sehgal², Jayaraman K Valadi^{2, 3*}

Affiliation

¹Department of Computer Engineering, Thadomal Shahani Engineering College, Mumbai- 400050; ²Shiv Nadar University, Gautam Budha Nagar, U.P 201314;

³Center for modelling and simulation, Savitribai Phule Pune university, Pune- 411007;

Email

jayaraman.valadi@snu.edu.in

Article Type

Review

Date

Received December 24, 2017; Revised December 25, 2017; Accepted December 25, 2017; Published December 31, 2017

The importance to develop effective alternatives to known antibiotics due to increased microbial resistance is gaining momentum in recent years. Therefore, it is of interest to predict, design and computationally model Antimicrobial Peptides (AMPs). AMPs are oligopeptides with varying size (from 5 to over100 residues) having key role in innate immunity. Thus, the potential exploitation of AMPs as novel therapeutic agents is evident. They act by causing cell death either by disrupting the microbial membrane by inhibiting extracellular polymer synthesis or by altering intra cellular polymer functions. AMPs have broad spectrum activity and act as first line of defense against all types of microorganisms including viruses, bacteria, parasites, fungi and as well as cancer (uncontrolled celldivision) progression. Large-scale identification and extraction of AMPs is often non-trivial, expensive and time consuming. Hence, there is a need to develop models to predict AMPs as therapeutics. We document recent trends and advancement in the prediction of AMP.

Keywords

Antimicrobial peptide, therapeutics, machine learning

Citation

Shah et al. Bioinformation 13(12): 415-416 (2017)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.