Centre for Modeling and Simulation
Savitribai Phule Pune University All Models Are False, Some Are Useful

Technical Report CMS-TR-20171231


Title Recent trends in antimicrobial peptide prediction using machine learning techniques
Author/s Yash Shah
Department of Computer Engineering, Thadomal Shahani Engineering College, Mumbai, India


Deepak Sehgal
Shiv Nadar University, India


VK Jayaraman
Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411 007, India
Abstract 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
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Citing This Document Yash Shah, Deepak Sehgal, and VK Jayaraman , Recent trends in antimicrobial peptide prediction using machine learning techniques . Technical Report CMS-TR-20171231 of the Centre for Modeling and Simulation, Savitribai Phule Pune University, Pune 411007, India (2017); available at http://cms.unipune.ac.in/reports/.
Notes, Published Reference, Etc. Published as Shah et al., Bioinformation 13(12): 415-416 (2017). Available also at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5767919/.
Contact jayaraman AT cms.unipune.ac.in
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