Prediction of antimicrobial peptides using hyperparameter optimized support vector machines

• Main Author: Gabere, Musa Nur
• Other Authors: Vladimir, Bajic
• Language: en
• Published: University of the Western Cape 2014
• Subjects: Antimicrobial peptides; Innate immune; Machine learning; Pattern search; Simulated annealing; Support vector machine; Global optimization; Database; Insect; Glossina morsistan
• Online Access: http://hdl.handle.net/11394/2633

Philosophiae Doctor - PhD === Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae. === South Africa