Physicochemical Feature Selection for Cathelicidin Antimicrobial Peptides

PHYSICOCHEMICAL FEATURE SELECTION FOR CATHELICIDIN ANTIMICROBIAL PEPTIDES Daniel Paul Veltri, M.S. George Mason University, 2013 Thesis Director: Dr. Amarda Shehu Due to recent attention on antimicrobial peptides (AMPs) as targets for antibacterial drug research, many machine learning methods are now turning their attention to AMP recognition. Approaches that rely on whole-peptide properties for recognition are challenged by the great sequence diversity among AMPs for effective feature construction. This thesis proposes a novel and complementary method for feature construction which relies on an extensive list of position-based amino acid physicochemical properties. These features are shown effective in the context of classification by support vector machine (SVM), both in comparison to related work in recognition of AMPs and in a novel study on the cathelicidin family. A detailed analysis and careful construction of a decoy dataset allows for the highlighting of antimicrobial activity-related features in cathelicidins. Special attention is also given to residue positions involved with enzymatic cleavage. The method presented in this thesis is a first step towards understanding what confers to cathelicidins their activity at the physicochemical level and may prove useful for future AMP design efforts. Chapter 1: Introduction and Background 1.1 Motivation: Antibiotic Resistance Drug-resistance in bacteria continues to be a growing problem around the world [1–3]. Penicillin, the first major clinical antibiotic, experienced widespread resistance in Western hospitals only a few years after its introduction in 1945 [4, 5]. Ten of the major classes of antibiotics (penicillin, chloramphenicol, erythromycin, methicillin, 1st-3rd generation cephalothin, vancomycin, carbapenems and linezolid), have seen resistance observed only an average of ∼ 5.6 years after first introduction [1]. Some proposed causes for the speed at which bacteria have managed this feat include: lateral gene transfer [1, 5], ineffectual clinical prescription regimes [6], and overuse in agriculture for livestock [7]. With cases of difficult-to-treat infections such as methicillin-resistant S. aureus (MRSA) on the rise, there has been a series of urgent calls from the World Health Organization for the development of new antibiotics [3]. Computational studies provide an opportunity to aid this effort through data analysis and hypothesis generation methods to assist in novel drug development. 1.2 Problem Statement The goal of this thesis is to obtain a better understanding of how physicochemical properties (collectively referred to as “features” throughout this work) relate to a naturally occurring family of antimicrobial peptides (AMPs) with innate activity. This thesis details a novel process for characterizing an extensive list of position-based amino acid features. It then utilizes machine learning and feature selection to rank their relative importance for 1 AMP activity. While the method can be modified for use with any type of AMP, focus is placed here on cathelicidinsan important family of helical AMPs found in humans with great potential for new drug development applications. At the time of this writing, the findings of this thesis have yet to be verified through experiment and should be considered theoretical in nature until their biological relevance has been confirmed.