Abstract:
Bacterial resistance to antibiotics continues to be a serious concern worldwide. This has motivated a strong research focus on naturally-occurring antimicrobial peptides (AMPs) as templates for new drug development. To date, experiments in the wet laboratory have characterized thousands of AMPs while generally concentrating on measures of antibacterial activity for natural sequences or peptides designed using a limited number of site-directed mutations. Based on these findings, the computational AMP research community seeks to better understand how biological signals and features relate to antimicrobial activity through the use of machine learning and statistical approaches. In this dissertation, we advance our understanding of the determinants for antimicrobial activity by carefully constructing a set of descriptive features for use in AMP classification models. In addition to using physicochemical features, we also construct new sequence-based features which capture information about distal patterns within a peptide. Comparative analysis with state-of-the-art methods in AMP recognition reveal our methods to be among the top performers while still providing a transparent summary of relative feature importance. Moreover, this dissertation applies our features in a new setting to demonstrate for the first time a computational model to recognize if an AMP may perform better against a representative Gram-positive or Gram-negative bacteria. Work presented is a step forward for in silico research seeking to help guide AMP design in the wet laboratory. Our predictive models are made accessible via AMP Scanner, a new publicly-available web server at: http://www.ampscanner.com.
