Browsing by Author "Veltri, Daniel Paul"
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Item A Computational and Statistical Framework for Screening Novel Antimicrobial Peptides(2015) Veltri, Daniel Paul; Veltri, Daniel Paul; Shehu, Amarda; Solka, JeffBacterial 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.Item Physicochemical Feature Selection for Cathelicidin Antimicrobial Peptides(2013-08-16) Veltri, Daniel Paul; Veltri, Daniel Paul; Shehu, AmardaDue 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.