title: Designing antimicrobial peptides with weighted finite-state transducers.
abstract: The design of novel antimicrobial peptides (AMPs) is an important problem given the rise of drug-resistant bacteria. However, the large size of the sequence search space, combined with the time required to experimentally test or simulate AMPs at the molecular level makes computational approaches based on sequence analysis attractive. We propose a method for designing novel AMPs based on learning from n-gram counts of classes of amino acid residues, and then using weighted finite-state machines to produce sequences that incorporate those features that are strongly associated with AMP sequences. Finite-state machines are able to generate sequences that include desired n-gram features. We use this approach to generate candidate novel AMPs, which we test using third-party prediction servers. We demonstrate that our framework is capable of producing large numbers of novel peptide sequences that share features with known antimicrobial peptides.
authors.affiliation: Division of Biomedical Computer Science and Center for Spoken Language Understanding, Oregon Health & Science University, Portland, OR, USA. whelanch@ohsu.edu
authors.forename: Christopher
authors.initials: C
authors.lastname: Whelan
authors.forename: Brian
authors.initials: B
authors.lastname: Roark
authors.forename: Kemal
authors.initials: K
authors.lastname: Sönmez
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D023181:Antimicrobial Cationic Peptides / Q000138:chemical synthesis*; D001185:Artificial Intelligence; D003198:Computer Simulation; D015195:Drug Design; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D014159:Transducers
chemical_list: D023181:Antimicrobial Cationic Peptides