Computational peptide discovery with a genetic programming approach.

ABSTRACT: The development of peptides for therapeutic targets or biomarkers for disease diagnosis is a challenging task in protein engineering. Current approaches are tedious, often time-consuming and require complex laboratory data due to the vast search spaces that need to be considered. In silico methods can accelerate research and substantially reduce costs. Evolutionary algorithms are a promising approach for exploring large search spaces and can facilitate the discovery of new peptides. This study presents the development and use of a new variant of the genetic-programming-based POET algorithm, called POET Regex , where individuals are represented by a list of regular expressions. This algorithm was trained on a small curated dataset and employed to generate new peptides improving the sensitiv

SUBMITTER: Scalzitti N 

PROVIDER: S-EPMC11416381 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature