Prediction of Antifungal Activity of Antimicrobial Peptides by Transfer Learning from Protein Pretrained Models
Fernando Lobo, Maily Selena González, Alicia Boto, José Manuel Pérez de la Lastra
Abstract
Peptides with antifungal activity have gained significant attention due to their potential therapeutic applications. In this study, we explore the use of pretrained protein models as feature extractors to develop predictive models for antifungal peptide activity. Various machine learning classifiers were trained and evaluated. Our AFP predictor achieved comparable performance to current state-of-the-art methods. Overall, our study demonstrates the effectiveness of pretrained models for peptide analysis and provides a valuable tool for predicting antifungal peptide activity and potentially other peptide properties.
Topics & Concepts
AntifungalPeptideAntimicrobialMachine learningComputational biologyFeature (linguistics)Antimicrobial peptidesTransfer of learningComputer scienceArtificial intelligenceBiologyBiochemistryMicrobiologyPhilosophyLinguisticsAntimicrobial Peptides and ActivitiesBiochemical and Structural CharacterizationMachine Learning in Bioinformatics