The dynamic landscape of peptide activity prediction

Elsevier

Available online 24 November 2022

Computational and Structural Biotechnology JournalAuthor links open overlay panelAbstract

Peptides are known to possess a plethora of beneficial properties and activities: antimicrobial, anticancer, anti-inflammatory or the ability to cross the blood-brain barrier are only a few examples of their functional diversity. For this reason, bioinformaticians are constantly developing and upgrading models to predict their activity in silico, generating a steadily increasing number of available tools. Although these efforts have provided fruitful outcomes in the field, the vast and diverse amount of resources for peptide prediction can turn a simple prediction into an overwhelming searching process to find the optimal tool. This minireview aims at providing a systematic and accessible analysis of the complex ecosystem of peptide activity prediction, showcasing the variability of existing models for peptide assessment, their domain specialization and popularity. Moreover, we also assess the reproducibility of such bioinformatics tools and describe tendencies observed in their development. The list of tools is available under https://biogenies.info/peptide-prediction-list/.

Keywords

peptides

activity

functional peptides, prediction

machine learning

deep learning

reproducibility

© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

留言 (0)

沒有登入
gif