The dynamic landscape of peptide activity prediction

Oriol Bárcenas, Carlos Pintado-Grima, Katarzyna Sidorczuk, Felix Teufel, Henrik Nielsen, Salvador Ventura*, Michał Burdukiewicz

*Corresponding author for this work

Research output: Contribution to journalReview articleResearchpeer-review

2 Citations (Scopus)


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

Original languageEnglish
Pages (from-to)6526-6533
Number of pages8
JournalComputational and Structural Biotechnology Journal
Publication statusPublished - Jan 2022


  • Activity
  • Deep learning
  • Functional peptides
  • Machine learning
  • Peptides
  • Prediction
  • Reproducibility


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