The dynamic landscape of peptide activity prediction
Bárcenas, Oriol (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Pintado-Grima, Carlos (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Sidorczuk, Katarzyna (University of Wrocław)
Teufel, Felix (University of Copenhagen)
Nielsen, Henrik (Technical University of Denmark)
Ventura, Salvador (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Burdukiewicz, Michał (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")

Date:	2022
Abstract:	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.
Grants:	Agència de Gestió d'Ajuts Universitaris i de Recerca 2021/FI_B-00087 Agencia Estatal de Investigación PID2019-105017RB-I00
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Peptides ; Activity ; Prediction ; Functional peptides ; Machine learning ; Deep learning ; Reproducibility
Published in:	Computational and Structural Biotechnology Journal, Vol. 20 (November 2022) , p. 6526-6533, ISSN 2001-0370

DOI: 10.1016/j.csbj.2022.11.043
PMID: 36467580

8 p, 1.1 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut de Biotecnologia i de Biomedicina (IBB)
Articles > Research articles
Articles > Published articles