TY - JOUR

T1 - The dynamic landscape of peptide activity prediction

AU - Bárcenas, Oriol

AU - Pintado-Grima, Carlos

AU - Sidorczuk, Katarzyna

AU - Teufel, Felix

AU - Nielsen, Henrik

AU - Ventura, Salvador

AU - Burdukiewicz, Michał

N1 - Publisher Copyright: © 2022

PY - 2022

Y1 - 2022

N2 - 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/.

AB - 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/.

KW - Activity

KW - Deep learning

KW - Functional peptides

KW - Machine learning

KW - Peptides

KW - Prediction

KW - Reproducibility

U2 - 10.1016/j.csbj.2022.11.043

DO - 10.1016/j.csbj.2022.11.043

M3 - Review

C2 - 36467580

AN - SCOPUS:85142854577

VL - 20

SP - 6526

EP - 6533

JO - Computational and Structural Biotechnology Journal

JF - Computational and Structural Biotechnology Journal

SN - 2001-0370

ER -