Dataset of manually curated peptides against arthropod-borne viruses

Anti-arboviral peptides are biomolecules capable of interfering with key stages of the arboviral lifecycle. We present a dataset compiling 270 peptides composed of standard amino acids with reported activity against arboviruses, along with their lengths, sequences, target specificities, peptide entry mechanisms, and interaction mechanisms. The dataset was structured to support the training of machine learning models designed to identify anti-arboviral peptides. Therefore, it facilitates the development and validation of predictive tools against arboviruses. As a carefully assembled and expert-reviewed resource, this dataset aims to serve as a reference standard for evaluating new prediction models or comparing them with automatically compiled peptide datasets. This resource provides a comprehensive overview of the antiviral mechanisms currently being explored and can serve as a foundation for designing next-generation peptides targeting arboviral infections.

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Publication Details

Journal
Data in Brief
Published
2026-04-15
DOI
https://doi.org/10.1016/j.dib.2026.112765
Primary Topic
Antimicrobial Peptides and Activities
Type
article
Field-Weighted Citation Impact
0.00

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article

Dataset of manually curated peptides against arthropod-borne viruses

Santiago Di Lella, Víctor Hermosilla-Mechetti, Helena Gomez-Adorno, Christian Schaerer et al.
Data in Brief
Antimicrobial Peptides and Activities
article

Dataset of manually curated peptides against arthropod-borne viruses

Santiago Di Lella, Víctor Hermosilla-Mechetti, Helena Gomez-Adorno, Christian Schaerer, Diego P. Pinto-Roa, Jose Colbes
article en

Abstract

Anti-arboviral peptides are biomolecules capable of interfering with key stages of the arboviral lifecycle. We present a dataset compiling 270 peptides composed of standard amino acids with reported activity against arboviruses, along with their lengths, sequences, target specificities, peptide entry mechanisms, and interaction mechanisms. The dataset was structured to support the training of machine learning models designed to identify anti-arboviral peptides. Therefore, it facilitates the development and validation of predictive tools against arboviruses. As a carefully assembled and expert-reviewed resource, this dataset aims to serve as a reference standard for evaluating new prediction models or comparing them with automatically compiled peptide datasets. This resource provides a comprehensive overview of the antiviral mechanisms currently being explored and can serve as a foundation for designing next-generation peptides targeting arboviral infections.

Data in BriefVol. 66
Fundación Ciencias Exactas y Naturales (AR), Universidad Nacional de Asunción (PY), Universidad Nacional Autónoma de México (MX)
Consejo Nacional de Ciencia y Tecnología, Paraguay
Zero hunger
Openalex Percentile: Top 25%
Antimicrobial Peptides and Activities
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