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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19699377 |
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Table of Contents:
- <p class="ds-markdown-paragraph"><strong>Title:</strong></p> <blockquote> <p class="ds-markdown-paragraph">Hemolytik 2.0 Database – Complete Dataset of Hemolytic and Non-Hemolytic Peptides</p> </blockquote> <p class="ds-markdown-paragraph"><strong>Description:</strong></p> <blockquote> <p class="ds-markdown-paragraph"><strong>Project:</strong> Hemolytik 2.0</p> <p class="ds-markdown-paragraph"><strong>Publication:</strong> Singh, A., Raj SA, K., Rathore, A.S., & Raghava, G.P.S. (2025). Hemolytic 2: An Updated Database of Hemolytic Peptides and Proteins. <em>ACS Chemical Research in Toxicology</em>. <a href="https://doi.org/10.1021/acs.chemrestox.5c00322" rel="noopener noreferrer">https://doi.org/10.1021/acs.chemrestox.5c00322</a></p> <p class="ds-markdown-paragraph"><strong>Overview:</strong> This dataset is the release accompanying the Hemolytik 2.0 database publication. It contains 13,215 systematically curated entries of experimentally validated hemolytic and non-hemolytic peptides, providing a comprehensive resource for peptide drug development and computational toxicology.</p> <p class="ds-markdown-paragraph"><strong>Content:</strong> The data includes unique peptide sequences (7,534), their amino acid sequences, hemolytic activity measurements, biological source, stereochemistry, terminal modifications, structural classification (linear/cyclic), and predicted tertiary structures (where available). The dataset also includes peptides associated with antimicrobial, anticancer, and other bioactivities.</p> <p class="ds-markdown-paragraph"><strong>Data Curation:</strong> Information was manually extracted from 1,645 peer-reviewed articles published between 2013 and 2024, as well as major peptide databases (APD3, UniProt, CAMP-R4, DRAMP 4.0). The complete methodology is detailed in the associated publication.</p> <p class="ds-markdown-paragraph"><strong>Usage:</strong> This dataset is designed for large-scale analyses, including machine learning model training, Quantitative Structure-Activity Relationship (QSAR) studies, and the rational design of non-hemolytic therapeutic peptides. It supports researchers in identifying and optimizing peptide candidates with improved safety profiles.</p> <p class="ds-markdown-paragraph"><strong>License:</strong> CC BY 4.0</p> <p class="ds-markdown-paragraph"><strong>Contact:</strong> raghava@iiitd.ac.in</p> </blockquote>