Version 1
: Received: 17 July 2024 / Approved: 18 July 2024 / Online: 18 July 2024 (09:28:14 CEST)
How to cite:
De Llano García, D.; Marrero-Ponce, Y.; Agüero-Chapin, G.; Ferri, F. J.; Antunes, A.; Martinez-Rios, F.; Rodríguez-Cabrera, H. M. Innovative Alignment-Based Method for Antiviral Peptide Prediction. Preprints2024, 2024071476. https://doi.org/10.20944/preprints202407.1476.v1
De Llano García, D.; Marrero-Ponce, Y.; Agüero-Chapin, G.; Ferri, F. J.; Antunes, A.; Martinez-Rios, F.; Rodríguez-Cabrera, H. M. Innovative Alignment-Based Method for Antiviral Peptide Prediction. Preprints 2024, 2024071476. https://doi.org/10.20944/preprints202407.1476.v1
De Llano García, D.; Marrero-Ponce, Y.; Agüero-Chapin, G.; Ferri, F. J.; Antunes, A.; Martinez-Rios, F.; Rodríguez-Cabrera, H. M. Innovative Alignment-Based Method for Antiviral Peptide Prediction. Preprints2024, 2024071476. https://doi.org/10.20944/preprints202407.1476.v1
APA Style
De Llano García, D., Marrero-Ponce, Y., Agüero-Chapin, G., Ferri, F. J., Antunes, A., Martinez-Rios, F., & Rodríguez-Cabrera, H. M. (2024). Innovative Alignment-Based Method for Antiviral Peptide Prediction. Preprints. https://doi.org/10.20944/preprints202407.1476.v1
Chicago/Turabian Style
De Llano García, D., Felix Martinez-Rios and Hortensia María Rodríguez-Cabrera. 2024 "Innovative Alignment-Based Method for Antiviral Peptide Prediction" Preprints. https://doi.org/10.20944/preprints202407.1476.v1
Abstract
Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenge of viral infections and their growing resistance to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised Multi-Query Similarity Search Models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models' robustness and reliability. The top-performing model, M13+, demonstrated impressive results with an accuracy of 0.969 and a Matthew’s correlation coeffi-cient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine learning and deep learning AVP predictors. The MQSSMs outper-formed these predictors, highlighting their efficiency in terms of resource demand and public ac-cessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date.
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
