Data-driven discovery of antiviral peptides against PRRSV using multiple machine learning models
Integrating proteomics with computational modeling revealed peptides with antiviral potential against PRRSV. The RF model demonstrated the best discriminative power, and amino acid composition played a key predictive role. Consequently, these comparative predictive results may serve as revolutionized and distinctive resources for the experimental validation and identification of PRRSV AVPs as prospective therapeutics.
Authors
- Y. P. Shen (ORCID: https://orcid.org/0000-0003-4124-6034)
- Nairui Huo (ORCID: https://orcid.org/0000-0001-6046-0699)
- Kuohai Fan (ORCID: https://orcid.org/0000-0003-1333-7270)
- Wafa Yousaf
- Hua Zhang (ORCID: https://orcid.org/0000-0003-2875-7243)
- Jia Zhong (ORCID: https://orcid.org/0000-0001-5465-5094)
- Hongquan Li (ORCID: https://orcid.org/0000-0002-2030-5587)
- Wei Yin (ORCID: https://orcid.org/0000-0002-5100-2132)
- Abdul Haseeb (ORCID: https://orcid.org/0000-0003-2455-5054)
- Zhenbiao Zhang (ORCID: https://orcid.org/0000-0002-2823-3354)
- Na Sun (ORCID: https://orcid.org/0000-0002-5894-8246)
- Huizhen Yang
- Panpan Sun (ORCID: https://orcid.org/0000-0002-0681-6145)
- Jianzhong Wang
- Yaogui Sun
Institutions
- Shanxi Agricultural University (CN)
Publication Details
- Journal
- Frontiers in Veterinary Science
- Published
- 2025-12-05
- DOI
- https://doi.org/10.3389/fvets.2025.1681083
- Primary Topic
- Animal Virus Infections Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China
- Shanxi Provincial Key Research and Development Project