Leveraging a Meta-Learning Strategy to Advance the Accuracy of Neutralizing Antibodies against Dengue Virus Serotype Prediction.

ABSTRACT: Dengue remains a major global public health threat with millions of infections reported annually and no widely effective treatment options available. Therapeutic monoclonal antibodies, particularly broadly neutralizing antibodies (bNAbs), show promise as treatments due to their ability to target conserved viral epitopes across all DENV serotypes. However, characterizing dengue virus (DENV) through experimental approaches remains expensive and time-consuming. Therefore, computational methods capable of identifying bNAbs against DENV from antibodies (CDR-H3) and epitope information can greatly complement experimental approaches and facilitate the rapid screening of bNAbs. Here, we propose an innovative meta-learning approach, termed Meta-iNAb, that can accurately identify bNAbs against DENV

SUBMITTER: Charoenkwan P 

PROVIDER: S-EPMC12750260 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature