Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes
1
Issued Date
2024-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85199774511
Journal Title
Scientific Reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.14 No.1 (2024)
Suggested Citation
Natsrita P., Charoenkwan P., Shoombuatong W., Mahalapbutr P., Faksri K., Chareonsudjai S., Rungrotmongkol T., Pipattanaboon C. Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes. Scientific Reports Vol.14 No.1 (2024). doi:10.1038/s41598-024-67487-8 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/100216
Title
Machine-learning-assisted high-throughput identification of potent and stable neutralizing antibodies against all four dengue virus serotypes
Corresponding Author(s)
Other Contributor(s)
Abstract
Several computational methods have been developed to identify neutralizing antibodies (NAbs) covering four dengue virus serotypes (DENV-1 to DENV-4); however, limitations of the dataset and the resulting performance remain. Here, we developed a new computational framework to predict potent and stable NAbs against DENV-1 to DENV-4 using only antibody (CDR-H3) and epitope sequences as input. Specifically, our proposed computational framework employed sequence-based ML and molecular dynamic simulation (MD) methods to achieve more accurate identification. First, we built a novel dataset (n = 1108) by compiling the interactions of CDR-H3 and epitope sequences with the half maximum inhibitory concentration (IC50) values, which represent neutralizing activities. Second, we achieved an accurately predictive ML model that showed high AUC values of 0.879 and 0.885 by tenfold cross-validation and independent tests, respectively. Finally, our computational framework could be applied to filter approximately 2.5 million unseen antibodies into two final candidates that showed strong and stable binding to all four serotypes. In addition, the most potent and stable candidate (1B3B9_V21) was evaluated for its development potential as a therapeutic agent by molecular docking and MD simulations. This study provides an antibody computational approach to facilitate the high-throughput identification of NAbs and accelerate the development of therapeutic antibodies.