Title: Computational B-cell epitope prediction using artificial intelligence methods
Project duration: 01/23 – 12/24
Research Area: Life Science and Medicine
We are developing machine learning methods to predict B cell epitopes taking the conformational change of viral glycoproteins into account. AxIEM (Accelerated class I fusion protein Epitope Mapping) is a method to identify possible B-cell epitope regions on viral glycoproteins (especially class I fusion glycoproteins) which are recognized by the humoral immune response, especially, by antibodies. Determining additional epitope regions on the glycoproteins is necessary to inform rational vaccine design.
The methods that we are developing requires two different conformations of the glycoprotein. Unfortunately, there are only few determined structures. We will overcome by leveraging AlphaFold2-Multimer, which is a deep learning protein structure prediction tool- to extend the benchmark set. Together with an experimental scientist, we aim to test our predicted epitopes in the laboratory.
Developing new antibodies and vaccines by only experimental methods is not time efficient and still very expensive. We hypothesize that computational methods can help to develop rational vaccines in short time and increase the efficiency of the experiments.
We predicted possible epitope regions for Marburg virus and based on the current result we
are optimizing and atomizing the protocol.
We use AlphaFold2-multimer which is the deep learning method to predict the structure of the protein. To predict B-cell epitopes of class I fusion glycoproteins, we optimize the AxIEM method which is also machine learning-based method.
The project will support to develop rational vaccine with machine learning-based methods.