epiGPTope: A machine learning-based epitope generator and classifier
This work addresses the problem of rational epitope design for researchers in biotechnology and immunology, offering a novel generative method that is incremental in combining generation with classification.
The study tackled the challenge of designing synthetic epitope libraries by developing epiGPTope, a large language model that generates novel epitope-like sequences with statistical properties similar to known epitopes, and includes classifiers to predict bacterial or viral origin to narrow candidate libraries. This approach bypasses the need for geometric frameworks or hand-crafted features, aiming to accelerate and reduce costs in epitope discovery for applications like immunotherapies and vaccines.
Epitopes are short antigenic peptide sequences which are recognized by antibodies or immune cell receptors. These are central to the development of immunotherapies, vaccines, and diagnostics. However, the rational design of synthetic epitope libraries is challenging due to the large combinatorial sequence space, $20^n$ combinations for linear epitopes of n amino acids, making screening and testing unfeasible, even with high throughput experimental techniques. In this study, we present a large language model, epiGPTope, pre-trained on protein data and specifically fine-tuned on linear epitopes, which for the first time can directly generate novel epitope-like sequences, which are found to possess statistical properties analogous to the ones of known epitopes. This generative approach can be used to prepare libraries of epitope candidate sequences. We further train statistical classifiers to predict whether an epitope sequence is of bacterial or viral origin, thus narrowing the candidate library and increasing the likelihood of identifying specific epitopes. We propose that such combination of generative and predictive models can be of assistance in epitope discovery. The approach uses only primary amino acid sequences of linear epitopes, bypassing the need for a geometric framework or hand-crafted features of the sequences. By developing a method to create biologically feasible sequences, we anticipate faster and more cost-effective generation and screening of synthetic epitopes, with relevant applications in the development of new biotechnologies.