SEMA version 2.0

SEMA (Spatial Epitope Modelling with Artificial intelligence) is a set of research tools for sequence- and structure-based conformational B-cell eptiope prediction, accurate identification of N-glycosylation sites, and a distinctive module for comparing the structures of antigen B-cell epitopes enhancing our ability to analyze and understand its immunogenic properties.

SEMA 2.0 contains following models:

SEMA-1D model is based on an ensemble of ESM2 transformer deep neural network protein language models.
SEMA-3D model is based on an ensemble of inverse folding models, SaProt.
The N-glycosylation prediction model (SEMA_PTM) was obtained by adding a fully-connected linear layer on the top layer of the ESM-2 pre-trained model.
Epitope comparison model is trained to identify local structural similarities within proteins, based on the non-linear transformation of multiplication of the embeddings of PLM with geometric modalities.

SEMA version 1.0.

This version contains SEMA1D model, based on ensemble of pre-trained ESM-1v models, and SEMA-3D model, based on ensemble of pre-trained ESM-IF1 models.

Read the article for details:
Shashkova, T.I., Umerenkov, D., Salnikov, M., Strashnov, P.V., Konstantinova A.V., Lebed, I., Shcherbinin, D.N., Asatryan, M.N., Kardymon, O.L., Ivanisenko, N.V. (2022). SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning. Front. Immunol. doi: 10.3389/fimmu.2022.960985