B-cell epitope prediction

EP4728519A1Pending Publication Date: 2026-04-22NEC ONCOIMMUNITY AS
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Patent Information

Authority / Receiving Office
EP · EP
Patent Type
Applications
Current Assignee / Owner
NEC ONCOIMMUNITY AS
Filing Date
2024-06-13
Publication Date
2026-04-22

AI Technical Summary

Technical Problem

Current methods for predicting B-cell epitopes, especially conformational B-cell epitopes, face challenges due to limited data defining true epitopes and the requirement for 3D protein structure information, which is often not available, leading to inaccurate predictions and difficulties in distinguishing between different epitopes.

Method used

A computer-implemented method using a trained machine learning model that predicts B-cell epitopes based on the protein's structure and surface characteristics in an unbound state, without requiring the full 3D structure, by accessing secondary structure, relative solvent accessibility, and half-sphere exposure, and incorporating physiochemical characteristics to improve prediction accuracy.

Benefits of technology

This approach enables accurate prediction of B-cell epitopes, including conformational ones, without the need for experimental 3D structure data, providing improved confidence in identifying true epitopes and their relationships, thus enhancing vaccine design and diagnostic applications.

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Abstract

A computer-implemented method of predicting whether a protein comprises a IB-cell epitope that is likely to instigate a binding event with an antibody is disclosed. The method comprises: (a) accessing one or more structure and / or surface characteristics of the protein; and (b) inputting the one or more structure and / or surface characteristics of the protein into a trained first machine learning model to predict whether the protein comprises a true B-cell epitope, wherein the first machine learning model is trained by: generating a first reference dataset that comprises: (i) a plurality of first reference proteins, each first reference protein comprising at least one B-cell epitope classified as a true B-cell epitope; and (ii) one or more structure and / or surface characteristics of each first reference protein in an unbound state; and training the first machine learning model using the first reference dataset to learn a relationship between the structure and / or surface characteristic(s) of the first reference proteins in an unbound state, and the B-cell epitopes classified as true B-cell epitopes.
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