Project description
Deep learning enables design of epitope-specific adaptive immune receptors
In contrast to the innate immune system, the adaptive immune system is highly specialised. It targets the specific pathogen causing an infection and ‘remembers’ it for long-lasting protection. This targeted attack relies on adaptive immune receptors (AIRs) and their interactions with specific parts of antigens called epitopes. Current methods for epitope mapping are costly and low-throughput. The ERC-funded AIRstructure project will address these problems by leveraging the researchers’ expertise in modelling protein-protein interactions, including AIR-antigen interactions, and in geometric deep learning. The resulting accurate and high-throughput models will enable structural modelling of AIR-antigen interactions, design of epitope-specific AIRs and structure-based specificity prediction for mining large AIR repertoires.
Objective
B- and T- cell adaptive immune receptor (AIR) repertoires are highly diverse, enabling response to a wide range of pathogens. While sequencing of an individual's immune repertoires is becoming common, our ability to convert these datasets into comprehensive antigen exposure information to inform clinical decisions is limited. The major challenges are to identify the antigens recognized by B-cell and T-cell immune receptors (BCRs/antibodies and TCRs), model their structures and determine their epitopes. Experimental approaches for epitope mapping are costly and low-throughput. While deep learning-based models have revolutionized structural biology by predicting highly accurate structures of proteins and protein complexes, they rely on multiple sequence alignments (MSAs) that are not available for the AIR-antigen interactions. Recently, my group has designed geometric deep learning models for AIR structure modeling and for epitope prediction without MSA.
In this project, I will build on my expertise in modeling protein-protein interactions, including AIR-antigen, and in geometric deep learning to develop accurate and high-throughput models that address the specific challenges of AIR-antigen systems.
My main goals are to develop deep learning-based models for: (i) accurate and high-throughput end-to-end structure modeling of AIR-antigen interactions; (ii) design of epitope-specific AIRs for targeting broadly neutralizing epitopes and optimized antigenicity profiles; and (iii) structure-based specificity prediction for mining large AIR repertoires.
These approaches will advance the analysis of immune repertoires, improve our understanding of immune response, and enable designing vaccines and therapeutics with broad specificity and resistance to antigenic mutations. Moreover, the methods will empower the cancer epitope discovery and the detection of autoimmune receptors.
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- natural sciences biological sciences biochemistry biomolecules proteins
- medical and health sciences basic medicine pharmacology and pharmacy pharmaceutical drugs vaccines
- natural sciences biological sciences genetics mutation
- medical and health sciences clinical medicine oncology
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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HORIZON.1.1 - European Research Council (ERC)
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(opens in new window) ERC-2024-COG
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91904 Jerusalem
Israel
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