Project description
Antibody-antigen binding interaction rules to advance vaccine design
Antibody-antigen binding is fundamental to monoclonal antibodies and vaccines. To accelerate therapeutic discovery, in silico design of antibodies (Ab) and antigens (Ag) must be conducted while addressing the challenge of predicting Ab-Ag binding interactions. Currently, prediction performance for Ab-Ag binding is limited, and the rules governing these interactions remain largely unknown. The ERC-funded AB-AG-INTERACT project aims to develop a high-throughput screening method for over 1 000 Ab paratope-mutated variants binding to more than 1 000 Ag epitope-mutated variants, generating data for over 1 million Ab-Ag binding pairs. This addresses the large-scale sequence and structural data gap. The findings will contribute to a deeper understanding of these rules, advancing the in silico design of antibodies and vaccines.
Objective
Antibody-antigen binding is the basis of two fundamental biotherapeutic pillars: monoclonal antibodies (1) and vaccines (2). To accelerate therapeutics discovery, we need to perform antibody (Ab) and antigen (Ag) design in silico. Specifically, we need to address a fundamental immuno-biotechnological challenge: understanding the interaction rules that predict Ab-Ag binding. Solving this challenge demands the convergence of biotechnology, computational structural biology, and machine learning (ML). My lab is one of the few worldwide to have this transdisciplinary expertise.
Research problem: Currently, the predictive performance of Ab-Ag binding is poor, and an understanding of the underlying rules of Ab-Ag binding is mostly absent. We previously showed that both unprecedentedly large datasets (>10^5 Ab-Ag sequence pairs) and extensive structural information on the Ab-Ag binding interface (paratope, epitope) are needed to increase prediction accuracy and recover binding rules.
Targeted breakthrough: To address the lack of large-scale Ab-Ag sequence and structural data, we will develop a method for high-throughput screening of >10^3 Ab paratope-mutated variants binding to >10^3 of Ag epitope-mutated variants, generating sequence data of Ab-Ag binding pairs at an unprecedented scale (>10^6 sequence Ab-Ag pairs). Structural information of the entirety of the sequence-based Ab-Ag binding data will be generated by building and innovating on recent breakthroughs in computational structural biology. To derive Ab-Ag interaction rules from the generated data, we will develop ML techniques for Ab-Ag binding prediction and rule recovery. We will demonstrate experimentally that we have begun to understand Ab-Ag interaction rules.
Impact: The proposed research generates the exact data necessary to recover the rules of Ab-Ag binding and provides a first groundbreaking insight into those rules, moving us closer to in silico on-demand antibody and vaccine design.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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(opens in new window) ERC-2023-COG
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0313 Oslo
Norway
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