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AB-AG-INTERACT

Financiado

Learning the interaction rules of antibody-antigen binding

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.... ver más

31/12/2028

Innovasjon Norge

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

Innovasjon Norge No se ha especificado una descripción o un objeto social para esta compañía.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-12-04

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2023-COG: ERC CONSOLIDATOR GRANTS

I+D

Cerrada hace 1 año

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1 Participantes

Innovasjon Norge

2.00M€ | Lider

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Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 60 meses Fecha Inicio: 2023-12-04
Fecha Fin: 2028-12-31

Líder del proyecto

Innovasjon Norge

Presupuesto del proyecto 2M€

Descripción del proyecto 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.

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