Computer aided de novo design of nanobodies
The project aims to automate the design of fully de novo nanobodies with nanomolar affinity using AI-driven methods, eliminating animal use and enhancing efficiency in antibody development.
Projectdetails
Introduction
Antibodies have become major players in the pharmaceutical industry and were valued at 0.16 billion US dollars in 2023. Traditionally, antibodies are obtained after immunization of different animals and then produced in relevant cells for use in research, diagnostics, and therapy.
Background
In recent years, there has been a growing public opinion in Europe to ban the use of animals for biomedical research. Therefore, there is increasing pressure to move from animal-produced antibodies to designing and producing them in vitro.
Advantages of Antibody Engineering
Antibody engineering has another important advantage: the possibility of targeting a precise epitope rather than relying on serendipity, as is the case when injecting an animal with an antigen.
Recent Advances
In recent years, there have been significant advances in protein design based on the use of artificial intelligence and precise force fields. Despite this progress, the majority of companies that work on antibody design combine rational engineering with massive proprietary screening methods. So far, there are no reported cases of fully de novo design of an antibody with nanomolar (nM) affinity against a defined epitope.
Case Study
Using an interleukin receptor as a case study, we have shown that we can indeed fully design de novo a nanobody that recognizes the target with nM affinity, using our proprietary protein design software FoldX and ModelX. Experts consulted to date indicate that the results obtained so far are “truly impressive,” prompting us to continue validating and optimizing our process.
Objectives
Our proposal has two main objectives:
-
To fully automate our pipeline that involves:
- Epitope selection
- Antibody framework selection
- Docking
- Backbone move
- Side chain search
-
To demonstrate that our optimized pipeline can design fully de novo nanobodies against a defined target in a fast and cost-effective way.
Conclusion
Success in both objectives will open the way to fully de novo antibodies with desired properties and position ourselves in the search for funding and spin-off incorporation.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 150.000 |
Totale projectbegroting | € 150.000 |
Tijdlijn
Startdatum | 1-11-2024 |
Einddatum | 30-4-2026 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- FUNDACIO CENTRE DE REGULACIO GENOMICApenvoerder
Land(en)
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Vergelijkbare projecten uit andere regelingen
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---|---|---|---|---|
PROposing Action to ConTrol and Impede betacoronaVirus EmergenciesDevelop vaccines and monoclonal antibodies targeting subdominant epitopes of SARS-CoV-2 to ensure broad protection against current and future variants, enhancing global pandemic preparedness. | ERC ADG | € 2.498.750 | 2023 | Details |
Learning the interaction rules of antibody-antigen bindingThis project aims to enhance antibody-antigen binding prediction by generating large-scale sequence and structural data through high-throughput screening and machine learning techniques. | ERC COG | € 2.000.000 | 2024 | Details |
Accelerated Discovery Nanobody PlatformThe ALADDIN project aims to revolutionize therapeutic antibody discovery for cancer by integrating nanobody technology, AI tools, and innovative models to enhance efficiency and reduce reliance on animal testing. | EIC Pathfinder | € 3.315.441 | 2024 | Details |
Computational design of synthetic antibody repertoires for accelerated therapeutic discoveryCADABRE aims to design and optimize diverse human antibody repertoires with enhanced stability and developability for therapeutic discovery using advanced protein design and AI-driven screening methods. | ERC ADG | € 2.741.000 | 2024 | Details |
PROposing Action to ConTrol and Impede betacoronaVirus Emergencies
Develop vaccines and monoclonal antibodies targeting subdominant epitopes of SARS-CoV-2 to ensure broad protection against current and future variants, enhancing global pandemic preparedness.
Learning the interaction rules of antibody-antigen binding
This project aims to enhance antibody-antigen binding prediction by generating large-scale sequence and structural data through high-throughput screening and machine learning techniques.
Accelerated Discovery Nanobody Platform
The ALADDIN project aims to revolutionize therapeutic antibody discovery for cancer by integrating nanobody technology, AI tools, and innovative models to enhance efficiency and reduce reliance on animal testing.
Computational design of synthetic antibody repertoires for accelerated therapeutic discovery
CADABRE aims to design and optimize diverse human antibody repertoires with enhanced stability and developability for therapeutic discovery using advanced protein design and AI-driven screening methods.