Towards the accurate modelling of antibody−antigen complexes from sequence using machine learning and information-driven docking
The source code of HADDOCK3 is freely available at github.com/haddocking/haddock3. The code to generate and analyse the data is available at github.com/haddocking/ai-antibodies. The full runs, including docking models from all modules of a workflow have been deposited in our lab collection (data.sbgrid.org/labs/32/1139) at the SBGRID data repository.
Authors
- Daniel Cutting (ORCID: https://orcid.org/0000-0002-7395-7802)
- Charlotte M. Deane (ORCID: https://orcid.org/0000-0003-1388-2252)
- Marco Giulini (ORCID: https://orcid.org/0000-0002-2691-712X)
- Alexandre M. J. J. Bonvin (ORCID: https://orcid.org/0000-0001-7369-1322)
- Constantin Schneider (ORCID: https://orcid.org/0000-0002-2579-0307)
- Nikita Desai
Institutions
- University of Applied Sciences Utrecht (NL)
- Utrecht University (NL)
- BioScientia (Poland) (PL)
Publication Details
- Journal
- Bioinformatics
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1093/bioinformatics/btae583
- Citations
- 25
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 10.11
Funders
- European Commission