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ABCFold: easier running and comparison of AlphaFold 3, Boltz-1 and Chai-1
Open accessPreprint — published as 10.1093/bioadv/vbaf153 · Paper page
Abstract
Motivation: The latest generation of deep learning-based structure prediction methods enable accurate modelling of most proteins and many complexes. However, preparing inputs for the locally installed software is not always straightforward, and the results of local runs are not always presented in an ideally accessible fashion. Furthermore, it is not yet clear whether the latest tools perform equivalently for all types of target. Results: ABCFold facilitates the use of AlphaFold 3, Boltz-1, and Chai-1 with a standardised input to predict atomic structures, with Boltz-1 and Chai-1 being installed on runtime (if required). MSAs can be generated internally using either the JackHMMER MSA search within AlphaFold 3, or with the MMseqs2 API. Alternatively, users can provide their own custom MSAs. This therefore allows AlphaFold 3 to be installed and run without downloading the large databases needed for JackHMMER. There are also straightforward options to use templates, including custom templates. Results from all packages are treated in a unified fashion, enabling easy comparison of results from different methods. A variety of visualisation options are available which include information on steric clashes. Availability and implementation: ABCFold is coded in Python and JavaScript. All scripts and associated documentation are available from https://github.com/rigdenlab/ABCFold or https://pypi.org/project/ABCFold/1.0.0/ . Contact: drigden@liverpool.ac.uk
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Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Elliott, L. G., Simpkin, A. J., & Rigden, D. J.
- Affiliations
- University of Liverpool (GB)
- Corresponding author
- Rigden, Daniel J.
- Venue
- Bioinformatics Advances
- Publisher
- openRxiv
- Published
- 2025-03-17
- Volume
- 5
- Issue
- 1
- Pages
- vbaf153-vbaf153
- ISSN
- 2635-0041 (ISSN-L)
- PMC
- PMC12287924
- OpenAlex
- W4411712941
- Cited by
- 12
- References
- 1
- Citations by year
- 2026: 9 · 2025: 3
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Bioinformatics Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (BB/Y008901/1)
- Science and Technology Facilities Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein structure prediction, AlphaFold3, Chai-1, multiple sequence alignment, MMseqs2, template-based modeling, steric clashes
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