ArticleBioinformatics advances2025
ABCFold: easier running and comparison of AlphaFold 3, Boltz-1, and Chai-1.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Improved hydrolysis of piperacillin by OXA-48-like R214G variants, a selective advantage under piperacillin-tazobactam exposure.Antimicrobial agents and chemotherapy · 2026Article
- Structure and signaling mechanism ofbioRxiv : the preprint server for biology · 2026Article
- Capturing protein-protein interactions in plants: recent advances, challenges, and opportunities.Frontiers in molecular biosciences · 2026Review
- AI-driven drug reposition for pathogens: a new paradigm in pandemic preparedness.Frontiers in chemistry · 2026Review
- An integrated approach to smoky food flavor: Decoding key odorants, sensory contributions, and olfactory recognition mechanisms.Current research in food science · 2026Article
- CCD2MD: A Suite of Packages for Preparing Co-Folded Outputs for Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
- Accelerating Biomolecular Modeling with AtomWorks and RF3.bioRxiv : the preprint server for biology · 2025Article
- Molecular Insight into the Recognition of DNA by the DndCDE Complex in DNA Phosphorothioation.International journal of molecular sciences · 2025Article
- Balancing speed and precision in protein folding: a comparison of AlphaFold2, ESMFold, and OmegaFold.Frontiers in genetics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 standardized 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 visualization 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/.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.