Accelerating crystal structure determination with iterative AlphaFold prediction
Open accessPublished version of a preprint
Abstract
Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold . Here, an automatic procedure requiring only sequence information and crystallographic data is presented that uses AlphaFold predictions to produce an electron-density map and a structural model. Iterating through cycles of structure prediction is a key element of this procedure: a predicted model rebuilt in one cycle is used as a template for prediction in the next cycle. This procedure was applied to X-ray data for 215 structures released by the Protein Data Bank in a recent six-month period. In 87% of cases our procedure yielded a model with at least 50% of C α atoms matching those in the deposited models within 2 Å. Predictions from the iterative template-guided prediction procedure were more accurate than those obtained without templates. It is concluded that AlphaFold predictions obtained based on sequence information alone are usually accurate enough to solve the crystallographic phase problem with molecular replacement, and a general strategy for macromolecular structure determination that includes AI-based prediction both as a starting point and as a method of model optimization is suggested.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Using AF models in structure determination protocols reduces time and effort compared with ab initio model building.
“The use of AF models in structure determination protocols has shown to reduce the time and effort required relative to ab initio model building68–70”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [69]
In a benchmark, 187 of 215 structures were solved by AF-guided molecular replacement.
“This iterative procedure works extremely well as demonstrated in a benchmark where 187 of 215 structures were solved by AF-guided molecular replacement; the success was shown to be dependent on high confidence scores associated with the AF prediction69.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [69]
An iterative AF2-integrated molecular replacement procedure has been developed, using AF models in the initial structure-solution cycle followed by data-guided cycles of AF prediction and model rebuilding.
“Recently, an AF2 integrated iterative procedure for molecular replacement has been developed where AF models are utilized during the initial structure-solution cycle, followed by data guided cycles of AF structure prediction and model rebuilding60,69.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [69]
In that benchmark, success of AF-guided molecular replacement depended on high confidence scores of the AF prediction.
“This iterative procedure works extremely well as demonstrated in a benchmark where 187 of 215 structures were solved by AF-guided molecular replacement; the success was shown to be dependent on high confidence scores associated with the AF prediction69.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [69]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 43 · Cited by 62
Research works printed but not linked: 1
- Shahin, M., Sheppard, D., Raynaud, C., Berry, J.-L., Gurung, I., Silva, L. M., Feizi, T., Liu, Y. & Pelicic, V. (2022). bioRxiv, 2022.08.25.505150.
- Authors
- Terwilliger, T. C., Afonine, P. V., Liebschner, D., Croll, T. I., McCoy, A. J., Oeffner, R. D., Williams, C. J., Poon, B. K., Richardson, J. S., Read, R. J., & Adams, P. D.
- Affiliations
- Los Alamos National Laboratory (US)
- Lawrence Berkeley National Laboratory (US)
- University of Cambridge (GB)
- Duke University (US)
- Corresponding author
- Terwilliger, Thomas C.
- ORCID
- Venue
- Acta Crystallographica Section D Structural Biology
- Published
- 2023-02-27
- Volume
- 79
- Issue
- 3
- Pages
- 234-244
- ISSN
- 2059-7983 (ISSN-L)
- PMC
- PMC9986801
- OpenAlex
- W4322485564
- Cited by
- 62
- References
- 43
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 21 · 2023: 14
- FWCI
- 4.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Acta Crystallographica Section D Structural Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Apollo (cc-by)
- Funders
- Lawrence Berkeley National Laboratory (DE-AC02-05CH11231)
- Phenix Industrial Consortium
- National Institutes of Health, National Institute of General Medical Sciences (GM063210)
- Wellcome Trust (209407/Z/17/Z)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Inorganic Chemistry › Crystal structures of chemical compounds
- Keywords
- AlphaFold, crystal structure determination, electron density maps, molecular replacement, crystallographic phase problem, model rebuilding, macromolecular structure determination
- MeSH terms
- Artificial Intelligence, Crystallography, Models, Structural, Databases, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
