AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank
Open accessAbstract
By providing predicted protein structures from nearly all known protein sequences, the artificial intelligence program AlphaFold (AF) is having a major impact on structural biology. While a stunning accuracy has been achieved for many folding units, predicted unstructured regions and the arrangement of potentially flexible linkers connecting structured domains present challenges. Focusing on single-chain structures without prosthetic groups, an earlier comparison of features derived from small-angle X-ray scattering (SAXS) data taken from the Small-Angle Scattering Biological Data Bank (SASBDB) is extended to those calculated using the corresponding AF-predicted structures. Selected SASBDB entries were carefully examined to ensure that they represented data from monodisperse protein solutions and had sufficient statistical precision and q resolution for reliable structural evaluation. Three examples were identified where there is clear evidence that the single AF-predicted structure cannot account for the experimental SAXS data. Instead, excellent agreement is found with ensemble models generated by allowing for flexible linkers between high-confidence predicted structured domains. A pool of representative structures was generated using a Monte Carlo method that adjusts backbone dihedral allowed angles along potentially flexible regions. A fast ensemble modelling method was employed that optimizes the fit of pair distance distribution functions [ P ( r ) versus r ] and intensity profiles [ I ( q ) versus q ] computed from the pool to their experimental counterparts. These results highlight the complementarity between AF prediction, solution SAXS and molecular dynamics/conformational sampling for structural modelling of proteins having both structured and flexible regions.
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Using an ensemble of AlphaFold-predicted structures rather than one static model significantly improves fitting to SAXS data.
“Recent methods have shown that fitting of SAXS data significantly improves when an ensemble of AF predicted structures is used rather than a static AF model71, highlighting the importance of integrating AF models with experimental data.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [71]
Experimental data, especially SAXS, NMR, and cryo-EM, have been described as guides for modeling ensembles and alternative conformations.
“The use of experimental data, especially SAXS, NMR, and cryo-EM, has also been described to guide modeling of ensembles and alternative conformations71,82,108.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [71]
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The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
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- Authors
- Brookes, E., Rocco, M., Vachette, P., & Trewhella, J.
- Affiliations
- University of Montana (US)
- Ospedale Policlinico San Martino (IT)
- Centre National de la Recherche Scientifique (FR)
- The University of Sydney (AU)
- Corresponding author
- Brookes, Emre
- Trewhella, Jill
- Venue
- Journal of Applied Crystallography
- Published
- 2023-07-20
- Volume
- 56
- Issue
- 4
- Pages
- 910-926
- ISSN
- 0021-8898 (ISSN-L)
- 1600-5767
- PMC
- PMC10405597
- OpenAlex
- W4385683583
- Cited by
- 21
- References
- 50
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 5 · 2023: 3
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Applied Crystallography (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health, National Institute of General Medical Sciences (120600)
- National Science Foundation, Office of Advanced Cyberinfrastructure (1912444, ACI-1548562, CHE-1265821)
- Engineering and Physical Sciences Research Council (EP/K039121/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- AlphaFold, protein structure prediction, small-angle X-ray scattering, flexible linkers, intrinsically disordered regions, ensemble modelling, pair distance distribution function, protein conformational sampling, solution structure
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