Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models
Open accessPublished version of a preprint
Abstract
Recent breakthroughs in neural network-based structure prediction methods, such as AlphaFold2 and RoseTTAFold, have dramatically improved the quality of computational protein structure prediction. These models also provide statistical confidence scores that can estimate uncertainties in the predicted structures, but it remains unclear to what extent these scores are related to the intrinsic conformational dynamics of proteins. Here, we compare AlphaFold2 prediction scores with explicit large-scale molecular dynamics simulations of 28 one- and two-domain proteins with varying degrees of flexibility. We demonstrate a strong correlation between the statistical prediction scores and the explicit motion derived from extensive atomistic molecular dynamics simulations and further derive an elastic network model based on the statistical scores of AlphFold2 (AF-ENM), which we benchmark in combination with coarse-grained molecular dynamics simulations. We show that our AF-ENM method reproduces the global protein dynamics with improved accuracy, providing a powerful way to derive effective molecular dynamics using neural network-based structure prediction models.
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Cites
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Background
- Authors
- Jussupow, A., & Kaila, V. R. I.
- Affiliations
- Stockholm University (SE)
- Corresponding author
- Kaila, Ville R. I.
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-03-24
- Volume
- 19
- Issue
- 7
- Pages
- 1965-1975
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC11181330
- OpenAlex
- W4360829321
- Cited by
- 42
- References
- 13
- Citations by year
- 2026: 12 · 2025: 12 · 2024: 14 · 2023: 4
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Chemical Theory and Computation (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Knut och Alice Wallenbergs Stiftelse (2019.0043, 2019.0251)
- Cancerfonden (pj200968)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- protein conformational dynamics, AlphaFold2, elastic network model, coarse-grained molecular dynamics, protein flexibility
- MeSH terms
- Molecular Conformation, Motion, Protein Conformation, Proteins, Neural Networks, Computer, Molecular Dynamics Simulation
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