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PreprintCited by 5Protein Structure and DynamicsAdvanced Physical and Chemical Molecular InteractionsComputational Drug Discovery Methods

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Characterizing structural and kinetic ensembles of intrinsically disordered proteins using writhe

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Sisk, Thomas R., Olsson, Simon, Robustelli, Paul·2025·Journal of Chemical Theory and Computation
DOI 10.1101/2025.04.26.650781PMC PMC12874386

Preprint — published as 10.1021/acs.jctc.5c01133

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Abstract

The biological functions of intrinsically disordered proteins (IDPs) are governed by the conformational states they adopt in solution and the kinetics of transitions between these states. We apply writhe, a knot-theoretic measure that quantifies the crossings of curves in three-dimensional space, to analyze the conformational ensembles and dynamics of IDPs. We develop multiscale descriptors of protein backbones from writhe to identify slow motions of IDPs and demonstrate that these descriptors provide a superior basis for constructing Markov state models of IDP conformational dynamics compared to traditional distance-based descriptors. Additionally, we leverage the symmetry properties of writhe to design an equivariant neural network architecture to sample conformational ensembles of IDPs with a denoising diffusion probabilistic model. The writhe-based frameworks presented here provide a powerful and versatile approach for understanding how the structural ensembles and conformational dynamics of IDPs influence their biological functions. Significance Statement: Intrinsically disordered proteins (IDPs) are essential for many cellular processes and are implicated in numerous diseases. The biological functions of IDPs are dictated by the populations of the diverse conformational states they adopt in solution and the kinetics of the conformational transitions between these states. Computer simulations are a powerful tool for studying IDPs, but analyzing simulated structural ensembles of IDPs to identify functionally important conformational states and motions remains a significant challenge. In this study, we demonstrate that writhe, a geometric descriptor from the field of knot theory that describes the crossing of curves in space, provides a powerful basis for characterizing the structural ensembles and conformational dynamics of IDPs in atomic detail. Our results provide new tools for unraveling the relationships between IDP sequences, their conformational ensembles, and their biological functions.

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Metadata

Authors
Sisk, T. R., Olsson, S., & Robustelli, P.
Affiliations
  • Dartmouth College (US)
  • Chalmers University of Technology (SE)
Corresponding author
Robustelli, Paul
ORCID
Venue
Journal of Chemical Theory and Computation
Publisher
openRxiv
Published
2025-04-30
Volume
21
Issue
23
Pages
12289-12303
ISSN
1549-9618 (ISSN-L)
DOI
10.1101/2025.04.26.650781
PMC
PMC12874386
OpenAlex
W4416381077
Cited by
5
References
1
Citations by year
2026: 3 · 2025: 2
FWCI
1.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Chemical Theory and Computation (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Chalmers Research (Chalmers University of Technology) (submittedVersion)
Funders
  • U.S. Department of Education (GAANN P200A240037)
  • Knut och Alice Wallenbergs Stiftelse
  • National Institutes of Health
  • National Institute of General Medical Sciences (R35GM152750, R35GM142750)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords
intrinsically disordered proteins, writhe, protein conformational ensembles, conformational dynamics, Markov state models, equivariant neural networks, denoising diffusion probabilistic models, conformational sampling
MeSH terms
Kinetics, Markov Chains, Protein Conformation, Neural Networks, Computer, Molecular Dynamics Simulation, Intrinsically Disordered Proteins

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