Protein Dynamics Beyond Structure Prediction
Preprint, 2026
The ability to predict protein three-dimensional structures from amino acid sequences is a landmark achievement in molecular biology, where recent deep learning approaches such as AlphaFold are the culmination of decades of work. Yet, the quantitative understanding of how protein sequences give rise to dynamic conformational changes and higher-order assemblies remains unsolved. Folding and conformational states are dynamic, stochastic processes, shaped by sequence, energy, co-translational constraints, chaperone machineries, and the physicochemical conditions of the cellular environment. Recent advances now position the field to move beyond static structural endpoints toward a mechanistic understanding of folding dynamics in living systems. Single-molecule techniques enable time-resolved observation of folding trajectories and intermediate states hitherto hidden by traditional structural biology approaches, while computational innovations and data-driven approaches offer new ways to integrate heterogeneous data across scales. In this Roadmap, we review the current conceptual landscape of protein folding, examine the experimental and theoretical gaps that remain, and discuss emerging strategies that integrate high-resolution measurements with multiscale modeling. We outline a roadmap toward a quantitative and predictive science of protein folding dynamics, conformational kinetics, and macromolecular self-assembly. Realizing this vision would transform our understanding of the dynamics of molecular self-organization, from the folding of individual polypeptides to the emergence of dynamic macromolecular complexes. This will enable rational control of folding and misfolding in health and disease, extend protein engineering principles beyond static structural design, and establish a mechanistic foundation for predictive and personalized interventions in proteostasis-related disorders.
Author
Juliette Griffié
Stockholm University
Sviatlana Shashkova
University of Gothenburg
Antonio Ciarlo
University of Gothenburg
Sreekanth Manikandan
University of Gothenburg
Claes Andréasson
Stockholm University
Malin Bäckström
University of Gothenburg
Tristan Bereau
Heidelberg University
Harald Brismar
Royal Institute of Technology (KTH)
Karolinska Institutet
Carlos Bustamante
University of California
Marta Carroni
Stockholm University
Roberto Covino
Goethe University Frankfurt
Andreas Dahlin
Chalmers, Chemistry and Chemical Engineering, Applied Chemistry
Sebastian Deindl
University of Tübingen
Uppsala University
Lucie Delemotte
Royal Institute of Technology (KTH)
Arne Elofsson
Stockholm University
John Eriksson
Euro-BioImaging ERIC
Giovanna Fragneto
Lund University
Anders Gunnarsson
AstraZeneca AB
Per Hammarström
Linköping University
Caroline Ingre
Karolinska Institutet
Christian Kaiser
Utrecht University
Petronella Kettunen
Sahlgrenska University Hospital
University of Gothenburg
Mark C. Leake
University of York
Benjamin Loos
Stellenbosch University
Anna Månberg
Royal Institute of Technology (KTH)
Antonia S. J. S. Mey
University of Edinburgh
Richard Neutze
University of Gothenburg
Thomas Nyström
University of Gothenburg
Karl Palmås
Chalmers, Technology Management and Economics, Science, Technology and Society
Charley Schaefer
University of Leeds
Markus Tamas
University of Gothenburg
Nicola Ticozzi
University of Milan
IRCCS Istituto Auxologico Italiano
Tomás S. Pilvelic
Lund University
Jacopo Sacquegno
Independent Visual Science Communicator
B.M. Tijms
Amsterdam University Medical Centers
Gunnar von Heijne
Stockholm University
Björn Wallner
Linköping University
Vitali Zhaunerchyk
University of Gothenburg
Simon Olsson
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
University of Gothenburg
Joana B. Pereira
Karolinska Institutet
Julia Fernandez-Rodriguez
University of Gothenburg
Fredrik Westerlund
Molecular Bioscience
Giovanni Volpe
University of Gothenburg
Subject Categories (SSIF 2025)
Molecular Biology
Bioinformatics and Computational Biology
Biophysics
DOI
10.48550/arXiv.2606.08647