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.
Författare
Juliette Griffié
Stockholms universitet
Sviatlana Shashkova
Göteborgs universitet
Antonio Ciarlo
Göteborgs universitet
Sreekanth Manikandan
Göteborgs universitet
Claes Andréasson
Stockholms universitet
Malin Bäckström
Göteborgs universitet
Tristan Bereau
Universität Heidelberg
Harald Brismar
Kungliga Tekniska Högskolan (KTH)
Karolinska Institutet
Carlos Bustamante
University of California
Marta Carroni
Stockholms universitet
Roberto Covino
Johann Wolfgang Goethe Universität Frankfurt am Main
Andreas Dahlin
Chalmers, Kemi och kemiteknik, Tillämpad kemi
Sebastian Deindl
Universität Tübingen
Uppsala universitet
Lucie Delemotte
Kungliga Tekniska Högskolan (KTH)
Arne Elofsson
Stockholms universitet
John Eriksson
Euro-BioImaging ERIC
Giovanna Fragneto
Lunds universitet
Anders Gunnarsson
AstraZeneca AB
Per Hammarström
Linköpings universitet
Caroline Ingre
Karolinska Institutet
Christian Kaiser
Universiteit Utrecht
Petronella Kettunen
Sahlgrenska universitetssjukhuset
Göteborgs universitet
Mark C. Leake
University of York
Benjamin Loos
Universiteit Stellenbosch
Anna Månberg
Kungliga Tekniska Högskolan (KTH)
Antonia S. J. S. Mey
University of Edinburgh
Richard Neutze
Göteborgs universitet
Thomas Nyström
Göteborgs universitet
Karl Palmås
Chalmers, Teknikens ekonomi och organisation, Science, Technology and Society
Charley Schaefer
University of Leeds
Markus Tamas
Göteborgs universitet
Nicola Ticozzi
Universita' degli Studi di Milano
IRCCS Istituto Auxologico Italiano
Tomás S. Pilvelic
Lunds universitet
Jacopo Sacquegno
Independent Visual Science Communicator
B.M. Tijms
Amsterdam UMC
Gunnar von Heijne
Stockholms universitet
Björn Wallner
Linköpings universitet
Vitali Zhaunerchyk
Göteborgs universitet
Simon Olsson
Chalmers, Data- och informationsteknik, Data Science och AI
Göteborgs universitet
Joana B. Pereira
Karolinska Institutet
Julia Fernandez-Rodriguez
Göteborgs universitet
Fredrik Westerlund
Molekylär biovetenskap
Giovanni Volpe
Göteborgs universitet
Ämneskategorier (SSIF 2025)
Molekylärbiologi
Bioinformatik och beräkningsbiologi
Biofysik
DOI
10.48550/arXiv.2606.08647