A GPU-accelerated method identifies slow vibrational modes that guide protein shape changes, acting as a roadmap to explore biologically relevant conformations and transition pathways while reducing computational cost to under a day.

Developed at Arizona State University under Associate Professor Matthias Heyden, the approach captures slow, low-frequency motions from nanosecond-scale simulations, making long-timescale transitions observable.

The technique extends prediction beyond static structures toward dynamic ensembles, potentially enabling a sequence-to-structure-to-dynamics framework for predictive proteomics, complementing advances like AlphaFold.

Published in Science Advances on March 27, 2026, the method shows proteins sampling multiple conformations by analyzing thermally driven fluctuations at room temperature, yielding robust, repeatable results.

Practically, the method accelerates drug discovery for targets in antibiotic resistance and cancer by enabling high-throughput, accurate exploration of conformational landscapes.

Dynamic sampling enhances understanding of allosteric effects and can improve rational drug design by revealing subtle binding sites and how ligand binding propagates structural changes.

Applications extend to protein engineering and synthetic biology, offering the potential to create smart proteins with switchable functions and improved catalytic efficiency through controlled dynamics.

The workflow leverages ASU’s Sol supercomputer and GPU parallelism to democratize access to dynamic protein simulations, transforming routine exploration for research labs worldwide.

This GPU-accelerated method enables observation of conformational transitions that were previously time-prohibitive, broadening the practical scope of molecular dynamics studies.

Future prospects include integration with experimental techniques like cryo-EM and NMR to build richer dynamic portraits and further refine predictive models of protein motion.