Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
This work addresses the need for efficient thermodynamic modeling of biomolecular conformational ensembles, a bottleneck in structural biology that AlphaFold and similar tools do not solve.
Polyformer is a generative framework that, given a polymer sequence and temperature, produces conformations matching the molecule's thermodynamic ensemble. It simultaneously predicts folding, ensemble distribution, and temperature-dependent changes, showing good agreement with MD simulations for proteins of 50-111 residues.
The classic paradigm of structural biology is that the sequence of a biomolecule (protein, nucleic acid, lipid, etc) determines its conformation (shape) which determines its biological function. Protein folding programs like AlphaFold address this paradigm by predicting the single best conformation given a sequence that defines the molecule. However, biomolecules are not static structures, and their conformational ensemble determines their function. We present the Polyformer -- a generative framework for thermodynamic modeling of polymeric molecules. Given the sequence and temperature (or another thermodynamic variable), the Polyformer generates conformations faithful to the molecule's thermodynamic conformational ensemble. It is the first generative model that solves three problems simultaneously: how does a molecule fold, what is its conformational ensemble, and how does the conformational ensemble change as we change physical temperature. As a concrete test case, we apply Polyformer to protein domains with 50-111 residues and report good agreement of model predictions to Molecular Dynamics (MD) trajectories.