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The paper introduces Polyformer, a generative model that predicts the thermodynamic conformational ensemble of polymeric molecules given their sequence and temperature. Polyformer addresses the challenge of simultaneously modeling molecular folding, conformational ensembles, and the impact of temperature on these ensembles. Applied to protein domains, Polyformer's predictions show good agreement with Molecular Dynamics trajectories, demonstrating its ability to generate conformations faithful to the molecule's thermodynamic behavior.
Imagine AlphaFold, but instead of a single structure, it gives you the full range of likely shapes a molecule will take at a given temperature.
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.