Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
Alessio Valentini, David Pekker, Chungwen Liang, Todd Martinez, Swagatam Mukhopadhyay
TLDR
Polyformer is a generative framework that models polymeric molecules' thermodynamic conformational ensembles, predicting how they fold and change with temperature.
Key contributions
- Introduces Polyformer, a generative framework for thermodynamic modeling of polymeric molecules.
- Generates molecular conformations reflecting thermodynamic ensembles based on sequence and temperature.
- Simultaneously predicts molecular folding, conformational ensembles, and their temperature-dependent changes.
- Validated on protein domains (50-111 residues), matching Molecular Dynamics simulations.
Why it matters
This paper introduces a crucial shift from static protein folding predictions to dynamic ensemble modeling. By generating conformations faithful to thermodynamic ensembles and their temperature dependence, Polyformer offers a more comprehensive understanding of biomolecular function. This advances structural biology beyond single-state predictions.
Original Abstract
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.
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