Modeling Static and Dynamic Protein Structure from 2D Infrared Spectrum with Stochastic Interpolant

Lee Cheuk Kit, Pietro Lio

Keywords: Stochastic Interpolant

TL;DR: Generative modeling for an inverse problem from 2D infrared spectrum to protein structure

Abstract: A protein's function is intrinsically linked to its dynamic structure, yet predicting these conformational changes in real-time remains a central challenge in molecular biology. While two-dimensional infrared (2DIR) spectroscopy provides a powerful experimental window into these dynamics, translating its complex, low-dimensional signals into high-resolution 3D structures is a formidable interpretive hurdle. Current machine learning approaches typically sidestep this challenge by predicting auxiliary information for other computationally expensive tools, creating a slow and indirect workflow. In this paper, we introduce a direct, end-to-end solution: an equivariant generative pipeline based on stochastic interpolants augmented with a length prediction head. The model pretrained on a dataset of static protein bypasses intermediate steps, learning to generate 3D protein structures directly from their corresponding 2DIR spectra, outperforming previous methods. We further show that the model can be finetuned on correlated samples drawn from simulated reversible folding trajectories to improve its predictive power for dynamic protein structure prediction.

Presenter: ~Lee_Cheuk_Kit1

Format: No, the presenting author is unable to, or unlikely to be able to, attend in person.

Funding: No, the presenting author of this submission does not fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 2