Seek and You Shall Fold
Nadav Bojan Sellam, Meital Bojan, Paul Schanda, Alex Bronstein

TL;DR
This paper introduces a novel framework that enables the integration of non-differentiable experimental data into protein generative models, improving structure prediction by coupling diffusion models with genetic algorithms across multiple data modalities.
Contribution
It presents a general method for guiding protein generative models with non-differentiable experimental data using a genetic algorithm, expanding capabilities to include chemical shifts.
Findings
Effective guidance using pairwise distance constraints
Successful incorporation of nuclear Overhauser effect restraints
First demonstration of chemical shift guided structure generation
Abstract
Accurate protein structures are essential for understanding biological function, yet incorporating experimental data into protein generative models remains a major challenge. Most predictors of experimental observables are non-differentiable, making them incompatible with gradient-based conditional sampling. This is especially limiting in nuclear magnetic resonance, where rich data such as chemical shifts are hard to directly integrate into generative modeling. We introduce a framework for non-differentiable guidance of protein generative models, coupling a continuous diffusion-based generator with any black-box objective via a tailored genetic algorithm. We demonstrate its effectiveness across three modalities: pairwise distance constraints, nuclear Overhauser effect restraints, and for the first time chemical shifts. These results establish chemical shift guided structure generation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsProtein Structure and Dynamics · Gene Regulatory Network Analysis · Computational Drug Discovery Methods
