Seek and You Shall Fold
This work addresses a bottleneck in protein modeling for computational biologists by enabling automated, data-conditioned structure generation beyond differentiability constraints, though it is incremental in combining existing methods.
The paper tackled the challenge of integrating non-differentiable experimental data, such as chemical shifts, into protein generative models by introducing a framework that couples a diffusion-based generator with a genetic algorithm, enabling structure generation guided by chemical shifts for the first time.
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 as feasible, expose key weaknesses in current predictors, and showcase a general strategy for incorporating diverse experimental signals. Our work points toward automated, data-conditioned protein modeling beyond the limits of differentiability.