Destabilize to Stabilize: Inference-Time Guidance for Generating More Stable Proteins and Complexes

We aim to design more stable proteins by steering protein diffusion models such as BoltzGen. We hypothesize that generative models learn an implicit plausibility landscape around generated proteins and that this landscape can be used for structure destabilization as well as corrective stabilizing sequence mutations. We introduce RipZipGuide, a training-free workflow based on inference-time guidance of diffusion models. Our destabilization technique operates on collective variables and elicits structural failure modes through progressive destabilization steering of generated proteins. A candidate protein or protein-complex is progressively destabilized over multiple noising-denoising iterations, with noise levels dynamically adapted to the candidate and with repulsive bias accumulated across iterations to discourage returning to previously sampled conformations, resembling metadynamics. Failure modes become observable as local unfolding events in nearby conformational basins. These regions are then patched/stabilized through targeted denoising using another bias optimizing for hydrogen-bonding. Case studies on a toy-model and realistic proteins demonstrate the universality, reproducibility, and utility domains of the method. We expect the main gain of the method to be in the speed-up of sampling proteins with custom properties without requiring any (re)training or fine-tuning of the base-model.