Learning and Steering Protein Flexibility for Generative Protein Design

Protein flexibility is a fundamental determinant of molecular recognition, allostery, and catalysis, yet predicting and engineering it at scale remains an open challenge. Existing approaches either rely on computationally prohibitive molecular dynamics simulations, resort to physics-based approximations that lack differentiability and miss sequence-dependent effects, or learn representations that are often tied to specific simulation protocols and can struggle to generalize across protein families and conditions. We introduce FlexFlow, a framework for designing proteins with prescribed per-residue root mean square fluctuation (RMSF) profiles via inference-time guidance on a frozen pretrained sequence–structure co-design model. At its core, FlexFlow is powered by FlexFormer, a differentiable predictor of per-residue backbone flexibility that achieves state-of-the-art accuracy and, unlike predictors that read only coordinates, responds to individual amino acid substitutions, tracking their measured effect on flexibility more faithfully than existing methods. Rather than retraining a conditional generative model, which may distort the structural prior learned from experimentally solved structures and sacrifices designability for flexibility, FlexFlow steers a pretrained joint sequence–structure co-design model at inference time using FlexFormer gradients, without any task-specific retraining. Each backbone is steered directly toward the target flexibility profile during generation, eliminating the post-hoc screening that existing methods require, yielding significant reduction in wall-clock time at substantially better structural quality.