Protein World Model: Learning Temperature-Conditioned Conformational Dynamics

Protein function emerges from conformational dynamics rather than isolated structures, yet molecular dynamics simulations remain prohibitively expensive for systematic experimentation across proteins and conditions. Existing generative models produce plausible trajectories, but plausibility alone is insufficient for controlled experimentation: predictions must depend on recent dynamical history, represent stochastic futures, and respond to external perturbations. We introduce a protein world model that infers predictive state from conformational history and generates stochastic future trajectories under controllable temperature actions. Trained on multi-temperature mdCATH, our model combines history-conditioned flow matching with differentiable geometric refinement to generate temporally coherent, geometrically valid multi-step rollouts. Extensive experiments show that the model captures trajectory distributions and dynamical observables more accurately than persistence and matched history-ablated controls, while controlled ablations demonstrate that it exploits temporal ordering beyond static conformational statistics. From a shared molecular state, temperature interventions induce systematic and physically consistent changes in predicted flexibility, structural organization, and conformational evolution, enabling controlled branching experiments across thermal conditions. The model further generalizes to unseen protein domains and temperatures and remains stable under closed-loop rollout. Together, these results establish protein world modeling as a framework for controllable, stochastic simulation of protein dynamics and in-silico experimentation.