2026/08/03 by Devlina Chakravarty, Justin J. Miller, Da Teng +8
Biochemistry, Genetics and Molecular Biology · #q-bio.BM
arxiv created 2026/08/03 · arxiv updated 2026/08/05
Recent AI advances have enabled protein structure prediction at near-experimental accuracy, largely solving the problem of identifying a dominant conformation from sequence. Many proteins, however, function as dynamic systems populating multiple conformational states with activity emerging from shifts in relative occupancy--an incomplete picture when reduced to one structure. Here, we argue that structure prediction should be reformulated as a state-space inference problem: recovering not one conformation's coordinates but accessible states, their energetic and kinetic relationships, context dependence, and responses to perturbations. We review emerging strategies--deep learning ensemble generators, physics-based simulations, and experimental constraints--and outline a roadmap toward state-space prediction.