Year: 2026
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funders: U.S. Department of Health and Human Services, Agenția Națională pentru Cercetare și Dezvoltare, Agencia Nacional de Investigación y Desarrollo, Alliance de recherche numérique du Canada, National Institutes of Health, Natural Sciences and Engineering Research Council of Canada, National Institute of General Medical Sciences, U.S. National Library of Medicine, Fondo Nacional de Desarrollo Científico y Tecnológico, NHLBI Division of Intramural Research, CHIST-ERA
OpenAlex ID: W7172511149