From prediction to discovery: AI as an observatory of physical organization in protein space

Artificial intelligence (AI) has transformed protein science from a data-sparse field into one increasingly rich in model-derived information. Predicted structures, sequence embeddings, confidence measures, mutation scores, inverse-design outputs, and generative ensembles provide complementary views of protein sequence, structure, dynamics, function, and designability. In this review, we develop the view of AI as an observatory of protein systems, shifting the question from how AI can be applied to protein problems to what successful models have learned about the constraints that shape proteins: physical constraints on structure and dynamics, statistical regularities in learned representations, and evolutionary constraints on sequence variation and design. This perspective is developed through three large-scale patterns exposed by AI-derived observables: the global structural landscape of the predicted protein Universe, proteome-scale relations between folding topology and native-state dynamics, and the organization of sequence, structure, and function into shared, searchable multimodal spaces. We then discuss how uncertainty analysis, perturbation and contrastive scoring, representation decomposition, physically informed probes, and experimental benchmarking extract interpretable information from these signals, and how the resulting descriptions connect to principles of folding, flexibility, evolutionary filtering, functional response, and design feasibility. At the same time, AI-derived observables are not direct physical measurements, but compressed, model-dependent readouts whose meaning requires systematic calibration. This perspective positions AI as both a predictive instrument and a systematic observational interface through which the organizational principles linking protein structure, dynamics, evolution, function, and design can be quantitatively probed and physically interpreted.