From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions

TL;DR

This review highlights the transformative impact of AI on protein science, shifting from static structure prediction to dynamic, multimodal, and generative modeling of proteins and their interactions, with future directions toward more physically consistent and interpretable models.

Contribution

It systematically reviews recent advances in AI-driven protein modeling, emphasizing generative frameworks, multimodal integration, and functional predictions, and discusses current challenges and future research directions.

Findings

AI enables modeling of conformational ensembles and interactions.

Generative models like diffusion capture thermodynamic distributions.

Identifies bottlenecks such as data biases and interpretability issues.

Abstract

The protein folding problem has been fundamentally transformed by artificial intelligence, evolving from static structure prediction toward the modeling of dynamic conformational ensembles and complex biomolecular interactions. This review systematically examines the paradigm shift in AI driven protein science across five interconnected dimensions: unified multimodal representations that integrate sequences, geometries, and textual knowledge; refinement of static prediction through MSA free architectures and all atom complex modeling; generative frameworks, including diffusion models and flow matching, that capture conformational distributions consistent with thermodynamic ensembles; prediction of heterogeneous interactions spanning protein ligand, protein nucleic acid, and protein protein complexes; and functional inference of fitness landscapes, mutational effects, and text guided property prediction. We critically analyze current bottlenecks, including data distribution biases, limited mechanistic interpretability, and the disconnect between geometric metrics and biophysical reality, while identifying future directions toward physically consistent generative models, multimodal foundation architectures, and experimental closed loop systems. This methodological transformation marks artificial intelligence's transition from a structural analysis tool into a universal simulator capable of understanding and ultimately rewriting the dynamic language of life.