Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling

2026-08-17

AI Summary

This study addresses the ambiguity surrounding the methodological evolution of protein structure prediction by proposing a "four-stage, three-transition" analytical framework. By systematically examining technical paradigm shifts across representation, architecture, and evaluation dimensions, this research integrates deep learning and generative modeling to elucidate the intrinsic trajectory from evolutionary constraints to generative design. Consequently, this work constructs a comprehensive methodological evolution map that clarifies historical transitions in model capabilities and application roles. Ultimately, it provides a systematic theoretical foundation for understanding the developmental logic and future trends within the field, offering critical insights into how predictive methodologies have matured over time.

Abstract

Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation. Recent advances in deep learning have transformed the field from multiple sequence alignment (MSA)-driven monomer folding into broader frameworks capable of modeling protein complexes and increasingly heterogeneous molecular systems. Existing reviews have summarized this progress from the perspectives of representative models, application domains, and protein design. Building on these efforts, this review focuses on the methodological evolution of the field itself. It examines recent developments through three closely related dimensions: representations and data, architectures and learning strategies, and confidence and evaluation. Within this perspective, the field is organized into four methodological phases and three cross-cutting transitions: from explicit evolutionary coupling features and early contact prediction to learned sequence representations in AlphaFold2, RoseTTAFold, and ESMFold; from protein-only monomer folding to increasingly integrated modeling of heterogeneous molecular systems in AlphaFold-Multimer, RoseTTAFoldNA, and AlphaFold3; and, more recently, from prediction-oriented structure inference to design-oriented generative modeling in RFdiffusion and related frameworks. This framework provides a clearer understanding of how methodological shifts have shaped the capabilities, limitations, and practical roles of recent models.

Problem

• Protein Structure Prediction
• Generative Modeling
• Evolutionary Constraints
• Methodological Evolution

Innovation

• Generative Modeling
• Methodological Evolution
• Heterogeneous Molecular Systems
• Learned Sequence Representations
• Design-oriented Frameworks