Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
A recent review article published on arXiv (2608.16094) delves into the progression of methodologies in protein structure prediction, which is essential to structural biology. It categorizes the field into four distinct phases and identifies three overarching transitions, emphasizing aspects such as data representations, learning strategies, and evaluation metrics. This work builds upon earlier reviews that highlighted advancements through the lenses of model representation, application areas, and protein design. Notably, the paper illustrates how the advent of deep learning has transformed the discipline from MSA-driven monomer folding to more comprehensive frameworks that can model protein complexes and diverse molecular systems. Authored by a team of researchers, the review is accessible as a preprint, reflecting ongoing advancements in the area.
Key facts
- The review is published on arXiv with identifier 2608.16094.
- It focuses on the methodological evolution of protein structure prediction.
- The field is organized into four methodological phases and three cross-cutting transitions.
- Three dimensions are examined: representations and data, architectures and learning strategies, and confidence and evaluation.
- Deep learning has transformed the field from MSA-driven monomer folding to modeling protein complexes and heterogeneous systems.
- Previous reviews have covered representative models, application domains, and protein design.
- The review is a preprint, indicating it has not yet undergone peer review.
- Protein structure prediction is fundamental to structural biology because structure underlies function.
Entities
Institutions
- arXiv