Predicting protein structures at any folding stage from sequence
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arXiv:2608. 16094v1 Announce Type: new Abstract: Accurate protein structure prediction is fundamental to structural biology because protein structure underlies molecular function and provides a basis for mechanistic interpretation.
The paper investigates how ESMFold’s folding trunk transforms amino‑acid sequences into 3D protein structures by using sparse autoencoders (SAEs) to uncover interpretable features. It first analyzes representation distributions across modules and recycles to identify distinct spatial computations, then shows that single representations carry inherited semantics while pair representations compute pairwise geometry. Finally, the authors use a reasoning language model and gradient‑based attribution to trace concept composition across blocks, verifying alignment with known biochemistry and demonstrating that the trained sparse dictionaries can predict the model’s fold quality, thereby turning the black‑box trunk into an interpretable, scientifically verifiable circuit.
arXiv:2410.20317v2 Announce Type: replace Abstract: Molecular dynamics (MD) simulations are a principled but computationally expensive approach for studying protein conformational variability, making...
SymFold introduces a symmetric dual‑path architecture that combines protein language models (PLMs) and multimodal protein language models (MPLMs) to iteratively guide protein sequence generation for inverse folding. By leveraging pretrained sequence evolution knowledge from PLMs and structural knowledge from MPLMs, the method improves upon the traditional serial pipeline where structure encoders produce coarse sequences refined by PLMs. Experiments on standard inverse‑folding benchmarks show state‑of‑the‑art performance, and ablation studies confirm the effectiveness of the symmetric design.
arXiv:2607. 16087v1 Announce Type: new Abstract: AlphaFold2's 93 million parameters, shaped by the evolutionary record of protein structure encoded in the Protein Data Bank and in sequence alignments, are conventionally treated only as machinery for converting sequence to structure.
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?