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.
By Handong Wang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang
arXiv:2602. 06020v3 Announce Type: replace Abstract: How do protein structure prediction models fold proteins?
By Kevin Lu, Jannik Brinkmann, Stefan Huber, Aaron Mueller, Yonatan Belinkov, David Bau, Chris Wendler
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.
By Kaustav Mehta
arXiv:2608.29207v1 Announce Type: new
Abstract: Protein structure modeling rests on a single computational primitive: the interaction between what a residue is (sequence content) and where it sits (t...
By Yifan Feng, Guanjie Cheng, Shihui Ying, Shaoyi Du, Yue Gao
arXiv:2512. 15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality.
By Yi Zhou, Haohao Qu, Yunqing Liu, Shanru Lin, Le Song, Wenqi Fan
arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.
By Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy