arXiv:2510.03095v4 Announce Type: replace
Abstract: Diffusion- and flow-based generative models have recently demonstrated strong performance in protein backbone generation tasks, offering unpreceden...
By Liyang Xie, Haoran Zhang, Zhendong Wang, Wesley Tansey, Mingyuan Zhou
arXiv:2606. 11243v1 Announce Type: new Abstract: De novo protein generation has transformative potential in therapeutic design, enzyme engineering, and synthetic biology.
By Chuanzhen Wang, Meade Cleti, Pete Jano
arXiv:2603. 14717v2 Announce Type: replace Abstract: Generating novel protein sequences that respect a family's statistical constraints typically requires training deep generative models on thousands to millions of examples.
By Jeffrey D. Varner
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:2607. 23518v1 Announce Type: new Abstract: The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via joint sequence-structure modeling or hallucination.
By Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang, Yunhong Lu, Chenxi Cai, Yan Zhang, Min Zhang
The paper introduces StructEvo, a structure-aware reinforcement learning framework designed to improve protein directed evolution. By using a delta-structure fusion encoder to approximate mutant structure features and a hierarchical action network aligned with protein structure, the method navigates the vast mutation space more effectively. StructEvo outperforms existing machine learning-assisted directed evolution techniques by 9.2% and 16.3% on two benchmarks and uncovers an experimentally validated epistasis pattern in GFP, underscoring the value of structural guidance.
By Zikun Nie, Suyuan Zhao, Yizhen Luo, Siqi Fan, Zaiqing Nie