可药性
计算机科学
计算生物学
编码
功能(生物学)
生物
遗传学
基因
作者
Fred Zhangzhi Peng,Chentong Wang,Tong Chen,Benjamin Schussheim,Sophia Vincoff,Pranam Chatterjee
出处
期刊:Nature Methods
[Nature Portfolio]
日期:2025-04-10
卷期号:22 (5): 945-949
被引量:33
标识
DOI:10.1038/s41592-025-02656-9
摘要
Current protein language models (LMs) accurately encode protein properties but have yet to represent post-translational modifications (PTMs), which are crucial for proteomic diversity and influence protein structure, function and interactions. To address this gap, we develop PTM-Mamba, a PTM-aware protein LM that integrates PTM tokens using bidirectional Mamba blocks fused with ESM-2 protein LM embeddings via a newly developed gating mechanism. PTM-Mamba uniquely models both wild-type and PTM sequences, enabling downstream tasks such as disease association and druggability prediction, PTM effect prediction on protein-protein interactions and zero-shot PTM discovery. In total, our work establishes PTM-Mamba as a foundational tool for PTM-aware protein modeling and design.
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