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PTM-Mamba:一种具有双向门控曼巴模块的PTM感知蛋白质语言模型。

PTM-Mamba: a PTM-aware protein language model with bidirectional gated Mamba blocks.

作者信息

Peng Fred Zhangzhi, Wang Chentong, Chen Tong, Schussheim Benjamin, Vincoff Sophia, Chatterjee Pranam

机构信息

Department of Biomedical Engineering, Duke University, Durham, NC, USA.

School of Life Sciences, Westlake University, Hangzhou, China.

出版信息

Nat Methods. 2025 May;22(5):945-949. doi: 10.1038/s41592-025-02656-9. Epub 2025 Apr 10.

Abstract

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.

摘要

当前的蛋白质语言模型(LMs)能够准确地编码蛋白质特性,但尚未能够表示翻译后修饰(PTM),而翻译后修饰对于蛋白质组多样性至关重要,并会影响蛋白质的结构、功能和相互作用。为了弥补这一差距,我们开发了PTM-Mamba,这是一种能够感知PTM的蛋白质语言模型,它通过一种新开发的门控机制,使用与ESM-2蛋白质语言模型嵌入融合的双向Mamba模块来整合PTM标记。PTM-Mamba独特地对野生型和PTM序列进行建模,从而实现下游任务,如疾病关联和药物可及性预测、PTM对蛋白质-蛋白质相互作用的影响预测以及零样本PTM发现。总的来说,我们的工作将PTM-Mamba确立为用于感知PTM的蛋白质建模和设计的基础工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c6a/12074982/fe25dea21f1f/41592_2025_2656_Fig1_HTML.jpg

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