Work

PTM-Mamba: A PTM-Aware Protein Language Model with Bidirectional Gated Mamba Blocks

Year: 2024

Type: conference-paper

Abstract: Proteins serve as the workhorses of living organisms, orchestrating a wide array of vital functions. Post-translational modifications (PTMs) of their amino acids greatly influence the structural and functional diversity of different protein types. However, current protein language models (pLMs) like ESM-2 and ProtT5 do not account for PTMs. We introduce PTM-Mamba, a PTM-aware pLM that integrates PTM information into protein sequence modeling. PTM-Mamba leverages structured state space models (SSMs) and incorporates PTM tokens into its training regime. Our results show that PTM-Mamba improves performance on PTM-specific tasks and is the first pLM capable of representing both wild-type and PTM sequences.

Source: ACM International Conference on Information and Knowledge Management (CIKM)

DOI: 10.1145/3627673.3680276

Author: Zhangzhi Peng

Institution: Duke University

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

OpenAlex ID: W4403582477

FWCI: 21.75

Cites: 8

Cited by: 12

Related to: 10