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Cited by 31Machine Learning in BioinformaticsProtein Structure and DynamicsGenomics and Chromatin Dynamics

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TransPTM: a transformer-based model for non-histone acetylation site prediction

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Meng, Lingkuan, Chen, Xingjian, Cheng, Ke, Chen, Nanjun, Zheng, Zetian, Wang, Fuzhou, Sun, Hongyan, Wong, Ka-Chun·2024·Briefings in Bioinformatics
DOI 10.1093/bib/bbae219PMC PMC11082075

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Abstract

Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been developed, there is a lack of benchmark dataset and bespoke predictors for non-histone acetylation site prediction. To address these problems, we have contributed to both dataset creation and predictor benchmark in this study. First, we construct a non-histone acetylation site benchmark dataset, namely NHAC, which includes 11 subsets according to the sequence length ranging from 11 to 61 amino acids. There are totally 886 positive samples and 4707 negative samples for each sequence length. Secondly, we propose TransPTM, a transformer-based neural network model for non-histone acetylation site predication. During the data representation phase, per-residue contextualized embeddings are extracted using ProtT5 (an existing pre-trained protein language model). This is followed by the implementation of a graph neural network framework, which consists of three TransformerConv layers for feature extraction and a multilayer perceptron module for classification. The benchmark results reflect that TransPTM has the competitive performance for non-histone acetylation site prediction over three state-of-the-art tools. It improves our comprehension on the PTM mechanism and provides a theoretical basis for developing drug targets for diseases. Moreover, the created PTM datasets fills the gap in non-histone acetylation site datasets and is beneficial to the related communities. The related source code and data utilized by TransPTM are accessible at https://www.github.com/TransPTM/TransPTM.

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Metadata

Authors
Meng, L., Chen, X., Cheng, K., Chen, N., Zheng, Z., Wang, F., Sun, H., & Wong, K.-C.
Affiliations
  • City University of Hong Kong (HK)
  • Harvard University (US)
Corresponding author
  • Sun, Hongyan
  • Wong, Ka-Chun
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2024-03-27
Volume
25
Issue
3
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbae219
PMC
PMC11082075
OpenAlex
W4396809860
Cited by
31
References
6
Citations by year
2026: 9 · 2025: 16 · 2024: 6
FWCI
4.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • ChemRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • CityU Scholars (submittedVersion, cc-by-nc)
Funders
  • National Natural Science Foundation of China (32170654, 32000464)
  • Shenzhen Research Institute
  • City University of Hong Kong (2021SIRG036, CityU 9667265, CityU 11203221)
  • Research Grants Council of the Hong Kong Special Administrative Region (CityU 11203723)
  • Innovation and Technology Commission (ITB/FBL/9037/22/S)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
post-translational modification, protein modification prediction, benchmark dataset, ProtT5, protein language models, graph neural networks
MeSH terms
Acetylation, Algorithms, Humans, Protein Processing, Post-Translational, Proteins, Software, Neural Networks, Computer, Computational Biology, Databases, Protein

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