{
  "abstract": "Methyltransferases (MTs) are a functionally diverse class of enzymes that catalyse methyl group transfer from S-adenosyl-L-methionine (SAM) to DNA, RNA, and proteins, thereby regulating epigenetic programming, gene expression, and numerous disease processes. Dysregulation of these enzymes is implicated in a wide spectrum of human cancers and neurodegenerative disorders, making their accurate computational identification a priority in both basic research and drug discovery. We present GAC-BiT-CNN, a novel hybrid deep learning framework integrating Generative Adversarial Capsule Networks (GAC), Bidirectional Transformers (BiT), and Convolutional Neural Networks (CNN). The model leverages high-dimensional protein embeddings from four protein language models, including ProtGen, ESM-2, ProtT5-BFD, and ProtT5-XL, used individually, together with a fused feature set formed by concatenating the ProtT5-BFD and ProtT5-XL embeddings. All architectural components were optimised via systematic grid search hyperparameter tuning, and SHapley Additive exPlanations (SHAP)-based analysis was applied to the fused embeddings to identify the most discriminative feature dimensions. GAC-BiT-CNN with the fused feature set achieved 98.34% accuracy on the training set and 95.50% accuracy on the held-out test set, with an area under the receiver operating characteristic curve (AUC) of 98.70% and a Matthews correlation coefficient (MCC) of 0.901, surpassing all baseline models by a substantial margin. GAC-BiT-CNN provides a scalable, alignment-free framework with representation-level SHAP attribution for high-throughput methyltransferase identification, with direct applications in epigenomics, drug discovery, and cancer biology.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Bahria University, Islamabad 44000, Pakistan",
    "Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia",
    "Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia",
    "Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 23890, Saudi Arabia",
    "Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi Arabia"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Muhammad Muazzam",
      "orcid": "0009-0001-1571-0158"
    },
    {
      "affs": [
        0
      ],
      "name": "Farman Ali",
      "orcid": "0000-0002-0914-1577"
    },
    {
      "affs": [
        1
      ],
      "name": "Raed Alsini",
      "orcid": "0000-0002-3163-575X"
    },
    {
      "affs": [
        2
      ],
      "name": "Naif Waheb Rajkhan"
    },
    {
      "affs": [
        3
      ],
      "name": "Omar Alghushairy",
      "orcid": "0000-0002-7378-5545"
    },
    {
      "affs": [
        4
      ],
      "name": "Tamim Alkhalifah",
      "orcid": "0000-0001-8407-2068"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1bnp5v6yqmrg6.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27219967",
  "fulltext_url": null,
  "id": "1bnp5v6yqmrg6",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://www.mdpi.com/1422-0067/27/21/9967",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/1bnp5v6yqmrg6.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:21:27Z",
      "native_id": "10.3390/ijms27219967",
      "source": "crossref",
      "url": "https://www.mdpi.com/1422-0067/27/21/9967"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "issn:1422-0067",
      "subfield": "general"
    }
  ],
  "title": "GAC-BiT-CNN: A Hybrid Generative Adversarial Capsule–Transformer–CNN Model with Fused Protein Language Model Embeddings for Methyltransferase Prediction",
  "title_text": "GAC-BiT-CNN: A Hybrid Generative Adversarial Capsule–Transformer–CNN Model with Fused Protein Language Model Embeddings for Methyltransferase Prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-11-10T00:21:27Z",
  "url": "https://research.draconitic.com/paper/1bnp5v6yqmrg6",
  "venue": "International Journal of Molecular Sciences",
  "versions": []
}