{
  "abstract": "Plant ATP-binding proteins play a central role in various physiological processes in plants, including growth and development, energy metabolism, signal transduction, and environmental adaptation. Accurately identifying ATP-binding proteins from plant protein sequences is crucial for a deeper understanding of plant physiology and their applications in agricultural biotechnology. In this study, we propose ATPPred, a tool based on the protein large language model Evolutionary Scale Modeling 2 (ESM2) and a 1-dimensional convolutional neural network for the high-precision prediction of plant ATP-binding proteins. The tool extracts global features of plant ATP-binding proteins using ESM2 and then further extracts feature information with the 1-dimensional convolutional neural network. Comprehensive performance metrics demonstrate ATPPred's competitive prediction capability. A systematic evaluation of the impact of protein sequence similarity thresholds on ATPPred yielded an average AUC ranging from 0.9191 ± 0.0161 to 0.9669 ± 0.0030 across thresholds from 0.4 to 0.9, highlighting its robustness. Cross-dataset benchmarking of ATPPred further validated the effectiveness of our framework, showcasing superior performance compared to existing methods. In summary, ATPPred provides a feasible computational approach for sequence-based prediction of plant ATP-binding proteins and may serve as a useful auxiliary tool for the preliminary screening and prioritization of candidate ATP-binding proteins.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hong-Qi Zhang",
      "orcid": "0009-0000-5214-0855"
    },
    {
      "affs": [
        0
      ],
      "name": "Zi-Xuan Zhang",
      "orcid": "0009-0001-5311-7179"
    },
    {
      "affs": [
        0
      ],
      "name": "Yi-Xuan Qi"
    },
    {
      "affs": [
        0
      ],
      "name": "Cheng Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "Jian Huang",
      "orcid": "0000-0003-3282-8892"
    },
    {
      "affs": [
        0
      ],
      "name": "Hao Lin",
      "orcid": "0000-0001-6265-2862"
    },
    {
      "affs": [
        0
      ],
      "name": "Ke-Jun Deng",
      "orcid": "0000-0003-0411-549X"
    },
    {
      "affs": [
        0
      ],
      "name": "Juan Feng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8gma4xa612pvz.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27198566",
  "fulltext_url": "https://europepmc.org/articles/PMC13664943",
  "id": "8gma4xa612pvz",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://europepmc.org/article/MED/42883812",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7214353493",
  "pdf_url": "https://europepmc.org/articles/PMC13664943?pdf=render",
  "pmcid": "PMC13664943",
  "pmid": "42883812",
  "published": "2026-09-25",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8gma4xa612pvz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42883812",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42883812"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution.",
  "title_text": "ATPPred: Prediction of Plant ATP-Binding Proteins Using ESM2 and 1D Convolution.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/8gma4xa612pvz",
  "venue": "International journal of molecular sciences",
  "versions": []
}