{
  "abstract": "Motivation: The turnover number (kcat) is crucial for understanding enzyme kinetics, yet its experimental determination remains costly and low-throughput. Existing deep learning approaches often rely on single-substrate assumptions and overlook full reaction contexts or language-informed semantic representations, limiting their accuracy and generalization across diverse reactions.\n\nResults: To address these challenges, we propose Katalyst, a knowledge-guided semantic alignment framework for kcat prediction based on contrastive learning. Katalyst integrates enzyme sequences, protein textual descriptions, substrates and products into a shared latent space using multi-channel encoders and contrastive alignment. An interaction-aware fusion module is designed to capture complementary cross-modal information. Experimental results demonstrate that Katalyst achieved the best overall performance, with the RMSE of 0.9880, R2 of 0.5829, and SRCC of 0.7603. Compared with the State-Of-The-Art (SOTA) baselines, Katalyst reduced RMSE by 3.01% and improved R2 and SRCC by 4.72% and 2.31%, respectively. Moreover, ablation studies and evaluations across sequence scales further confirmed the effectiveness of semantic alignment and multimodal feature fusion. Overall, Katalyst provides a robust and accurate framework for kcat prediction.\n\nAvailability and implementation: The code and data are available at https://github.com/liruilin-SUAT/Katalyst.",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer Science and Software Engineering, Shenzhen University , Shenzhen, 518060,",
    "School of Computer Science and Control Engineering, Shenzhen University of Advanced Technology , Shenzhen, 518107,",
    "Department of Computer Science and Software Engineering, Southern University of Science and Technology , Shenzhen, 518055,",
    "School of Computer Science, Nanjing University , Nanjing, 210023,",
    "School of Computer Science and Engineering, Central South University , Changsha, 410083,",
    "School of Chinese Materia Medica, Beijing University of Chinese Medicine , Beijing, 102488,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ruilin Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Senyu Tang"
    },
    {
      "affs": [
        2
      ],
      "name": "Jiaqi Deng"
    },
    {
      "affs": [
        3
      ],
      "name": "Taixing Qiu"
    },
    {
      "affs": [
        4
      ],
      "name": "Fei Guo",
      "orcid": "0000-0001-8346-0798"
    },
    {
      "affs": [
        1
      ],
      "name": "Jijun Tang"
    },
    {
      "affs": [
        5
      ],
      "name": "Xiaoyi Liu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/28mb74gjedny6.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag709",
  "fulltext_url": null,
  "id": "28mb74gjedny6",
  "issns": [
    "1367-4803",
    "1367-4811"
  ],
  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag709/8860687",
  "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",
      "1312|Machine Learning in Bioinformatics"
    ]
  ],
  "openalex": "W7220392455",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag709/71581565/btag709.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/28mb74gjedny6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag709",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag709/8860687"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Katalyst: knowledge-guided semantic alignment via contrastive learning for enzyme turnover prediction",
  "title_text": "Katalyst: knowledge-guided semantic alignment via contrastive learning for enzyme turnover prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:38Z",
  "url": "https://research.draconitic.com/paper/28mb74gjedny6",
  "venue": "Bioinformatics",
  "versions": []
}