{
  "abstract": "Microbial nonribosomal peptides (NRPs) exhibit remarkable structural diversity and are important sources of lead compounds for clinical drug development. The biosynthesis of NRPs relies on nonribosomal peptide synthetases (NRPSs), in which adenylation (A) domains define the core structure by selectively recognizing and activating amino acid substrates. Accurately predicting the substrate specificities of A-domains is thus essential for understanding the core structural and biosynthetic logic of NRPs. Here, we present DeepAden, a two-stage deep learning model. In the first stage, a graph attention network (GAT)-based model localizes 27-residue binding pockets within 6 Å of bound substrates and converts these into pocket representations. In the second stage, pocket representations are encoded alongside substrate information using pretrained language models and aligned using contrastive learning. We also applied a SHapley Additive exPlanations (SHAP)-guided data augmentation strategy to mitigate class imbalance, particularly focusing on nonproteinogenic substrates. DeepAden achieved competitive performance compared with state-of-the-art tools on a benchmark dataset and facilitated the annotation of two Streptomyces NRPS gene clusters by providing supportive A-domain substrate-specificity predictions. DeepAden provides a practical approach for pocket localization and substrate prediction and may facilitate the discovery and characterization of new NRP natural products. The DeepAden web server is available at https://deepnp.site/.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Biological Science and Technology, Advanced Institute of Natural Sciences, Beijing Normal University , Zhuhai, Guangdong 519087 ,",
    "State Key Laboratory of Drug Research & Natural Products Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Shanghai 201203 ,",
    "University of Chinese Academy of Sciences , Beijing 100049 ,",
    "Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences , Zhongshan 528400 ,"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiaquan Huang",
      "orcid": "0000-0001-7534-8739"
    },
    {
      "affs": [
        0
      ],
      "name": "Liangjun Ge"
    },
    {
      "affs": [
        0
      ],
      "name": "Y J Wu",
      "orcid": "0000-0003-3485-519X"
    },
    {
      "affs": [
        0
      ],
      "name": "Yi Tian",
      "orcid": "0009-0006-6423-4369"
    },
    {
      "affs": [
        0
      ],
      "name": "Jianmin Wu",
      "orcid": "0000-0002-0999-9194"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiandi Gao",
      "orcid": "0009-0001-6636-6844"
    },
    {
      "affs": [
        0
      ],
      "name": "Li Pan",
      "orcid": "0009-0000-4127-6626"
    },
    {
      "affs": [
        1,
        2,
        3
      ],
      "name": "Song Meng",
      "orcid": "0000-0002-6197-7689"
    },
    {
      "affs": [
        0
      ],
      "name": "Heqian Zhang",
      "orcid": "0000-0002-0196-240X"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhiwei Qin",
      "orcid": "0000-0002-1444-3684"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/be4rh1d58vrvn.bib",
  "cited_by": 7,
  "doi": "10.1093/nar/gkag656",
  "fulltext_url": null,
  "id": "be4rh1d58vrvn",
  "issns": [
    "0305-1048",
    "1362-4954",
    "1362-4962"
  ],
  "landing_url": "https://doi.org/10.1093/nar/gkag656",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2402|Microbial Natural Products and Biosynthesis"
    ]
  ],
  "openalex": "W7167489825",
  "pdf_url": null,
  "pmcid": "PMC13335487",
  "pmid": "42406630",
  "published": "2026-07-03",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/be4rh1d58vrvn.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T13:20:53Z",
      "native_id": "W7167489825",
      "source": "openalex",
      "url": "https://doi.org/10.1093/nar/gkag656"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:2402|Microbial Natural Products and Biosynthesis",
      "subfield": "microbiology"
    }
  ],
  "title": "DeepAden: an explainable machine learning method for predicting the substrate specificity of nonribosomal peptide synthetases",
  "title_text": "DeepAden: an explainable machine learning method for predicting the substrate specificity of nonribosomal peptide synthetases",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:46Z",
  "url": "https://research.draconitic.com/paper/be4rh1d58vrvn",
  "venue": "Nucleic Acids Research",
  "versions": []
}