{
  "abstract": "Protein-peptide interactions underlie many cellular signaling and regulatory processes and are increasingly exploited in drug discovery. Characterizing such interfaces often requires analyzing ensembles of conformations obtained from molecular modeling or molecular dynamics (MD) simulations, in which transient contacts and alternative binding modes can be critical. Pharmacophore models provide an intuitive, transferable representation of molecular interactions. Dynophore, i.e., \"dynamic pharmacophore\", approaches have been developed for small-molecule ligands with MD information. We present PP-MAPS (Protein-Peptide Molecular dynamics Assisted Pharmacophore Signatures), an open-source workflow that extracts and aggregates pharmacophore interactions along MD trajectories of proteinpeptide complexes. PP-MAPS produces per-residue interaction frequencies and pharmacophore heatmaps that facilitate comparison of peptides, binding sites, and receptor variants. PP-MAPS is implemented in Python and is available under an open-source license at https:// github.com/camilledepenveiller/PP-MAPS. The workflow relies on GROMACS for trajectory processing and can use either LigandScout or the Chemical Data Processing Toolkit (CDPKit) for pharmacophore feature detection.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Camille Depenveiller"
    },
    {
      "name": "Arezki Guerda"
    },
    {
      "name": "Emilia Rabia"
    },
    {
      "name": "Aziza Caidi"
    },
    {
      "name": "Yaqoub Ashhab"
    },
    {
      "name": "Fathia Mami-Chouaib"
    },
    {
      "name": "Matthieu Montes"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/br7ctcxnf5xs5.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.jcim.6c00605",
  "fulltext_url": null,
  "id": "br7ctcxnf5xs5",
  "issns": [
    "1549-9596",
    "1549-960X"
  ],
  "landing_url": "https://hal.science/hal-05779391v1",
  "language": "en",
  "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": [
    [
      "hal",
      "0.chim"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "hal",
      "1.chim.chem"
    ],
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      "hal",
      "1.sdv.bibs"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7166034750",
  "pdf_url": "https://hal.science/hal-05779391/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-26",
  "publisher": "American Chemical Society",
  "ris": "https://research.draconitic.com/paper/br7ctcxnf5xs5.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:48Z",
      "native_id": "hal-05779391",
      "source": "hal",
      "url": "https://hal.science/hal-05779391v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.bibs",
      "subfield": "bioinformatics"
    },
    {
      "field": "chemistry",
      "origin": "hal:0.chim",
      "subfield": "general"
    }
  ],
  "title": "PP-MAPS: Dynamic Pharmacophore Signatures of Protein–Peptide Interfaces from Molecular Dynamics Trajectories",
  "title_text": "PP-MAPS: Dynamic Pharmacophore Signatures of Protein–Peptide Interfaces from Molecular Dynamics Trajectories",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:48Z",
  "url": "https://research.draconitic.com/paper/br7ctcxnf5xs5",
  "venue": "Journal of Chemical Information and Modeling",
  "versions": []
}