PP-MAPS: Dynamic Pharmacophore Signatures of Protein–Peptide Interfaces from Molecular Dynamics Trajectories

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.

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BibTeX RIS

BibTeX

@article{depenveiller2026ppmaps,
  title = {{PP-MAPS: Dynamic Pharmacophore Signatures of Protein–Peptide Interfaces from Molecular Dynamics Trajectories}},
  author = {Camille Depenveiller and Arezki Guerda and Emilia Rabia and Aziza Caidi and Yaqoub Ashhab and Fathia Mami-Chouaib and Matthieu Montes},
  journal = {Journal of Chemical Information and Modeling},
  year = {2026},
  month = jun,
  doi = {10.1021/acs.jcim.6c00605},
  url = {https://hal.science/hal-05779391v1},
  note = {CC BY 4.0},
}

RIS

TY  - JOUR
TI  - PP-MAPS: Dynamic Pharmacophore Signatures of Protein–Peptide Interfaces from Molecular Dynamics Trajectories
AU  - Camille Depenveiller
AU  - Arezki Guerda
AU  - Emilia Rabia
AU  - Aziza Caidi
AU  - Yaqoub Ashhab
AU  - Fathia Mami-Chouaib
AU  - Matthieu Montes
PY  - 2026
DA  - 2026/06/26
JO  - Journal of Chemical Information and Modeling
DO  - 10.1021/acs.jcim.6c00605
UR  - https://hal.science/hal-05779391v1
AB  - 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.
SN  - 1549-9596
SN  - 1549-960X
ER  -

Abstract

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