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Peer-reviewedCC BY 4.0Scientific Data

A comprehensive dataset of 32 million pentapeptide structures for high-throughput virtual screening

  • Josep-Ramon Codina1,2
  • Emre Dikici1,2,3
  • Sapna K. Deo1,2,3
  • Sylvia Daunert1,2,3
  1. 1Department of Biochemistry and Molecular Biology, University of Miami, Miller School of Medicine, 1600 NW 10th Ave, Miami, FL 33136 USA
  2. 2Dr. John T. Macdonald Foundation Biomedical Nanotechnology Institute, 1951 NW 7th Ave#475, Miami, FL 33136 USA
  3. 3Sylvester Comprehensive Cancer Center, University of Miami, 1475 NW 12th Ave, Miami, FL 33136 USA
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Abstract

Small peptides are widely used as binders, modulators, and structural motifs, but their conformational flexibility complicates structure-based analysis and high-throughput screening. We present an open dataset of three-dimensional structures for the complete space of canonical amino-acid pentapeptides: 3,200,000 unique sequences with up to 10 conformers per sequence, for a total of 32,000,000 peptide conformers. Structures were generated directly from sequence using an automated workflow built on UCSF ChimeraX for model construction, Reduce for hydrogen placement, and RDKit for conformer generation and optimization. The dataset is distributed as compressed archives with an accompanying index that maps each sequence and conformer identifier to its coordinate record, enabling efficient download, subset selection, and programmatic access. Technical validation includes symmetry-aware inter-conformer RMSD analysis, Ramachandran quality assessment, and benchmarking against experimentally observed pentapeptide fragments from the Protein Data Bank. Although we focus here on pentapeptides to enable exhaustive sequence coverage, the publicly released workflow is solely based on open-source software and can be applied to other short peptides to generate comparable conformer libraries. This resource supports virtual screening with pre-generated peptide conformer ensembles, method benchmarking, and machine-learning applications in peptide design and protein engineering by removing the need for researchers to repeatedly generate large conformer ensembles from scratch.

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Cite

BibTeXRIS
BibTeX
@article{codina2026comprehensive,
  title = {{A comprehensive dataset of 32 million pentapeptide structures for high-throughput virtual screening}},
  author = {Josep-Ramon Codina and Emre Dikici and Sapna K. Deo and Sylvia Daunert},
  journal = {Scientific Data},
  year = {2026},
  month = jul,
  doi = {10.1038/s41597-026-07838-4},
  url = {https://doi.org/10.1038/s41597-026-07838-4},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A comprehensive dataset of 32 million pentapeptide structures for high-throughput virtual screening
AU  - Josep-Ramon Codina
AU  - Emre Dikici
AU  - Sapna K. Deo
AU  - Sylvia Daunert
PY  - 2026
DA  - 2026/07/07
JO  - Scientific Data
DO  - 10.1038/s41597-026-07838-4
UR  - https://doi.org/10.1038/s41597-026-07838-4
AB  - Small peptides are widely used as binders, modulators, and structural motifs, but their conformational flexibility complicates structure-based analysis and high-throughput screening. We present an open dataset of three-dimensional structures for the complete space of canonical amino-acid pentapeptides: 3,200,000 unique sequences with up to 10 conformers per sequence, for a total of 32,000,000 peptide conformers. Structures were generated directly from sequence using an automated workflow built on UCSF ChimeraX for model construction, Reduce for hydrogen placement, and RDKit for conformer generation and optimization. The dataset is distributed as compressed archives with an accompanying index that maps each sequence and conformer identifier to its coordinate record, enabling efficient download, subset selection, and programmatic access. Technical validation includes symmetry-aware inter-conformer RMSD analysis, Ramachandran quality assessment, and benchmarking against experimentally observed pentapeptide fragments from the Protein Data Bank. Although we focus here on pentapeptides to enable exhaustive sequence coverage, the publicly released workflow is solely based on open-source software and can be applied to other short peptides to generate comparable conformer libraries. This resource supports virtual screening with pre-generated peptide conformer ensembles, method benchmarking, and machine-learning applications in peptide design and protein engineering by removing the need for researchers to repeatedly generate large conformer ensembles from scratch.
SN  - 2052-4463
ER  -