Interactive protein-structure inspection and Ramachandran analysis, from experimental structures to AlphaFold and ESMFold predictions.
RamplotR is an open-source R Shiny application that brings backbone geometry, a 3D molecular viewer, residue-level validation and prediction confidence into one linked workspace. Click a residue in the plot, sequence, table or structure to inspect it everywhere. Compare conformations, examine multi-model ensembles and export figures or reproducible reports.
RamplotR's default publication palette. Example: PDB 1CRN. More screenshots.
Try the browser app · Get started · Explore the features · Scientific interpretation · Documentation
| Workflow | Capabilities |
|---|---|
| Explore a structure | Interactive φ/ψ plots with several reference-density datasets, residue-aware classification and instant amino-acid/chain filtering. |
| Inspect residues in context | Synchronized Ramachandran plot, searchable residue table, all-chain sequence navigator and NGL 3D viewer. Selected residues are highlighted and brought into focus; an issue queue helps navigate outliers and missing angles. |
| Work with predicted models | Import AlphaFold DB models by UniProt accession or upload AlphaFold 2/3, ColabFold and ESMFold structures. Examine pLDDT, and view a linked PAE heatmap when compatible confidence data are available. |
| Examine structural geometry | Explore peptide ω, side-chain χ1 and descriptive Cβ measurements. Optionally attach the matching deposited structure's official wwPDB validation report for independent rotamer, clash and geometry annotations. |
| Inspect experimental evidence | Overlay a local CCP4/MRC cryo-EM map in the 3D viewer as a qualitative aid, without uploading the map to a separate service. |
| Compare models | Sequence-align chains from two structures; inspect wrapped angular differences and changes in classification. For compatible multi-model structures, calculate circular φ/ψ variability and classification consistency. |
| Publish or automate | Export SVG and high-resolution PNG figures, filtered CSV tables and standalone HTML reports. Run the offline R command-line tool on individual files or a directory of structures. |
Advanced analysis stays in collapsible panels or dedicated comparison/summary views, keeping the everyday 2D/3D inspection screen uncluttered.
All images below are taken from real browser tests. The prediction-confidence example uses synthetic test data to demonstrate the interface, not a biological result.
| Linked residue inspection | All-chain sequence navigator |
|---|---|
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| Prediction confidence and PAE | Multi-model ensemble analysis |
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Install R (a current R 4.x release), clone the repository and start the Shiny application:
git clone https://github.com/BiKC/RamplotR.git
cd RamplotRinstall.packages(c(
"shiny", "shinyWidgets", "colourpicker", "bio3d",
"NGLVieweR", "DT", "jsonlite", "htmltools", "xml2"
))
shiny::runApp("shinyRam")Enter a four-character PDB ID (for example, 1CRN), upload your own PDB/mmCIF structure or choose AlphaFold DB to retrieve an available model by UniProt accession. Local uploads support .pdb, .ent, .cif, .mcif and .mmcif.
The project has also been hosted at bioit.shinyapps.io/RamplotR, but that deployment may not reflect the latest GitHub version. Running locally is the most reliable way to use the current implementation. Public-accession retrieval requires an internet connection; uploaded coordinates and local map files can be inspected without an external folding service.
Open RamplotR at bikc.be/RamplotR. The public version runs through Shinylive on static one.com hosting. R runs in your browser through webR, so no R installation is required. A first visit downloads and starts webR and its R packages; subsequent visits can reuse cached assets. Loading large structures still depends on the visitor's device.
The browser build uses the same five reference datasets and original RDS distributions as the desktop/server app. To avoid including all 110 distributions in the initial app.json, reference files are hosted separately and downloaded on first use. Every file is checked against the export's MD5 manifest, and loaded references are cached within the session. The full Plotly library starts downloading when you click Analyse instead of delaying the initial form. A small φ/ψ favicon matches the app's teal colour scheme.
For a reproducible deployment, run this from the repository root with the shinylive R package installed:
Rscript scripts/export-shinylive.R bikc.be https://bikc.be/RamplotR/reference-dataUpload the contents of the generated bikc.be/ directory to the site's document root on one.com, including RamplotR/reference-data/ and the shared shinylive/ assets. The export also includes optional, directory-scoped .htaccess files for gzip/Brotli (when available) and cautious browser caching; these do not alter the website's root configuration. one.com restricts some Apache features, so check the HTTP response headers after deployment rather than assuming that compression is active.
Ordinary PDB/mmCIF analysis does not require xml2. Importing official wwPDB validation XML does require it; compatibility of that optional feature should be checked in the specific exported webR build. The local/server Shiny app and the batch command remain available when a browser package is unsupported.
See the Shinylive export and one.com deployment guide for hosting checks, caching rules and troubleshooting.
From the repository root, process a structure or a directory containing supported structure files:
Rscript scripts/ramplotr-batch.R --input structures/ --output results/ --reportThe offline command produces per-residue CSV, machine-readable JSON and a batch summary; --report additionally requests SVG and standalone HTML reports. For a compatible multi-model structure, add --ensemble-models 20. Use --help for all options, including declared prediction provenance and an optional matching wwPDB validation XML.
See the batch-analysis instructions for examples and resource limits.
RamplotR's residue-aware mode evaluates general residues, glycine, proline and pre-proline against their corresponding bundled reference distributions. The selected plotting background is independent of those residue-specific classification calculations. The original density references trace back to the distributions discussed by Lovell et al. (2003); other bundled reference datasets can also be selected.
RamplotR region labels are not interchangeable with MolProbity or wwPDB classifications. They use different reference populations, residue treatments and region definitions. Our independent validation results, reproducible protocol and pinned experimental-structure corpus document agreement in calculated angles as well as differences in classification. For deposited experimental structures, attach the official report for the same structure and model when making independent quality assessments.
For predictions, pLDDT and PAE describe model confidence rather than experimental verification. ESMFold normally provides pLDDT but not PAE; experimental thermal B-factors are never automatically interpreted as prediction confidence. The native ω, χ1 and Cβ measurements are descriptive, and visualising a density map is not a quantitative map–model fit measurement.
The default RamplotR teal contour palette provides consistent, recognisable publication figures; changing colours never changes the scientific reference distribution or classification thresholds. Figure and report exports include relevant analysis settings and provenance.
- Interactive inspection, colours, comparisons and exports
- AlphaFold, ColabFold and ESMFold confidence analysis
- Geometry, official wwPDB evidence, cryo-EM overlays, ensembles and batch mode
- Independent wwPDB validation protocol and benchmark results · Results
- Performance and large-structure benchmarks · Scaling results
- Shinylive browser deployment and one.com caching
Developers can run the pure-R scientific regression suite from the repository root with Rscript tests/scientific.R. Additional tests cover structure parsing, validation imports, confidence formats, geometry, ensembles and the batch CLI. GitHub Actions also exercises the application in a real browser and runs the scientific tests on Ubuntu and Windows.
The v0.1.0-legacy tag preserves the historical version for reproducing earlier analyses. Record the exact RamplotR revision and reference dataset when publishing results.
RamplotR has been used to visualise protein models in the study Unveiling Intra-Clonal Diversity of Monkeypox Virus from Brazil's First Outbreak Wave (Witt et al., Viruses, 2026), including supplementary Ramachandran plots of viral polymerase and helicase models. This is an example of its research use, not an independent validation of the software.
When using RamplotR in a publication, cite the software repository and the exact version or commit you used, and report the selected reference dataset and analysis mode.
MIT. The bundled reference datasets and external validation reports retain their respective scientific provenance; consult the linked documentation when reusing those data.