BiologybioRxiv
Ranked by formulaVerdict: NotablePROTFIG: one-command, publication-quality biomolecular figures
Molecular structures contain rich information about biomolecular interactions, but translating this information into clear, scientifically informative figures remains a largely manual process.
Caveats
- Preprint; not yet peer reviewed.
VerdictWorth a reader's time today.
Abstract
Molecular structures contain rich information about biomolecular interactions, but translating this information into clear, scientifically informative figures remains a largely manual process. Producing publication-quality illustrations requires decisions about molecular representations, colors, structural features, camera orientation, and annotations, often involving extensive user interaction, expertise and limiting reproducibility. Here, we introduce PROTFIG, an open-source, PyMOL-based command-line tool that transforms biomolecular structures into scientifically informative, reproducible, and editable figures with minimal manual intervention. Given a structure file, PDB identifier or PyMOL scene, PROTFIG automatically identifies relevant molecular components and structural features, selects appropriate visual representations, and generates publication-quality figures using sixteen customizable presets. These cover a broad range of structural contexts, including protein-ligand and protein-peptide interactions, binding pockets, electrostatic surfaces, protein-protein interfaces, oligomeric assemblies, and membrane proteins. Figures can be customized through command-line options or TOML configuration files, while saved PyMOL sessions preserve full editability. We demonstrate the versatility of PROTFIG using representative structures relevant to drug discovery and public health, including GLP-1 receptor agonists, targeted protein degraders, and H5N1 influenza hemagglutinin. By combining automated structural interpretation with reproducible visualization workflows, PROTFIG makes the generation of scientifically informative molecular figures more accessible, consistent, and efficient.
The editor's rubric
- Leverage4 / 5
- A general-purpose tool used across several fields (Adam, ResNet, LoRA, next-generation sequencing).
- Magnitude2 / 5
- Solid incremental gain on a meaningful problem.
- Evidence3 / 5
- Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
- Novelty3 / 5
- A genuinely new approach to an open problem.
- Trajectory3 / 5
- A clear path to scale.
- Stakes2 / 5
- Benefits a professional community (practitioners, clinicians, engineers).
Editor’s rationale
Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose, we report); breadth (wide range); novelty (discovery); verification (independent replication, code released); scale (efficient); stakes (global scale).
How the score was computed
Desks place stronger verdicts first, then use this score to order work within the same verdict.
- Merit
- 6.0 / 10
- Adjusted merit
- 4.9 / 10
- Attention
- 0%
- Freshness
- 88%