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bioRxiv · 10.64898/2026.02.23.707401

RevelioPlots: An Interactive Web Application for Fast AI-Based Protein Models Quality Assessment

Fernandes, L. L. d. S.·Azevedo, A. H. D. d.·Franca, J. V. S. d.·Lima, J. P. M. S.
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Abstract

High-accuracy protein structure prediction by deep learning requires rigorous model quality assessment, a process currently hampered by fragmented, non-interactive tools designed for older experimental data formats. We present RevelioPlots, an open-source, interactive web application (Python/Streamlit) that simplifies and streamlines the assessment of AI-predicted protein structure quality. Its key feature is the combination of statistical pLDDT score analysis (mean, median, box plots) with an interactive, confidence-colored Ramachandran plot. This integration establishes a direct visual link between a models predicted local reliability (pLDDT) and its stereochemical feasibility (backbone geometry). RevelioPlots handles both individual and batch-uploaded models, intelligently falling back to B-factors as a proxy for pLDDT values. Using example model proteins, we demonstrated the tools effectiveness, revealing differences in reliability and a clear visual correlation between regions of low pLDDT scores and residues in sterically disallowed regions. By unifying these critical metrics, RevelioPlots empowers non-experienced researchers to quickly and intuitively assess, compare, and interpret structural model quality, enabling a more confident and integrated use of predicted data. AvailabilityRevelioPlots is available at revelioplots.streamlit.app, with the source code publicly accessible on GitHub at https://github.com/evomol-lab/RevelioPlots.

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Fernandes, L. L. d. S., Azevedo, A. H. D. d., Franca, J. V. S. d., Lima, J. P. M. S.. 2026-02-24. RevelioPlots: An Interactive Web Application for Fast AI-Based Protein Models Quality Assessment. https://doi.org/10.64898/2026.02.23.707401

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