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

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.