ProCAST: A Bioinformatics Suite for Mass Spectrometry-Based Protein Corona Proteomics Analysis

When nanoparticles are exposed to biological fluids, they spontaneously adsorb proteins, forming a protein corona that defines their biological identity and dictates cellular uptake, biodistribution, and toxicity. Characterizing protein coronas includes using proteomics approaches (e.g., LC-MS/MS) to identify proteins and generate vast lists of adsorbed proteins, often visualized via complex heatmaps. While heatmaps display data they do not offer heuristic guide, leaving the driving mechanisms of adsorption unknown. Moreover, interpretation of protein corona proteomics data remains limited by fragmented workflows, inconsistent preprocessing, and visual outputs that are often descriptive rather than readily interpretable. These conventional methods identify adsorbed proteins but fail to explain why specific proteins are selected or how they influence the particles biological fate. Here, we developed ProCAST (Protein Corona Analysis and Statistical Tool), an R-based framework for protein corona proteomics that integrates proteomics data, nanoparticle metadata, protein annotations, and multi-level visualization within a single analytical workflow. ProCAST facilitates abundant protein clustering based on sample conditions, sequence descriptors, property or protein correlations, and gene ontology-based functional visualization. It also distinguishes abundant proteins from frequent proteins, providing distinct layers of information from the same dataset. ProCAST was used to re-analyze previously published PAMAM G4 dendrimer-FBS datasets, demonstrating that ProCAST reproduces descriptor-level visualizations and offers new insights through clearer comparisons of functional patterns and hypothesis generation from dominant corona proteins. By organizing results as complementary views of the same dataset, ProCAST facilitates the shift of protein corona analysis from descriptive outputs toward structured, comparative, and experimentally testable interpretations.