Peptide-to-Protein Data Aggregation Using Fisher's Method Improves Target Identification in Chemical Proteomics

Protein-level statistical tests in proteomics, aimed at obtaining p-values, are conventionally made on protein abundances aggregated from peptide data. This integral approach overlooks peptide-level heterogeneity and ignores important information coded in individual peptide data, while protein p-values can also be obtained by Fisher's method of combining peptide p-values using chi-square statistics. Here, we test this latter approach across diverse chemical proteomics data sets based on assessments of protein expression, solubility, and protease accessibility. Using the top four peptides ranked by their p-values consistently outperformed protein-level analysis and avoided biases introduced by the inclusion of deviant peptides or the imputation of missing peptide values. Fisher's method provides a simple and robust strategy, improving identification of regulated/shifted proteins in diverse proteomics assays.