A Dual-Background Enrichment Strategy for Phosphoproteomics Highlights Intrinsic, High-Confidence Kinase-Substrate Motifs by Mitigating Orthogonal Sources of Bias

Background: Distinguishing genuine kinase–substrate motifs from background noise is a growing challenge, as mass spectrometry (MS) -based global phosphoproteomics identifies a rapidly expanding set of phosphorylation sites. One of the major limitations is selecting an appropriate background model that systematically controls both tech-nical and biological sources of bias. Although using the entire proteome as a back-ground in FASTA format considers the overall amino acid composition, it is still prone to biases from protein abundance and the uneven distribution of sequence space (par-ticularly around low-abundance proteins). By contrast, internal background methods can control experiment-specific detection biases, but they may not fully capture resi-due-specific compositions or general trends in phosphorylation. Methods: I develop a Dual-Background Enrichment (DBE) strategy, which involves analyzing motif en-richment against two distinct background models: (1) A residue-heterogeneous inter-nal background composed of phospho-motifs centered on the residue, e.g., serine (S) motifs, against threonine (T) and tyrosine (Y) centered motifs. (2) A FASTA back-ground that includes all canonical S, T, and Y sites from the reference proteome. Re-sults: Motifs are classified as high confidence if they meet statistical significance (q ≤ 0.05) against both background models. Conclusion: By applying the DBE strategy to a large-scale phosphoproteomics dataset, we distinguish motifs driven by amino acid composition (enriched in FASTA background only) from those reflecting kinase sub-strate specificity (enriched in both backgrounds). This dual-reference approach reduces false positives arising from sequence composition bias and identifies high-confidence candidate kinase recognition motifs.