Phosphorylation, a crucial post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphorylated site localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to become integrated and adapted for phosphoproteomics datasets. Here, we present InstaNovo-P, a phosphorylation specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P surpasses existing methods in phosphorylated peptide detection and phosphorylated site localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylated sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. We experimentally validate our model predictions by studying FGFR2 signaling, further demonstrating that InstaNovo-P uncovers phosphorylated sites previously missed by traditional database searches. These predictions align with critical biological processes, confirming the model’s capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways.

The type III-E CRISPR-controlled protease Craspase is distinguished from other type III systems by its single-subunit RNA-guided protein complex and direct coupling of RNA recognition to protease activation without second messenger signaling, making it an attractive development platform for bioengineering and therapeutics. Here, we identify five positions within the CRISPR RNA (crRNA) of Craspase from Candidatus "Scalindua brodae" (Sb-Craspase) that are sensitive to single-nucleotide mismatches. We leverage these positions to design crRNAs that selectively target clinically relevant single-nucleotide variants (SNVs) in oncogenic RNA transcripts. Using this approach, Sb-Craspase is selectively activated by the "undruggable" KRAS G12D SNV, while the wild-type transcript does not induce protease activation. Collectively, our results establish a framework for designing crRNAs to target clinically relevant SNVs, laying the groundwork for Craspase-based diagnostics and therapeutics against otherwise intractable oncogenic mutations.

Dysregulations within the epidermal proteolytic network can cause hyperproliferative and inflammatory disorders. Although the metalloprotease meprin α is localized in the stratum basale in healthy skin, increased levels are found in the upper epidermal layers in wound healing and psoriatic lesions. To investigate a link between meprin α expression and keratinocyte proliferation, we developed a mouse model for inducible expression of pathological meprin α levels (ie, K5Mα mice). K5Mα mice developed a skin phenotype characterized by hyperkeratosis, acanthosis, parakeratosis, and barrier defect. Keratinocyte hyperproliferation and local inflammation were induced upon induction of meprin α expression. By N-terminomics, we identified dermokine, a regulator of keratinocyte proliferation and epidermal immune response, as a putative substrate of meprin α. We validated the proteolysis and identified the cleavage site, which is highly conserved in mammals, suggesting that dermokine degradation by meprin α represents a central mechanism in wound healing and hyperproliferative skin diseases.

Proteolytic cleavage is an irreversible post-translational modification (PTM), and dysregulation of protease activity is often a hallmark in disease. Aberrant proteolysis can alter protein abundance or function, disturbing cellular state and resulting in disease-specific biomarkers or therapeutic targets. Positional proteomics facilitates global identification and precise quantification of position-specific peptides, such as those located N- or C-terminal in the protein sequence. These techniques enable the study of both natural and neo-protein termini, as well as associated PTMs. Despite its importance, proteolysis remains understudied due to experimental challenges and complex data processing. In this review, we outline key strategies for data analysis and processing in positional proteomics, emphasizing how identification, quantification, and interpretation of proteolytic cleavage sites differ from standard proteomics data analysis pipelines. We discuss differences in common approaches for terminomics-focused workflows, comparing N- versus C-terminomics, as well as different labeling strategies and acquisition methods. Additionally, we highlight considerations for proper normalization approaches, specifically the need to normalize cleavage abundances relative to protein and protease abundance. We explain the importance of integrating structural data, solvent accessibility, and tissue expression profiles during data analysis to better evaluate the biological significance of experimental results.