Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.10.08.681213

A UNIQUOME BASED METHOD FOR THE PROTEIN IDENTIFICATION BY MASS SPECTROMETRY

Abstract

Protein identification by mass spectrometry is a pivotal step in proteomics. Numerous methods have been developed to securely and effectively identify proteins derived from experimentally detected peptides by mass spectrometry. The dominant approach is based on the assumption that each experimentally identified peptide can be matched with a peptide included in a database of peptide sequences, generated by in silico digestion of proteins with a specific proteolytic enzyme. In this way, the protein containing the peptide can be identified. In a more advanced approach, the proteins and their in silico-digested peptides in the database are transformed into theoretical mass spectrometry spectra, and search engines match the experimentally obtained spectra to these theoretical spectra generated from protein and peptide sequences. We developed an alternative method for protein identification using Core Unique Peptides (CrUPs) and the Uniquome, termed as Uniquome-Based Protein Identification Method (UB-PIM). According to this method, instead of searching for peptides in the database of in silico-digested peptides, we search for CrUPs within the experimentally obtained peptides by mass spectrometry. If a peptide contains at least one CrUP, it can be directly correlated to the protein from which the CrUP is derived. Because of the unique nature of CrUPs, peptides obtained by MS can securely and uniquely identify the protein of origin. This provides a reference space in which even single-peptide identifications can achieve high specificity, reducing the ambiguity caused by shared or homologous sequences and improving the interpretability of MS data. Furthermore, UB-PIM can be applied to any type of peptide and is effective with both Data-Independent Acquisition (DIA) and Data-Dependent Acquisition (DDA) approaches, as well as with top-down and bottom-up proteomics. This allows confident protein identification from minimal evidence, expands the scope of detectable proteins, and remains computationally efficient, rapid, and universally applicable.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pierros, V., Kontopodis, E., Stravopodis, D. J., Tsangaris, G. T.. 2025-10-09. A UNIQUOME BASED METHOD FOR THE PROTEIN IDENTIFICATION BY MASS SPECTROMETRY. https://doi.org/10.1101/2025.10.08.681213

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Comparative functional genomics reveals distributed SERPINE1 like characteristics among Drosophila serpins

Serpins (serine protease inhibitors) regulate proteolytic activity through a suicide-substrate inhibitory mechanism, whereby the serpin covalently traps and inactivates their target protease. Serpins are broadly conserved across eukaryotes, but the evolutionary and functional relationships between human SERPINE1, and Drosophila Serpins in incompletely defined. To identify candidate Drosophila functional analogs of SERPINE1/PAI-1, a multi-omics framework was applied to thoroughly compare the serpins across species. The multi-level analysis integrated orthology prediction, protein sequence and structural characterization, phylogenetic reconstruction, bulk tissue transcriptomics, single-cell transcriptomics, and aging-associated expression analysis. Candidate serpins (Spn42Da, Spn42Db, Spn42Dc, Spn42Dd, Spn28B, and Spn28F) were evaluated using DIOPT, AlphaFold/ChimeraX structural comparisons, reactive center loop analyses, phylogenetic distances, GTEx, Human Protein Atlas, FlyAtlas2, Fly Cell Atlas, and Aging Fly Cell Atlas datasets. Comparative analyses demonstrated full-length sequence and reactive center loop (RCL) divergence despite preservation of the canonical serpin fold. Tissue, cell-type, and age-associated expression analysis revealed distinct candidate-specific patterns. Spn42Dd comparisons uncovered similarities to human SERPINE1 in sequence/structural and broad expression but did not represent the strongest overall cross-species expression correlations. Spn42Da and Spn42Db received the highest cross species integrated support. Quality check of the ranking confirmed that Spn42Da and Spn42Db were stable top candidates across orthology, sequence alignment, and expression domains. The multi-domain analysis was unable to identify a single definitive Drosophila serpin as the closest counterpart to human SERPINE1, indicating that cross-species similarity is distributed across multiple candidate serpins. This study therefore provides a quantitative method for prioritizing potential evolutionary and functional analogs across highly divergent species.

bioinformatics↗

CellChat Hotspot: A Targeted Approach to Cell Cell Communication Analysis With Spatial transcriptomics Validation.

Abstract. In recent years, single cell RNA sequencing (scRNA seq) has enabled the investigation of intercellular communication through Cell Cell Communication (CCC) tools such as CellChat. Despite their potential, these approaches remain challenging to interpret, particularly when global analyses generate complex interaction networks that may include biologically irrelevant signals. To address this limitation, we developed a framework termed CellChat Hotspot, designed to improve the biological interpretability of inferred communication networks. This approach integrates gene set enrichment analysis with a context driven filtering strategy, selecting both genes and cells based on their functional relevance prior to communication inference. Applied to stage II colon cancer data, CellChat Hotspot enabled the identification of condition-specific interaction patterns associated with recurrence. Non recurrent patients showed enrichment of immune related processes, whereas recurrent cases were characterized by pathways associated with invasion and metastasis. Notably, a loss of interaction in the HSPG pathway between CALB1+ and stromal cells was observed in recurrent patients, suggesting a potential disruption of cell matrix adhesion mechanisms. To verify the reliability of this finding, we applied the framework to spatial transcriptomics data, which confirmed the presence of the HSPG2 DAG1 interaction in non-recurrent tumors and its near absence in recurrent ones. Overall, this framework provides a biologically guided strategy for studying intercellular communication in complex systems such as the tumor microenvironment.

bioinformatics↗

DiffTernary: a Diffusion Model for Molecular Glue Ternary Complex Docking

Molecular glues (MGs) modulate protein function by inducing or stabilizing protein interactions. However, accurately modeling MG ternary complexes remains challenging. Here, we present DiffTernary, a diffusion-based docking method for the joint prediction of relative protein arrangement and MG binding pose. We introduce Ternary-PDB, a dataset of 76,702 experimentally determined ternary complexes curated from the Protein Data Bank using structural filters and a release cutoff aligned with AlphaFold 3 (AF3). When trained on this dataset, DiffTernary achieved acceptable protein-protein interaction (PPI) predictions (DockQ > 0.23) for 50.0% of MGBench complexes with a single prediction, compared with 51.1% for AF3. In comparisons with AF3, OpenFold3, ESMFold2, Protenix-v1 and Protenix-v2, DiffTernary ranked second with a best-of-25 PPI prediction success rate of 78.4%, behind ESMFold2 (90.9%) and ahead of AF3 (72.7%). A separate DiffTernary model trained on TernaryDB achieved a mean DockQ of 0.306 on the 94 complexes in the TernaryDB MGD test set with a single prediction, compared with 0.228 for DeepTernary. Confidence-based selection among 40 samples yielded acceptable PPI predictions for 41.5% of complexes, compared with 30.9% for DeepTernary. An E2Former variant increased training and sampling throughput 6.90-fold and 4.37-fold, respectively, under the evaluated configurations. Together, DiffTernary and Ternary-PDB provide an efficient docking approach for MG complex prediction and a valuable structural resource for MG discovery and design.

bioinformatics↗