Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.08.09.552645

A Fit for Purpose Approach to Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics

Lundgren, T. J.·Clark, P. L.·Champion, M. M.
View original source ↗Export metadata ↓

Abstract

Amino acid substitutions (AAS) change a protein from its genome-expected sequence. Accumulation of substitutions in proteins underlie numerous diseases and antibiotic mechanisms. Accurate global detection of substitutions and their frequencies would help characterize these mechanisms. Measurement of AAS using shotgun proteomics is attractive due to its high sensitivity and untargeted acquisition. However, identifying substituted peptide-spectra requires search strategies that extrapolate beyond the genome, which can introduce bias. To characterize this bias, we constructed a "ground-truth" approach using the similarities between the Escherichia coli and Salmonella typhimurium proteomes to effectively model the complexity of distinguishing substitutions from genomic peptides. Shotgun proteomics on combined whole cell lysates from both organisms generated a library representing nearly 100,000 peptide-spectra and 4,161 distinct peptide sequences corresponding to genome-level single AAS with defined stoichiometry. We tested the ability to identify S. typhimurium peptide-spectra using only the E. coli genome in substitution-tolerant database searching. Overall, 64.1% of library peptides were correctly identified. We observed a wide range of identification efficiencies based on the specific AAS, but no inherent bias from stoichiometry of the substitution. Short peptides and substitutions near peptide termini, which require specific diagnostic ions for unambiguous identification, are matched with below-average frequency. We also identified "scissor substitutions" that gain or lose protease cleavage sites. Although scissor substitutions are chemically distinct from the genomic peptide, they had poor identification efficiency. This ground-truth AAS library identifies multiple sources of bias in AAS peptide-spectra identification and sets expectations for the application of shotgun proteomics to testing AAS hypotheses. Significance statementHigh-fidelity decoding of the genome is essential for life. Mistranslation leads to amino acid substitutions, which can disrupt protein folding and function, and impact cell fitness. Detection of mistranslated protein products necessitates robust and non-biased approaches. Proteomics is a promising solution, but identifying non-genomic peptide-spectra is a severe bioinformatics challenge. We created a ground-truth library of substituted amino acid peptides by mixing two closely related bacteria in a single sample. We quantitatively defined the degree to which informatics could correctly distinguish substituted peptides when single-organism databases are present. This approach defines intrinsic and informatics limits in substitution detection in shotgun proteomics and identifies previously overlooked challenges with identifying "scissor substitutions".

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lundgren, T. J., Clark, P. L., Champion, M. M.. 2023-08-13. A Fit for Purpose Approach to Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics. https://doi.org/10.1101/2023.08.09.552645

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

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗