Brian C. Searle

This study compares Scribe and database search engines in metaproteomic analysis of a ground-truth microbiome dataset, finding Scribe detects more proteins and quantifies microbial composition more accurately.

This study uses mass spectrometry to analyze proteomic changes in Pseudomonas aeruginosa under varying zinc availability, revealing insights into zinc homeostasis mechanisms.

This study introduces MAP-MS, a method to improve proteomic dynamic range using Orbitrap mass spectrometers without hardware or software changes, achieving nearly 2x dynamic range with minimal impact.

The paper develops a workflow for targeted proteomics using a hybrid quadrupole-linear ion trap instrument, demonstrating consistent quantification without high-mass accuracy.

This study compares DIA-MS and DDA-MS for metaproteomics, finding DIA-MS provides more reproducible and accurate protein and peptide identifications across laboratories.

The paper introduces nf-encyclopedia, a NextFlow pipeline for DIA proteomics analysis, showing it works well on cloud and local platforms and improves protein quantification with MSstats.

The paper introduces Chronologer, a deep learning tool for accurate peptide retention time prediction, outperforming existing methods by leveraging harmonized datasets of post-translational modifications.

The paper presents a high-quality quantitative proteomics dataset for Alzheimer's disease, including samples from different brain regions and clinical groups.

Scribe improves library searching for DDA experiments by predicting fragmentation and retention times for all peptides, outperforming traditional database searching in sensitivity and precision.

The paper identifies and characterizes small ORFs and microproteins in adipose cells, showing their metabolic relevance and potential roles in obesity and inflammation.

The paper develops a method combining linear ion traps and FAIMS for low-input proteomics, improving sensitivity and throughput for limited material samples.

This paper evaluates a DIA proteomics approach using three proteases to increase proteome sequence coverage, achieving higher peptide and amino acid detection rates compared to trypsin alone.

The paper evaluates DIA performance across multiple laboratories using standardized samples and provides benchmark datasets for education and tool development.

The paper introduces a method to quantify lysine and arginine PTMs in NASH using mass spectrometry and applies it to mouse liver lysates, finding altered methylation and acetylation in NASH.

This paper introduces a method to generate high-quality DIA-MS libraries using peptide predictions and empirical refinement, demonstrating its effectiveness in non-model organisms and non-canonical databases.

Thesaurus identifies phosphoprotein positional isomers by analyzing fragment ions from mass spectrometry data, revealing kinase-specific regulation in the PI3K/AKT pathway.

The paper improves metabolite identification in LC-MS datasets by incorporating in-source fragment information from MS/MS spectral libraries.

This study develops a method to quantify nuclear protein dynamics by isolating chromatin compartments and using mass spectrometry, revealing chromatin remodeling during BET protein degradation.