Biology subjects

Baker, P. R.

Publications and source records attributed to Baker, P. R..

5 recordsLinked to original sources

bioRxiv2026

Optimization of Search and Acquisition Strategies for DSSO Crosslinking Mass Spectrometry using Protein Prospector and Touchstone.

Crosslinking mass spectrometry (XL-MS) enables residue-level characterization of protein-protein interactions (PPIs) in both purified assemblies and complex biological systems. Cleavable crosslinkers such as DSSO are widely used to facilitate analysis of complex samples, yet there remains limited consensus on how best to leverage their properties in both acquisition and data analysis workflows. In part, this reflects differences in scoring, classification, and false discovery rate (FDR) estimation across analysis pipelines. Therefore, an integrated XL-MS data analysis pipeline that enables confident identification of MS-cleavable cross-linked peptides through improved scoring and rigorous FDR assessment is essential for systematically comparing and benchmarking workflows across different sample preparations. To this end, we implemented a new capability within the Protein Prospector search engine to improve MS2-based identification of MS-cleavable cross-linked peptides. Next, we developed Touchstone, a framework for re-scoring, classification, FDR-controlled summarization, and visualization of Prospector XL-MS search results. Using this approach, we evaluate the contributions of DSSO-specific fragmentation features and compare multiple acquisition strategies across datasets ranging from purified macromolecular assemblies to proteome-scale samples. We find that the primary advantage of DSSO arises from enhanced backbone fragmentation of both linked peptides, rather than from diagnostic ion formation, and that incorporating diagnostic ion pairs into MS2-based scoring or filtering does not improve identification performance. Benchmarking against established datasets shows that Prospector-Touchstone performs comparably to leading methods for non-cleavable crosslinkers and provides improved performance for DSSO analysis. We further compare acquisition strategies and find that for purified protein assemblies, MS2.stHCD characterizes more residue-pairs than ETD-based, MS3-based, and hybrid methods. In contrast, for proteome-scale XL-MS, MS3-based acquisition yields similar numbers of inter-protein PPIs with lower error rates despite reduced sampling depth. Together, these results highlight complementary strengths of MS2- and MS3-based workflows and provide practical guidance for MS-cleavable XL-MS experiments with varying sample complexities.

bioinformatics↗

bioRxiv2025

Retrospective metabolomics via dual-dimensional deconvolution using ZT Scan DIA 2.0

We present a scanning data-independent acquisition (DIA) strategy, ZT Scan DIA, combined with dual-dimensional tandem mass spectrometry spectral filtering and deconvolution along both the quadrupole and retention time axes to reconstruct compound-specific MS2 spectra from complex mixtures. This approach is particularly effective for hydrophilic metabolomics data, where spectral similarity-based annotation is widely used, increasing annotation rates by 119-193% compared with conventional data-dependent acquisition (DDA) and window-based DIA methods. In lipidomics, deconvolution improved annotation precision by removing contaminant product ions and enabled separate quantification of co-eluting isomers using MS2 chromatograms, although common diagnostic ions could also be erroneously removed. Nevertheless, optimization of analysis parameters minimized this negative effect. Furthermore, we developed a practical data processing pipeline in which raw ZT Scan DIA-MS2 chromatograms are directly used for isomer separation and MS2-based quantification, covering 1,393 and 3,020 molecules for human plasma and mouse liver tissues, respectively. All data processing steps, including direct import of vendor raw data, are supported in MS-DIAL.

bioengineering↗

bioRxiv2021

Application of Parallel Reaction Monitoring in 15N labeled Samples for Quantification

Accurate relative quantification is critical in proteomic studies. The incorporation of stable isotope 15N to plant-expressed proteins in vivo is a powerful tool for accurate quantification with a major advantage of reducing preparative and analytical variabilities. However, 15N labeling quantification has several challenges. Less identifications are often observed in the heavy labeled samples because of incomplete labeling, resulting in missing values in reciprocal labeling experiments. Inaccurate quantification can happen when there is contamination from co-eluting peptides or chemical noise in the MS1 survey scan. These drawbacks in quantification can be more pronounced in less abundant but biologically interesting proteins, which often have very few identified peptides. Here we demonstrate the application of parallel reaction monitoring (PRM) to 15N labeled samples on a high resolution, high mass accuracy Orbitrap mass spectrometer to achieve reliable quantification even of low abundance proteins in samples.

plant biology↗

bioRxiv2021

15N metabolic labeling quantification workflow in Arabidopsis using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using 15N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the 15N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector. Further, we discuss several important features of 15N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

plant biology↗

bioRxiv2019

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff.

bioinformatics↗