Biology subjects

Chalkley, R. J.

Publications and source records attributed to Chalkley, R. J..

10 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

ADAR1 Restricts Poliovirus Infection through dual disruption of translation initiation and protein coding

Viral RNAs (vRNAs) interact with hundreds of host proteins, but how these interactions shape viral infection remains largely unknown. Here we developed a mass-spectrometry approach to identify cellular RNA-binding proteins (RBPs) that enhance or inhibit infection by modulating vRNA translation. We identified 130 RBPs that associate with polysomes in human cells infected with poliovirus, including known regulators of cap-independent translation from the viral internal ribosome entry site (IRES). We find that adenosine deaminase acting on RNA (ADAR1) is recruited to polysomes during infection and edits 3 sites in the vRNA, reducing viral replication. Two edits occur in the IRES and impair internal translation initiation, while the third edit occurs in the coding region and triggers an amino acid substitution. Incorporation of this threonine-to-alanine mutation into the viral genome attenuated replication. We conclude that ADAR1 restricts poliovirus infection by both reducing vRNA translation and introducing a recurrent coding error.

cell biology↗

bioRxiv2025

Assessment of Reported Error Rates in Crosslinking Mass Spectrometry

We benchmarked eight software tools in crosslinking mass spectrometry (MS) to evaluate their accuracy in detecting protein-protein interactions. Whereas about half performed reliably, others reported up to 31-fold more false interactions than indicated by their reported false discovery rates. During this community-driven effort, we helped correct one of the poorer-performing tools. Our findings demonstrate that, with robust algorithms, crosslinking MS can deliver highly dependable insights into biological systems.

bioinformatics↗

bioRxiv2024

Comparative Analysis of Glycoproteomic Software Using a Tailored Glycan Database

Glycoproteomics is a rapidly developing field, and data analysis has been stimulated by several technological innovations. As a result, there are many software tools from which to choose; and each comes with unique features that can be difficult to compare. This work presents a head-to-head comparison of five modern analytical software: Byonic, Protein Prospector, MSFraggerGlyco, pGlyco3, and GlycoDecipher. To enable a meaningful comparison, parameter variables were minimized. One potential confounding variable is the glycan database that informs glycoproteomic searches. We performed glycomic profiling of the samples and used the output to construct matched glycan databases for each software. Up to 19,000 glycopeptide spectra were identified across three replicates of wild-type SH-SY5Y cells. There was substantial overlap among most software for glycoproteins identified, locations of glycosites, and glycans, although Byonic reported a suspiciously large number of glycoproteins and glycosites of questionable reliability. We show that Protein Prospector identified the most glycopeptide spectrum matches with high agreement to known glycosites in UniProt. Overall, our results indicate that glycoproteomic searches should involve more than one software to generate confidence. It may be useful to consider software with peptide-first approaches and with glycan-first approaches.

bioinformatics↗

bioRxiv2024

Assessing Heterogeneity in the N-Telopeptides of Type I Collagen by Mass Spectrometry

Collagen cross-links created by the lysyl oxidase and lysyl hydroxylase families of enzymes are a significant contributing factor to the biomechanical strength and rigidity of tissues, which in turn influence cell signaling and ultimately cell phenotype. In the clinic, the proteolytically liberated N-terminal cross-linked peptide of collagen I (NTX) is used as a biomarker of bone and connective tissue turnover, which is altered in several disease processes. Despite the clinical utility of these collagen breakdown products, the majority of the cross-linked peptide species have not been identified in proteomic datasets. Here we evaluate several parameters for the preparation and identification of these peptides from the collagen I-rich Achilles tendon. Our refined approach involving chemical digestion for protein solubilization coupled with mass spectrometry allows for the identification of the NTX cross-links in a range of modification states. Based on the specificity of the enzymatic cross-linking reaction we utilized follow-up variable modification searches to facilitate identification with a wider range of analytical workflows. We then applied a spectral library approach to identify differences in collagen cross-links in bovine pulmonary hypertension. The presented method offers unique opportunities to understand extracellular matrix remodeling events in development, aging, wound healing, and fibrotic disease that modulate collagen architecture through lysyl-hydroxylase and lysyl-oxidase enzymes.

biochemistry↗

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↗

bioRxiv2021

Community Evaluation of Glycoproteomics Informatics Solutions Reveals High-Performance Search Strategies of Glycopeptide Data

Glycoproteome profiling (glycoproteomics) is a powerful yet analytically challenging research tool. The complex tandem mass spectra generated from glycopeptide mixtures require sophisticated analysis pipelines for structural determination. Diverse software aiding the process have appeared, but their relative performance remains untested. Conducted through the HUPO Human Proteome Project - Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates the performance of informatics solutions for system-wide glycopeptide analysis. Mass spectrometry-based glycoproteomics datasets from human serum were shared with all teams. The relative team performance for N- and O-glycopeptide data analysis was comprehensively established and validated through orthogonal performance tests. Excitingly, several high-performance glycoproteomics informatics solutions were identified. While the study illustrated that significant informatics challenges remain, as indicated by a high discordance between annotated glycopeptides, lists of high-confidence (consensus) glycopeptides were compiled from the standardised team reports. Deep analysis of the performance data revealed key performance-associated search variables and led to recommendations for improved "high coverage" and "high accuracy" glycoproteomics search strategies. This study concludes that diverse software for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies, and specifies key variables that may guide future software developments and assist informatics decision-making in glycoproteomics.

biochemistry↗

bioRxiv2020

Characterization of Prenylated C-terminal Peptides Using a Novel Capture Technique Coupled with LCMS

Post-translational modifications play a critical and diverse role in regulating cellular activities. Despite their fundamentally important role in cellular function, there has been no report to date of an effective generalized approach to the targeting, extraction and characterization of the critical c-terminal regions of natively prenylated proteins. Various chemical modification and metabolic labelling strategies in cell culture have been reported. However, their applicability is limited to cell culture systems and does not allow for analysis of tissue samples. The chemical characteristics (hydrophobicity, low abundance, highly basic charge) of many of the c-terminal regions of prenylated proteins have impaired the use of standard proteomic workflows. In this context, we sought a direct approach to the problem in order to examine these proteins in tissue without the use of labelling. Here we demonstrate that prenylated proteins can be captured on chromatographic resins functionalized with mixed disulfide functions. Protease treatment of resin-bound proteins using chymotryptic digestion revealed peptides from many known prenylated proteins. Exposure of the protease-treated resin to reducing agents and hydro organic mixtures released c-terminal peptides with intact prenyl groups along with other enzymatic modifications expected in this protein family. Database and search parameters were selected to allow for c-terminal modifications unique to these molecules such as CAAX box processing and c-terminal methylation. In summary, we present a direct approach to enrich and obtain information at a molecular level of detail about prenylation of proteins from tissue and cell extracts using high performance LCMS without the need for metabolic labeling and derivatization. O_FIG O_LINKSMALLFIG WIDTH=190 HEIGHT=200 SRC="FIGDIR/small/908152v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@12579f2org.highwire.dtl.DTLVardef@16934ccorg.highwire.dtl.DTLVardef@94b23org.highwire.dtl.DTLVardef@23f329_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

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↗