Biology subjects

Polasky, D. A.

Publications and source records attributed to Polasky, D. A..

9 recordsLinked to original sources

bioRxiv2026

Expanding Glycopeptide Identification with Match-Between-Glycans in FragPipe

Glycosylation is one of the most important, but also most complex, post-translational modifications of proteins, playing a pivotal role in various pathological processes. Mass spectrometry-based large-scale glycoproteomics analysis offers a powerful approach to explore the fundamental roles of glycosylation in both physiological and pathological contexts. Traditionally, DDA glycopeptide assignment relies on information-dense MS2 spectra, containing sufficient fragmentation information to identify both the peptide and glycan moieties. Achieving this fragmentation can be difficult, especially for low-abundant glycopeptides and/or large, complex glycans. These glycopeptides are often not assigned using current data analysis software, yet they can be of biological relevance. Here, we introduce a method called match-between-glycans (MBG), which expands glycopeptide identification while maintaining the existing glycoproteome analysis workflow. MBG enables expanding the set of identified glycopeptides to include those without MS2 spectra, or with lower quality MS2 spectra, by looking for MS1 signals displaced from other identified glycopeptides by one or multiple monosaccharide unit(s). MBG can also identify glycans not included in the glycan database, such as those containing adducts or modifications, allowing these glycans to be recovered without a drastic expansion of the search space. Combined with target-decoy FDR control, we show this method is capable of accurately expanding glycopeptide identifications and providing a more complete quantitative profile of glycosylation at each glycosite. MBG is fully integrated into the glycoproteomics workflows in FragPipe, allowing seamless, one-click operation.

bioinformatics↗

bioRxiv2025

A New Detailed Mass Offset Search in MSFragger for Improved Interpretation of Complex PTMs

Conventional database search methods for proteomics struggle when tasked to identify dozens or hundreds of modifications simultaneously. Open or error-tolerant searches can address this limitation, but at the cost of increased difficulty in downstream interpretation of the results and quantification. We and others have previously described "mass offset" or multi-notch searches that sit in between closed and open searches, allowing simultaneous search for hundreds of modifications with more straightforward downstream interpretation than open search. The original mass offset searches were closer to open search, lacking the ability to restrict modifications to specific amino acids. Here, we describe a new "detailed" mass offset (DMO) search implemented in the MSFragger search engine, which allows each mass offset to have its own site restrictions and fragmentation rules. The benefits of DMO search over existing mass offset searches are shown with three example searches of complex modification sets: nearly one hundred post-translational modifications, fast photochemical oxidation of proteins (FPOP)-derived modifications, and amino acid substitutions. The DMO search further improves the interpretability of results by reducing ambiguity in site localization, particularly when modifications have overlapping masses, and provides benefits that scale with the complexity of the search.

bioinformatics↗

bioRxiv2023

Efficient Analysis of Proteome-wide FPOP Data by FragPipe

Monitoring protein structure before and after perturbations can give insights into the role and function of proteins. Fast photochemical oxidation of proteins (FPOP) coupled with mass spectrometry (MS) allows monitoring of structural rearrangements by exposing proteins to OH radicals that oxidize solvent accessible residues, indicating protein regions undergoing movement. Some of the benefits of FPOP include high throughput and lack of scrambling due to label irreversibility. However, the challenges of processing FPOP data have thus far limited its proteome-scale uses. Here, we present a computational workflow for fast and sensitive analysis of FPOP datasets. Our workflow combines the speed of MSFragger search with a unique hybrid search method to restrict the large search space of FPOP modifications. Together, these features enable more than 10-fold faster FPOP searches that identify 50% more modified peptide spectra than previous methods. We hope this new workflow will increase the accessibility of FPOP to enable more protein structure and function relationships to be explored.

bioinformatics↗

bioRxiv2022

HLA-Glyco: A large-scale interrogation of the glycosylated immunopeptidome

MHC-associated peptides (MAPs) bearing post-translational modifications (PTMs) have raised intriguing questions regarding their attractiveness for targeted therapies. Here, we developed a novel computational glyco-immunopeptidomics workflow that integrates the ultrafast glycopeptide search of MSFragger with a glycopeptide-focused false discovery rate (FDR) control. We performed a harmonized analysis of 8 large-scale publicly available studies and found that glycosylated MAPs are predominantly presented by the MHC class II. We created HLA-Glyco, a resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. Our comprehensive resource reveals high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between classical HLA class II allele groups. To support the nascent field of glyco-immunopeptidomics, we include the optimized workflow in the FragPipe suite and provide HLA-Glyco as a free web resource.

immunology↗

bioRxiv2022

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Post-translational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry (MS)-based proteomics, largely relying on traditional database search methods to annotate resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem MS experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new Labile Mode in the MSFragger search engine that can tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum annotation rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

bioinformatics↗

bioRxiv2022

Mining for ions: diagnostic feature detection in MS/MS spectra of post-translationally modified peptides

Post-translational modifications (PTMs) are an area of great interest in proteomics, with a surge in methods to detect them in recent years. However, PTMs can introduce complexity into proteomics searches by fragmenting in unexpected ways. Detecting post-translational modifications in mass spectrometry-based proteomics traditionally relies on identifying ions shifted by the masses of the modifications. This presents challenges for many PTMs. Labile PTMs lose part of their modification mass during fragmentation, rendering shifted fragment ions unidentifiable, and isobaric PTMs are indistinguishable by mass, requiring other diagnostic ions for disambiguation. Furthermore, even modifications that have undergone extensive characterization often produce different fragmentation patterns across instruments and conditions. To address these deficiencies and facilitate the next generation of PTM identification, we have developed a method to automatically find diagnostic spectral features for any PTM, allowing subsequent searches to take advantage of additional metrics and increase PTM identification and localization rates. The method has been incorporated into the open-search annotation tool PTM-Shepherd and the FragPipe computational platform.

bioinformatics↗

bioRxiv2021

Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

Rapidly improving methods for glycoproteomics have enabled increasingly large-scale analyses of complex glycopeptide samples, but annotating the resulting mass spectrometry data with high confidence remains a major bottleneck. We recently introduced a fast and sensitive glycoproteomics search method in our MSFragger search engine, which reports glycopeptides as a combination of a peptide sequence and the mass of the attached glycan. In samples with complex glycosylation patterns, converting this mass to a specific glycan composition is not straightforward, however, as many glycans have similar or identical masses. Here, we have developed a new method for determining the glycan composition of N-linked glycopeptides fragmented by collision or hybrid activation that uses multiple sources of information from the spectrum, including observed glycan B- (oxonium) and Y-type ions and mass and precursor monoisotopic selection errors to discriminate between possible glycan candidates. Combined with false discovery rate estimation for the glycan assignment, we show this method is capable of specifically and sensitively identifying glycans in complex glycopeptide analyses and effectively controls the rate of false glycan assignments. The new method has been incorporated into the PTM-Shepherd modification analysis tool to work directly with the MSFragger glyco search in the FragPipe graphical user interface, providing a complete computational pipeline for annotation of N-glycopeptide spectra with FDR control of both peptide and glycan components that is both sensitive and robust against false identifications.

bioinformatics↗

bioRxiv2020

Fast and Comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Glycosylation is a ubiquitous and heterogeneous post-translational modification (PTM) used to accomplish a wide variety of critical cellular tasks. Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale, high-quality glycoproteomics datasets, but interpreting this data remains challenging. In addition to being large, complex, and heterogeneous, glycans undergo fragmentation during vibrational activation, making common PTM search strategies ineffective for their identification. We present a computational tool called MSFragger-Glyco for fast and highly sensitive identification of N- and O-linked glycopeptides using open and glycan mass offset search strategies. Reanalysis of recently published N-glycoproteomics data resulted in annotation of 83% more glycopeptide-spectrum matches (glycoPSMs) than in previous results, which translated to substantial increases in the numbers of glycoproteins and glycosites that could be identified. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same peptides as the original search and resulted in up to a 6-fold increase when expanding searches to include large numbers of possible glycan compositions and other modifications. Expanded searches revealed trends in glycan composition and crosstalk with phosphorylation that remained hidden to the original search. With greatly improved spectral annotation, coupled with the fast speed of fragment ion index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

bioinformatics↗

bioRxiv2020

Fast quantitative analysis of timsTOF PASEF data with MSFragger and IonQuant

Ion mobility brings an additional dimension of separation to liquid chromatography-mass spectrometry, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to time-of-flight mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most commonly used to analyze these data. However, due to the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 minutes on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation prior to MS/MS analysis.Competing Interest StatementThe authors have declared no competing interest.AbbreviationsLC-MSliquid chromatography-mass spectrometryTIMStrapped ion mobility spectrometryTOFtime-of-flightPASEFparallel accumulation-serial fragmentationDDAdata-dependent acquisitionDIAdata-independent acquisitionMS/MStandem mass spectrometryPSMpeptide-spectrum matchLC-IMS-MSliquid chromatography-ion mobility-mass spectrometryXICextracted ion chromatogramCVcoefficient of variationLFQlabel free quantificationFDRFalse Discovery RateCIDcollision-induced dissociationCPUcentral processing unitPTMpost-translational modificationMBRmatch-between-runsView Full Text

bioinformatics↗