Biology subjects

Geiszler, D. J.

Publications and source records attributed to Geiszler, D. J..

3 recordsLinked to original sources

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↗