Biology subjects

Tarn, C.

Publications and source records attributed to Tarn, C..

5 recordsLinked to original sources

bioRxiv2025

Improved identification of peptides, cross-link sites, and modification sites by Target-enhanced Accurate Inclusion Mass Screening (TAlMS)

Chemical cross-linking of proteins coupled with mass spectrometry provides structural insights by identifying cross-linked peptide pairs, abbreviated as cross-links. Presently, cross-link identification suffers from ambiguity and poor sensitivity, because they are typically of lower abundance and consequently of lower MS2 quality than linear peptides present in the same sample. Here, we present Target-enhanced Accurate Inclusion Mass Screening (TAIMS), a meticulously optimized targeted mass spectrometry method. TAIMS significantly improved the quality of MS2 as indicated by fragment ion coverage (FIC) and other metrics. From data-dependent acquisition (DDA) to TAIMS, high-FIC cross-links increased by 401 or 678 on yeast ribosome or E. coli lysate, respectively, or from about 40% to around 90%. As a result, TAIMS substantially enhanced the accuracy of cross-link site localization and mitigated sensitivity loss in cross-link identification from large database searches. Enhanced identification sensitivity of TAIMS is further evidenced by its capacity to recover genuine cross-link identifications from data that would typically be discarded. On cross-linked E. coli lysate, 10.5% (284/2711) of the inclusion-list entries generated from unidentified cross-link-spectrum matches gained identity through TAIMS. Of these, 230 were linear peptides and 54 were cross-links, including 10 inter-molecular cross-links missed entirely by DDA. Additionally, we demonstrate that TAIMS is a general method for identification of low-abundance, post-translationally modified peptides. On a mouse brain sample, TAIMS increased the number of phosphopeptides identified with accurate phosphosite assignment by 67%. These findings indicate that TAIMS holds broad applicability in proteomics.

biochemistry↗

bioRxiv2025

Accurate and Fast Protein Acylation Identification by Eliminating Position Effects of Cyclic Immonium Ions with Stepped HCD

Mass spectrometry (MS)-based proteomics is indispensable for studying post-translational modifications (PTMs). Cyclic immonium (CycIm) ions serve as invaluable diagnostic markers for lysine acylations, yet the principles governing their generation efficiency are poorly understood. Here, we systematically investigate this question and uncover a robust "position effect": the generation of immonium ions is strongly favored when the modified residue is located near the N-terminus of a tryptic peptide. Utilizing LysargiNase digestion and isotope-labeled synthetic peptides, we demonstrate that this effect is likely driven by the inherent instability of b-type fragment ions during collision-induced dissociation. Furthermore, we show that a stepped HCD strategy enables enhanced sequence coverage and robust CycIm ion detection ([~]99%), thereby improving the depth, reliability and speed of PTM identification. Collectively, this work provides fundamental understanding of immonium ion formation and establishes an optimized acquisition and analysis strategy that enhances the efficiency and confidence of PTM analysis.

biochemistry↗

bioRxiv2023

PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Accurate and comprehensive peptide precursor ions are crucial to tandem mass spectrometry-based peptide identification. An identification engine can greatly benefit from the search space reduction hinted by credible and detailed precursors. Additionally, both the number of identifications and the spectrum explainability can be increased by considering multiple precursors per spectrum. Here, we propose PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for the following peptide identification. PepPre is evaluated both on regular and cross-linked peptides datasets, and compared with 11 methods in this paper. The experimental results show that PepPre achieves 203% more PSM and 68% more peptide identifications than instrument software for regular peptides, and 99% more PSM and 27% more peptide pair identifications for cross-linked peptides, which also outperforms all other evaluated methods. In addition to the increased identification numbers, further credibility evaluation evidence that the identifications are credible. Moreover, by widening the isolation window of data acquisition from 2 Th to 8 Th, the engine is able to identify at least 64% more PSMs with PepPre, demonstrating the potential advantages of large isolation windows. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC="FIGDIR/small/540645v1_ufig1.gif" ALT="Figure 1"> View larger version (5K): org.highwire.dtl.DTLVardef@1272a3corg.highwire.dtl.DTLVardef@45e25aorg.highwire.dtl.DTLVardef@f83e0org.highwire.dtl.DTLVardef@9b04e7_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Comparative analysis of chemical cross-linking mass spectrometry data indicates that protein STY residues rarely react with N-hydroxysuccinimide ester cross-linkers

Chemical cross-linking of proteins coupled with mass spectrometry (CXMS) has enjoyed growing popularity in biomedical research. Most CXMS experiments utilize cross-linkers based on N-hydroxysuccinimide (NHS) ester, which react selectively with the amine groups found on the free N-termini of proteins and on the side chain of lysine (K) residues. It is also reported that under certain conditions they can react with the hydroxyl groups of serine (S), threonine (T), and tyrosine (Y). Some of the popular cross-link search engines including MeroX and xiSearch set STY, in addition to K, as cross-linkable sites by default. However, to what extent NHS ester cross-linkers react with STY under the typical CXMS experimental conditions remains unclear, nor has the reliability of STY-cross-link identifications. Here, by setting amino acids with chemically inert side chains such as glycine (G), valine (V), and leucine (L) as cross-linkable sites, which serves as a negative control, we show that software-identified STY-cross-links are only as reliable as GVL-cross-links. This is true across different NHS ester cross-linkers including DSS, DSSO, and DSBU, and across different search engines including MeroX, xiSearch, and pLink. Using a published dataset originated from synthetic peptides, we demonstrate that STY-cross-links indeed have a high false discovery rate. Further analysis revealed that depending on the data and the CXMS search engine used to analyze the data, up to 65% of the STY-cross-links identified are actually K-K cross-links of the same peptide pairs, up to 61% are actually K-mono-links, and the rest tend to contain short peptides at high risk of false identification.

biochemistry↗

bioRxiv2020

pDeep3: Towards More Accurate Spectrum Prediction with Fast Few-Shot Learning

Spectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the pre-diction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten datasets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the dataset from a untrained instrument Sciex-6600, within about 10 seconds, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) dataset, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pdeep3.

bioinformatics↗