Biology subjects

He, S.-M.

Publications and source records attributed to He, S.-M..

6 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↗

bioRxiv2024

Global analysis of protein and small-molecule substrates of ubiquitin-like proteins (UBLs)

AbstractUbiquitin-like proteins (UBLs) constitute a family of evolutionarily conserved proteins that share similarities with ubiquitin in 3D structures and modification mechanisms. For most UBLs including Small-Ubiquitin-like Modifiers (SUMO), their modification sites on substrate proteins cannot be identified using the mass spectrometry-based method that has been successful for identifying ubiquitination sites, unless a UBL protein is mutated accordingly. To identify UBL modification sites without having to mutate UBL, we have developed a dedicated search engine pLink-UBL on the basis of pLink, a software tool for identification of cross-linked peptide pairs. pLink-UBL exhibited superior precision, sensitivity, and speed than "make-do" search engines such as MaxQuant, pFind, and pLink. For example, compared to MaxQuant, pLink-UBL increased the number of identified SUMOylation sites by 50 [~] 300% from the same datasets. Additionally, we present a method for identifying small-molecule modifications of UBLs. This method involves antibody enrichment of a UBL C-terminal peptide following enrichment of a UBL protein, followed by LC-MS/MS analysis and a pFind 3 blind search to identify unexpected modifications. Using this method, we have discovered non-protein substrates of SUMO, of which spermidine is the major one for fission yeast SUMO Pmt3. Spermidine can be conjugated to the C-terminal carboxylate group of Pmt3 through its N1 or also likely, N8 amino group in the presence of SUMO E1, E2, and ATP. Pmt3-spermidine conjugation does not require E3 and can be reversed by SUMO isopeptidase Ulp1. SUMO-spermidine conjugation is present in mice and humans. Also, spermidine can be conjugated to ubiquitin in vitro by E1 and E2 in the presence of ATP. The above observations suggest that spermidine may be a common small molecule substrate of SUMO and possibly ubiquitin across eukaryotic species. HighlightsO_LIA specialized software pLink-UBL enables precise identification of UBL modification sites on protein substrates. C_LIO_LIpFind 3 blind search enables identification of unexpected small-molecule substrates of a UBL protein. C_LIO_LISpermidine is a small molecule substrate of fission yeast SUMO Pmt3 as well as mammalian SUMO proteins. C_LIO_LIThe C-terminal carboxyl group of Pmt3 can be attached to the N1 or likely also the N8 amino group of spermidine in the presence of E1, E2, and ATP, and can be detached by SUMO isopeptidase. C_LI

biochemistry↗

bioRxiv2021

pChem: a modification-centric assessment tool for the performance of chemoproteomic probes

Chemoproteomics has emerged as a key technology to expand the functional space in complex proteomes for probing fundamental biology and for discovering new small molecule-based therapies. Here we report a modification-centric computational tool termed pChem to provide a streamlined pipeline for unbiased performance assessment of chemoproteomic probes. The pipeline starts with an experimental setting for isotopically coding probe-derived modifications (PDMs) that can be automatically recognized by pChem, with masses accurately calculated and sites precisely localized. Further, pChem exports on-demand reports by scoring the profiling efficiency, modification-homogeneity and proteome-wide residue selectivity of a tested probe. The performance and robustness of pChem were benchmarked by applying it to eighteen bioorthogonal probes. Of note, the analyses reveal that the formation of unexpected PDMs can be driven by endogenous reactive metabolites (e.g., bioactive aldehydes and glutathione). Together, pChem is a powerful and user-friendly tool that aims to facilitate the development of probes for the ever-growing field of chemoproteomics.

bioinformatics↗

bioRxiv2021

Precise, Fast and Comprehensive Analysis of Intact Glycopeptides and Monosaccharide-Modifications with pGlyco3

We present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A novel glycan ion-indexing algorithm developed in this work for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing the accuracies and sensitivities. By combining electron-based dissociation spectra, pGlyco3 integrates a fast, dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization (SSGL). Our evaluation based on synthetic and natural glycopeptides showed that the SSGL probabilities estimated by pGlycoSite were proved to be appropriate to localize site-specific glycans. With pGlyco3, we found that N-glycopeptides and O-mannose glycopeptides in yeast samples were extensively modified by ammonia adducts on Hex (aH) and verified the aH-glycopeptide identifications based on released N-glycans and 15N/13C-labeled data. Thus pGlyco3, which is freely available on https://github.com/pFindStudio/pGlyco3/releases, is an accurate and flexible tool to identify glycopeptides and modified saccharide units.

bioinformatics↗

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↗

bioRxiv2018

Open-pFind enables precise, comprehensive and rapid peptide identification in shotgun proteomics

Shotgun proteomics has grown rapidly in recent decades, but a large fraction of tandem mass spectrometry (MS/MS) data in shotgun proteomics are not successfully identified. We have developed a novel database search algorithm, Open-pFind, to efficiently identify peptides even in an ultra-large search space which takes into account unexpected modifications, amino acid mutations, semi- or non-specific digestion and co-eluting peptides. Tested on two metabolically labeled MS/MS datasets, Open-pFind reported 50.5-117.0% more peptide-spectrum matches (PSMs) than the seven other advanced algorithms. More importantly, the Open-pFind results were more credible judged by the verification experiments using stable isotopic labeling. Tested on four additional large-scale datasets, 70-85% of the spectra were confidently identified, and high-quality spectra were nearly completely interpreted by Open-pFind. Further, Open-pFind was over 40 times faster than the other three open search algorithms and 2-3 times faster than three restricted search algorithms. Re-analysis of an entire human proteome dataset consisting of [~]25 million spectra using Open-pFind identified a total of 14,064 proteins encoded by 12,723 genes by requiring at least two uniquely identified peptides. In this search results, Open-pFind also excelled in an independent test for false positives based on the presence or absence of olfactory receptors. Thus, a practical use of the open search strategy has been realized by Open-pFind for the truly global-scale proteomics experiments of today and in the future.

bioinformatics↗