Sheng-Bo Fan

Bio: Sheng-Bo Fan is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Protein structure & Proteome. The author has an hindex of 9, co-authored 9 publications receiving 970 citations.

Identification of cross-linked peptides from complex samples

Abstract: pLink, software for data analysis of cross-linked proteins coupled with mass spectrometry, estimates false discovery rate and enables analysis of protein complexes without extensive purification. We have developed pLink, software for data analysis of cross-linked proteins coupled with mass-spectrometry analysis. pLink reliably estimates false discovery rate in cross-link identification and is compatible with multiple homo- or hetero-bifunctional cross-linkers. We validated the program with proteins of known structures, and we further tested it on protein complexes, crude immunoprecipitates and whole-cell lysates. We show that it is a robust tool for protein-structure and protein-protein–interaction studies.

A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides

Abstract: We describe pLink 2, a search engine with higher speed and reliability for proteome-scale identification of cross-linked peptides. With a two-stage open search strategy facilitated by fragment indexing, pLink 2 is ~40 times faster than pLink 1 and 3~10 times faster than Kojak. Furthermore, using simulated datasets, synthetic datasets, 15N metabolically labeled datasets, and entrapment databases, four analysis methods were designed to evaluate the credibility of ten state-of-the-art search engines. This systematic evaluation shows that pLink 2 outperforms these methods in precision and sensitivity, especially at proteome scales. Lastly, re-analysis of four published proteome-scale cross-linking datasets with pLink 2 required only a fraction of the time used by pLink 1, with up to 27% more cross-linked residue pairs identified. pLink 2 is therefore an efficient and reliable tool for cross-linking mass spectrometry analysis, and the systematic evaluation methods described here will be useful for future software development. The identification of cross-linked peptides at a proteome scale for interactome analyses represents a complex challenge. Here the authors report an efficient and reliable search engine pLink 2 for proteome-scale cross-linking mass spectrometry analyses, and demonstrate how to systematically evaluate the credibility of search engines.

Mapping native disulfide bonds at a proteome scale

Abstract: We developed a high-throughput mass spectrometry method, pLink-SS (http://pfind.ict.ac.cn/software/pLink/2014/pLink-SS.html), for precise identification of disulfide-linked peptides. Using pLink-SS, we mapped all native disulfide bonds of a monoclonal antibody and ten standard proteins. We performed disulfide proteome analyses and identified 199 disulfide bonds in Escherichia coli and 568 in proteins secreted by human endothelial cells. We discovered many regulatory disulfide bonds involving catalytic or metal-binding cysteine residues.

Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

Abstract: To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

pQuant improves quantitation by keeping out interfering signals and evaluating the accuracy of calculated ratios.

Abstract: In relative protein abundance determination from peptide intensities recorded in full mass scans, a major complication that affects quantitation accuracy is signal interference from coeluting ions of similar m/z values. Here, we present pQuant, a quantitation software tool that solves this problem. pQuant detects interference signals, identifies for each peptide a pair of least interfered isotopic chromatograms: one for the light and one for the heavy isotope-labeled peptide. On the basis of these isotopic pairs, pQuant calculates the relative heavy/light peptide ratios along with their 99.75% confidence intervals (CIs). From the peptides ratios and their CIs, pQuant estimates the protein ratios and associated CIs by kernel density estimation. We tested pQuant, Census and MaxQuant on data sets obtained from mixtures (at varying mixing ratios from 10:1 to 1:10) of light- and heavy-SILAC labeled HeLa cells or 14N- and 15N-labeled Escherichia coli cells. pQuant quantitated more peptides with better accuracy ...

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

Protein Analysis by Shotgun/Bottom-up Proteomics

Abstract: According to Genome Sequencing Project statistics (http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html), as of Feb 16, 2012, complete gene sequences have become available for 2816 viruses, 1117 prokaryotes, and 36 eukaryotes.1–2 The availability of full genome sequences has greatly facilitated biological research in many fields, and has greatly contributed to the growth of proteomics. Proteins are important because they are the direct bio-functional molecules in the living organisms. The term “proteomics” was coined from merging “protein” and “genomics” in the 1990s.3–4 As a post-genomic discipline, proteomics encompasses efforts to identify and quantify all the proteins of a proteome, including expression, cellular localization, interactions, post-translational modifications (PTMs), and turnover as a function of time, space and cell type, thus making the full investigation of a proteome more challenging than sequencing a genome. There are possibly 100,000 protein forms encoded by the approximate 20,235 genes of the human genome,5 and determining the explicit function of each form will be a challenge. The progress of proteomics has been driven by the development of new technologies for peptide/protein separation, mass spectrometry analysis, isotope labeling for quantification, and bioinformatics data analysis. Mass spectrometry has emerged as a core tool for large-scale protein analysis. In the past decade, there has been a rapid advance in the resolution, mass accuracy, sensitivity and scan rate of mass spectrometers used to analyze proteins. In addition, hybrid mass analyzers have been introduced recently (e.g. Linear Ion Trap-Orbitrap series6–7) which have significantly improved proteomic analysis. “Bottom-up” protein analysis refers to the characterization of proteins by analysis of peptides released from the protein through proteolysis. When bottom-up is performed on a mixture of proteins it is called shotgun proteomics,8–10 a name coined by the Yates lab because of its analogy to shotgun genomic sequencing.11 Shotgun proteomics provides an indirect measurement of proteins through peptides derived from proteolytic digestion of intact proteins. In a typical shotgun proteomics experiment, the peptide mixture is fractionated and subjected to LC-MS/MS analysis. Peptide identification is achieved by comparing the tandem mass spectra derived from peptide fragmentation with theoretical tandem mass spectra generated from in silico digestion of a protein database. Protein inference is accomplished by assigning peptide sequences to proteins. Because peptides can be either uniquely assigned to a single protein or shared by more than one protein, the identified proteins may be further scored and grouped based on their peptides. In contrast, another strategy, termed ‘top-down’ proteomics, is used to characterize intact proteins (Figure 1). The top-down approach has some potential advantages for PTM and protein isoform determination and has achieved notable success. Intact proteins have been measured up to 200 kDa,12 and a large scale study has identified more than 1,000 proteins by multi-dimensional separations from complex samples.13 However, the top-down method has significant limitations compared with shotgun proteomics due to difficulties with protein fractionation, protein ionization and fragmentation in the gas phase. By relying on the analysis of peptides, which are more easily fractionated, ionized and fragmented, shotgun proteomics can be more universally adopted for protein analysis. In fact, a hybrid of bottom-up and top-down methodologies and instrumentation has been introduced as middle-down proteomics.14 Essentially, middle-down proteomics analyzes larger peptide fragments than bottom-up proteomics, minimizing peptide redundancy between proteins. Additionally the large peptide fragments yield similar advantages as top-down proteomics, such as gaining further insight into post-translational modifications, without the analytical challenges of analyzing intact proteins. Shotgun proteomics has become a workhorse for the analysis of proteins and their modifications and will be increasingly combined with top-down methods in the future. Figure 1 Proteomic strategies: bottom-up vs. top-down vs. middle-down. The bottom-up approach analyzes proteolytic peptides. The top-down method measures the intact proteins. The middle-down strategy analyzes larger peptides resulted from limited digestion or ... In the past decade shotgun proteomics has been widely used by biologists for many different research experiments, advancing biological discoveries. Some applications include, but are not limited to, proteome profiling, protein quantification, protein modification, and protein-protein interaction. There have been several reviews nicely summarizing mass spectrometry history,15 protein quantification with mass spectrometry,16 its biological applications,5,17–26 and many recent advances in methodology.27–32 In this review, we try to provide a full and updated survey of shotgun proteomics, including the fundamental techniques and applications that laid the foundation along with those developed and greatly improved in the past several years.

MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics

Abstract: There is a need to better understand and handle the 'dark matter' of proteomics-the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry-based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein-RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

Visualization of arrestin recruitment by a G-protein-coupled receptor

Abstract: Single-particle electron microscopy and hydrogen–deuterium exchange mass spectrometry are used to characterize the structure and dynamics of a G-protein-coupled receptor–arrestin complex. Much has been learned about the structure of G-protein-coupled receptors (GCPRs) over the past seven years, but we still don't know what an activated GPCR looks like when it is bound to a β-arrestin. (Arrestins are cellular mediators with a broad range of functions, many of them involving GPCRs.) In this study the authors use single-particle electron microscopy and hydrogen–deuterium exchange mass spectrometry to characterize the structure and dynamics of a GPCR–arrestin complex. Their data support a 'biphasic' mechanism, in which the arrestin initially interacts with the phosphorylated carboxy terminus of the GPCR before re-arranging to more fully engage the membrane protein in a signalling-competent conformation. G-protein-coupled receptors (GPCRs) are critically regulated by β-arrestins, which not only desensitize G-protein signalling but also initiate a G-protein-independent wave of signalling1,2,3,4,5. A recent surge of structural data on a number of GPCRs, including the β2 adrenergic receptor (β2AR)–G-protein complex, has provided novel insights into the structural basis of receptor activation6,7,8,9,10,11. However, complementary information has been lacking on the recruitment of β-arrestins to activated GPCRs, primarily owing to challenges in obtaining stable receptor–β-arrestin complexes for structural studies. Here we devised a strategy for forming and purifying a functional human β2AR–β-arrestin-1 complex that allowed us to visualize its architecture by single-particle negative-stain electron microscopy and to characterize the interactions between β2AR and β-arrestin 1 using hydrogen–deuterium exchange mass spectrometry (HDX-MS) and chemical crosslinking. Electron microscopy two-dimensional averages and three-dimensional reconstructions reveal bimodal binding of β-arrestin 1 to the β2AR, involving two separate sets of interactions, one with the phosphorylated carboxy terminus of the receptor and the other with its seven-transmembrane core. Areas of reduced HDX together with identification of crosslinked residues suggest engagement of the finger loop of β-arrestin 1 with the seven-transmembrane core of the receptor. In contrast, focal areas of raised HDX levels indicate regions of increased dynamics in both the N and C domains of β-arrestin 1 when coupled to the β2AR. A molecular model of the β2AR–β-arrestin signalling complex was made by docking activated β-arrestin 1 and β2AR crystal structures into the electron microscopy map densities with constraints provided by HDX-MS and crosslinking, allowing us to obtain valuable insights into the overall architecture of a receptor–arrestin complex. The dynamic and structural information presented here provides a framework for better understanding the basis of GPCR regulation by arrestins.