特邀专家

截止2020年5月1日,CNCP-2020 组委会已经邀请到来自海内外的26位学者做大会报告(按姓名拼音为序):


陈鹏

北京大学

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Bioorthogonal Chemistry-enabled Spatial-Temporal Proteomics

Abstract: Employing small molecules or other chemical means to modulate the function of an intracellular protein of interest, particularly in a gain-of-function fashion, remains highly desired but challenging. In this talk, I will introduce a “genetically encoded chemical decaging” strategy that relies on our recently developed bioorthogonal cleavage reactions to control protein activation in living systems. These reactions exhibit high efficiency and low toxicity for decaging the chemically “masked” lysine or tyrosine residues on intracellular proteins, allowing the spatial and temporal resolved proteomics study in living systems. Most recently, with the assistance of computer-based design and screening, we further expanded our method from “precise decaging” of enzyme active-sites to “proximal decaging” of enzyme pockets. This new method, termed Computationally Aided and Genetically Encoded Proximal Decaging” (CAGE-prox) (CAGE-prox), showed general applicability for switching on the activity of a broad range of proteins under living conditions. I will end by showcasing exciting applications of our CAGE-prox technique on: i) constructing orthogonal and mutually exclusive kinase signaling cascades; ii) temporal caspase activation for time-resolved profiling of proteolytic events upon apoptosis; and iii) on-demand activation of bacterial effectors as potential protein prodrugs for cancer therapy.

Key words: Proteomics, Bioorthogonal reaction, Spatial and temporal control, Living systems

陈顺兴

南方科技大学

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陈兴

北京大学

丁 明

中国药科大学

黄 河

中国科学院

上海药物研究所

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姜 颖

国家蛋白质科学中心(北京)

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李雪明

清华大学

李衍常

国家蛋白质科学中心(北京)

刘 超

北京航空航天大学

刘泽先

中山大学

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Genetic variation driven PTM aberrances: from molecular mechanism to targeted cancer therapies

Abstract: Post-translational modifications (PTMs) were critical for regulating cellular processes, and their aberrances were heavily implicated in cancer. Massive PTM sites have been identified through experimental identification and high-throughput proteomics techniques, however, their enzyme-specific regulation remains largely unknown. Recently, we developed the Deep-PLA software for HAT/HDAC-specific acetylation prediction based on deep learning, and employed the protein–protein interaction and co-sublocalization to reduce filter the false positive predictions. Through large-scale prediction based on TCGA cancer omics data, it was observed that mutations more frequently occurred at the region around acetylation sites, and acetylation-related mutations (ARMs) had higher variant allele fraction values than non-ARMs, which meant these mutations might be more functional in cancer. Furthermore, ARM proteins were significantly enriched in cancer genes and druggable proteins, and clinical survival analysis demonstrated that the patients with at least one ARM had significantly worse clinical prognosis in cancers such as head-neck squamous cell carcinoma. Besides the substrates and sites, we also studied the enzymes of PTMs, for example, HER2, which is the targeted kinase by trastuzumab in HER2 positive gastric cancer. Through monitoring patients by ctDNA, it was observed that the mutations of PIK3CA/R1/C3 or ERBB2/4 could indicated the trastuzumab resistance. Additionally, mutations in NF1 contributed to trastuzumab resistance, which was further confirmed through in vitro and in vivo studies, while combined HER2 and MEK/ERK blockade overcame trastuzumab resistance. Taken together, the PTM systems including the substrates, sites and enzymes, were critical in cancer, and further studies should be contributed to this area.

Key words: acetylation, phosphorylation, mutation, cancer

乔 亮

复旦大学

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Prediction of peptide spectral libraries by deep learning and its use in DIA proteomics

Abstract: Liquid chromatography (LC)-tandem mass spectrometry (MS/MS) has been the most widely used tool for proteomics studies. Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which limits the identification/quantification by DIA to the peptides identified by DDA. It is of great value to generate in silico spectral libraries with quality comparable to that of experimental libraries for DIA analysis. We constructed deep neural networks for accurate peptide MS/MS spectra and retention time prediction 1. The models take a peptide sequence as an input, and outputs relative intensities of b/y product ions at each possible fragmentation site, as well as normalized retention time (iRT) of the peptide. We trained and validated the model with three LC-MS/MS data sets of two organisms acquired in two laboratories. The median dot products between the predicted and experimental b/y peak intensities and the Pearson correlation coefficients of predicted and experimental iRT are comparable to experimental repeats within each data set. The change of lab gave higher impact on the accuracy of prediction than the change of organism, and good cross-sample prediction is feasible when keeping the instrument same. We built in silico spectral libraries by MS/MS and iRT prediction for peptide-centric DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models is comparable to that of experimental libraries, and outperforms libraries generated by other deep learning-based tools using global models 2. Deep learning was also used to predict detectability by mass spectrometry of peptides. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that our strategy can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library.

Key words: deep learning, in-silico spectral library, data-independent acquisition, proteomics

秦伟捷

国家蛋白质科学中心(北京)

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申华莉

复旦大学

水雯箐

上海科技大学

iHuman 研究所

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孙世伟

中国科学院

计算技术研究所

Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection

Abstract: Glycans play important roles in a variety of biological processes. Their activities are closely related to the fine details of their structures. Unlike the simple linear chains of proteins, branching is a unique feature of glycan structures, making their identification extremely challenging. Multistage mass spectrometry (MSn) has become the primary method for glycan structural identification. The major difficulty for MSn is the selection of fragment ions as precursors for the next stage of scanning. Widely-used strategies are either manual selection by experienced experts, which requires considerable expertise and time, or simply selecting the most intense peaks by which the product-ion spectrum generated may not be structurally informative and therefore fail to make the assignment. We here report an ‘intelligent precursor selection’ strategy (GIPS) to guide MSn experiments. Our approach consists of two key elements, an empirical model to calculate candidate glycan’s ‘probability’ and a statistical model to calculate fragment ion’s ‘distinguishing power’ in order to select the structurally-most informative peak as the precursor for next-stage scanning. Using 13 glycan standards, including 3 pairs with isomeric sequences, and 8 variously fucosylated oligosaccharides on linear or branched hexasaccharide backbones obtained from a human milk oligosaccharide fraction by HPLC, we demonstrate its successful application to branching pattern analysis with improved efficiency and sensitivity, and also the potential for automated operation.

谭敏佳

中国科学院

上海药物研究所

田瑞军

南方科技大学

瑕 瑜

清华大学

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PTM-Invariant Peptide Identification

Abstract: Mass spectrometry (MS) has become a primary tool in lipidomics for global lipid identification and quantitation. Despite the fact that multi-level structural information is available from MS and tandem mass spectrometry (MS/MS), localization of carbon-carbon double bond (C=C) is difficult from current analysis workflows. Our group is interested in harnessing radical chemistry for enhanced lipid analysis. We have paired the Paternò–Büchi (PB) reaction with tandem mass spectrometry (PB-MS/MS) for pinpointing C=Cs in unsaturated lipids. Acetone and aryl ketones are used as the PB reagents. Upon 254 nm ultra-violet irradiation, the PB reagent adds onto a C=C via [2+2] cycloaddition, forming the PB products. Collision-induced dissociation (CID) of the PB products produces C=C diagnostic fragment ions, allowing localization of C=C as well as isomer quantitation. The PB-MS/MS approach has been applied for shotgun lipid analysis and more recently hyphenated with liquid chromatography (LC)-MS. The LC-PB-MS platform enabled large-scale identification of unsaturated glycerophospholipids. It was found that the ratios of C=C isomers were much less affected by interpersonal variations than their individual abundances, allowing more sensitive discovery of lipid markers. 

Key words: lipidomics; unsaturated lipids; isomers; Paternò–Büchi reaction; tandem mass spectrometry

谢 鹭

上海生物信息技术研究中心

严 威

上海交通大学系统生物医学研究院

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杨明坤

中国科学院

水生生物研究所

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张 莹

复旦大学

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MS-based Approaches for Analysis of Glycosylation and Application

Abstract: Glycosylation is a complex form of protein modification occurring on eukaryotic proteins. It affects both the structure and function of proteins. To sensitively analyze the protein N-glycosylation by MS, we have developed a series of new approaches.To selective enrich the glycopeptides, we explored several different chemical reactions that can specifically occur between glycoproteins and solid phases including reductive amination and oxime click reaction. These methods greatly reduced the enrichment time and improve the selectivity of N-glycoprotein analysis. By using the oxime click reaction, we further designed a cross linker that can label the glycan and glycoproteins on bacterial surface in vivo and then can cross link the bacteria with its host interactors by UV irradiation, thereby enabled a time-resolved chemical proteomics strategy enabling host and pathogen temporal interaction profiling (HAPTIP) for tracking the entry of a pathogen into the host cell. Moreover, to enable the accurate quantification of the N-glycome, we developed several new novel N-glycan quantitation approaches based on isotope labeling combined with mass spectrometric analysis including metallic element chelated tag labeling (MeCTL) to increase the sample throughput and duplex stable isotope labeling (DuSIL) to quantify the sialic glycan and neutral glycans simultaneously. Recently, in response to the technical challenge in site-specific N-glycosylation analysis, we reported a chemical labeling strategy to improve the electron transfer dissociation efficiency of intact glycopeptides. This comprehensive glycosylation analysis strategy for the first time allows the discrimination of IgG3 and IgG4 intact N-glycopeptides with high similarity in sequence without the antibody-based pre-separation. In summary, these novel strategies helped the highly sensitive and specific MS analysis of the protein glycosylation.

Key words: Glycosylation, Chemical proteomics, Posttranslational modification, Mass spectrometry

赵 倩

香港理工大学

应用生物及化学科技学系

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赵 群

中国科学院

大连化学物理研究所

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Novel Methods for Chemical Crosslinking Based Protein Complex Analysis

Abstract: Chemical cross-linking combined with mass spectrometry (CXMS) has emerged as a powerful tool to assist traditional technologies to study protein structure and protein–protein interaction with advantages of providing direct interaction sites, less time-consuming and less demanding on sample purity. However, application of CXMS is still limited by the high complexity of CXMS samples, the low abundance of cross-linked peptides, and so on. Besides, how to realize the in-situ protein complex analysis with the lowest cell interference, and further analyze the dynamic conformation and interaction changes at both temporal and spatial dimensions, is an important issue for precisely characterizing the protein complexes, further to elucidate their functions. In response to the above problems, our team has developed a series of methods to improve the depth of chemical crosslinks and realize the in-situ dynamic analysis of protein complexes at temporal and spatial dimensions. And the obtained results suggested our developed strategy might be a promising tool for the global analysis of protein complexes assembly.

Key words: hemical cross-linking coupled with mass spectrometry (CXMS), protein complex, in-situ, dynamic analysis, temporal and spatial dimension

郑 杰

中国科学院

上海药物研究所

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Hydrogen/Deuterium exchange Mass spectrometry – Hijacking molecular plasticity to fine tune receptor signalling

Abstract: While proteins are often depicted as static models, they exhibit a degree of structural plasticity in solution. Characterizing this structural plasticity is essential for understanding the relationship between structure and function. In this talk, I will present the latest advances of solution-phase hydrogen-deuterium exchange Mass spectrometry (HDX-MS) on receptor signaling events derived from key diseases mechanisms related to nuclear receptor signaling and innate immunity receptor associated autoimmune diseases. Our laboratory adopts a synergistic strategy for structure-function studies by coupling the solution-phase solvent exchange measurements from HDX with static structures to inform the dynamic properties of macromolecules.

周 虎

中国科学院

上海药物研究所