FragPipe 是一套计算工具的 Java 图形用户界面 (GUI)，能够对基于质谱的蛋白质组学数据进行综合分析。 它由提供- 一种适用于常规和“开放”（宽前体质量耐受性）肽识别的超快蛋白质组学搜索引擎。 FragPipe 包括工具包，用于 MSFragger 搜索结果（PeptideProphet、iProphet、ProteinProphet）的下游后处理、FDR 过滤、基于标签的量化和多实验总结报告生成。 和以帮助解释开放搜索结果。 FragPipe 二进制文件中还包括用于基于 TMT/iTRAQ 同量异位标记量化的 、用于具有运行间匹配 (MBR) 功能的无标签量化的 、SpectraST 和 EasyPQP 谱库构建模块以及 DIA-Umpire SE 模块用于直接分析数据独立采集 (DIA) 数据。 FragPipe 教程 （涵盖所有 FragPipe 模块的通用教程）

I Introduction 1 1 Administrative Optimization of Proteomics Networks for Drug Development 3 André van Hall and Michael Hamacher 1.1 Introduction 3 1.2 Tasks and Aims of Administration 4 1.3 Networking 6 1.4 Evaluation of Biomarkers 7 1.5 A Network for Proteomics in Drug Development 9 1.6 Realization of Administrative Networking: the Brain Proteome Projects 10 1.6.1 National Genome Research Network: the Human Brain Proteome Project 11 1.6.2 Human Proteome Organisation: the Brain Proteome Project 14 1.6.2.1 The Pilot Phase 15 References 17 2 Proteomic Data Standardization, Deposition and Exchange 19 Sandra Orchard, Henning Hermjakob, Manuela Pruess, and Rolf Apweiler 2.1 Introduction 19 2.2 Protein Analysis Tools 21 2.2.1 UniProt 21 2.2.2 InterPro 22 2.2.3 Proteome Analysis 22 2.2.4 International Protein Index (IPI) 23 Proteomics in Drug Research Edited by M. Hamacher, K. Marcus, K. Stühler, A. van Hall, B. Warscheid, H. E. Meyer Copyright (C) 2006 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim ISBN: 3-527-31226-9 Contents VI 2.2.5 Reactome 23 2.3 Data Storage and Retrieval 23 2.4 The Proteome Standards Initiative 24 2.5 General Proteomics Standards (GPS) 24 2.6 Mass Spectrometry 25 2.7 Molecular Interactions 27 2.8 Summary 28 References 28 II Proteomic Technologies 31 3 Difference Gel Electrophoresis (DIGE): the Next Generation of Two-Dimensional Gel Electrophoresis for Clinical Research 33 Barbara Sitek, Burghardt Scheibe, Klaus Jung, Alexander Schramm and Kai Stühler 3.1 Introduction 34 3.2 Difference Gel Electrophoresis: Next Generation of Protein Detection in 2-DE 36 3.2.1 Application of CyDye DIGE Minimal Fluors (Minimal Labeling with CyDye DIGE Minimal Fluors) 38 3.2.1.1 General Procedure 38 3.2.1.2 Example of Use: Identification of Kinetic Proteome Changes upon Ligand Activation of Trk-Receptors 39 3.2.2 Application of Saturation Labeling with CyDye DIGE Saturation Fluors 44 3.2.2.1 General Procedure 44 3.2.2.2 Example of Use: Analysis of 1000 Microdissected Cells from PanIN Grades for the Identification of a New Molecular Tumor Marker Using CyDye DIGE Saturation Fluors 45 3.2.3 Statistical Aspects of Applying DIGE Proteome Analysis 47 3.2.3.1 Calibration and Normalization of Protein Expression Data 48 3.2.3.2 Detection of Differentially Expressed Proteins 50 3.2.3.3 Sample Size Determination 51 3.2.3.4 Further Applications 52 References 52 4 Biological Mass Spectrometry: Basics and Drug Discovery Related Approaches 57 Bettina Warscheid 4.1 Introduction 57 4.2 Ionization Principles 58 4.2.1 Matrix-Assisted Laser Desorption/Ionization (MALDI) 58 4.2.2 Electrospray Ionization 60 4.3 Mass Spectrometric Instrumentation 62 Contents VII 4.4 Protein Identification Strategies 65 4.5 Quantitative Mass Spectrometry for Comparative and Functional Proteomics 67 4.6 Metabolic Labeling Approaches 69 15 N Labeling 70 4.6.1 4.6.2 Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) 71 4.7 Chemical Labeling Approaches 73 4.7.1 Chemical Isotope Labeling at the Protein Level 73 4.7.2 Stable Isotope Labeling at the Peptide Level 75 4.8 Quantitative MS for Deciphering Protein–Protein Interactions 78 4.9 Conclusions 80 References 81 5 Multidimensional Column Liquid Chromatography (LC) in Proteomics – Where Are We Now? 89 Egidijus Machtejevas, Klaus K. Unger and Reinhard Ditz 5.1 Introduction 90 5.2 Why Do We Need MD-LC/MS Methods? 91 5.3 Basic Aspects of Developing a MD-LC/MS Method 92 5.3.1 General 92 5.3.2 Issues to be Considered 93 5.3.3 Sample Clean-up 94 5.3.4 Choice of Phase Systems in MD-LC 94 5.3.5 Operational Aspects 97 5.3.6 State-of-the-Art – Digestion Strategy Included 98 5.3.6.1 Multidimensional LC MS Approaches 98 5.4 Applications of MD-LC Separation in Proteomics – a Brief Survey 100 5.5 Sample Clean-Up: Ways to Overcome the “Bottleneck” in Proteome Analysis 104 5.6 Summary 109 References 110 6 Peptidomics Technologies and Applications in Drug Research 113 Michael Schrader, Petra Budde, Horst Rose, Norbert Lamping, PeterSchulz-Knappe and Hans-Dieter Zucht 6.1 Introduction 114 6.2 Peptides in Drug Research 114 6.2.1 History of Peptide Research 114 6.2.2 Brief Biochemistry of Peptides 116 6.2.3 Peptides as Drugs 117 6.2.4 Peptides as Biomarkers 118 6.2.5 Clinical Peptidomics 118 6.3 Development of Peptidomics Technologies 120 6.3.1 Evolution of Peptide Analytical Methods 120 Contents VIII 6.3.2 Peptidomic Profiling 121 6.3.3 Top-Down Identification of Endogenous Peptides 123 6.4 Applications of Differential Display Peptidomics 124 6.4.1 Peptidomics in Drug Development 124 6.4.2 Peptidomics Applied to in vivo Models 127 6.5 Outlook 129 References 130 7 Protein Biochips in the Proteomic Field 137 Angelika Lücking and Dolores J. Cahill 7.1 Introduction 137 7.2 Technological Aspects 139 7.2.1 Protein Immobilization and Surface Chemistry 139 7.2.2 Transfer and Detection of Proteins 141 7.2.3 Chip Content 142 7.3 Applications of Protein Biochips 144 7.4 Contribution to Pharmaceutical Research and Development 150 References 151 8 Current Developments for the In Vitro Characterization of Protein Interactions 159 Daniela Moll, Bastian Zimmermann, Frank Gesellchen and Friedrich W.Herberg 8.1 Introduction 160 8.2 The Model System: cAMP-Dependent Protein Kinase 161 8.3 Real-time Monitoring of Interactions Using SPR Biosensors 161 8.4 ITC in Drug Design 163 8.5 Fluorescence Polarization, a Tool for High-Throughput Screening 165 8.6 AlphaScreen as a Pharmaceutical Screening Tool 167 8.7 Conclusions 170 References 171 9 Molecular Networks in Morphologically Intact Cells and Tissue–Challenge for Biology and Drug Development 173 Walter Schubert, Manuela Friedenberger and Marcus Bode 9.1 Introduction 173 9.2 A Metaphor of the Cell 174 9.3 Mapping Molecular Networks as Patterns: Theoretical Considerations 176 9.4 Imaging Cycler Robots 177 9.5 Formalization of Network Motifs as Geometric Objects 179 9.6 Gain of Functional Information: Perspectives for Drug Development 182 References 182 Contents IX III Applications 185 10 From Target to Lead Synthesis 187 Stefan Müllner, Holger Stark, Paivi Niskanen, Erich Eigenbrodt, SybilleMazurek and Hugo Fasold 10.1 Introduction 187 10.2 Materials and Methods 190 10.2.1 Cells and Culture Conditions 190 10.2.2 In Vitro Activity Testing 190 10.2.3 Affinity Chromatography 190 10.2.4 Electrophoresis and Protein Identification 191 10.2.5 BIAcore Analysis 191 10.2.6 Synthesis of Acyl Cyanides 192 10.2.6.1 Methyl 5-cyano-5-oxopentanoate 192 10.2.6.2 Methyl 6-cyano-6-oxohexanoate 193 10.2.6.3 Methyl-5-cyano-3-methyl-5-oxopentanoate 193 10.3 Results 193 10.4 Discussion 201 References 203 11 Differential Phosphoproteome Analysis in Medical Research 209 Elke Butt and Katrin Marcus 11.1 Introduction 210 11.2 Phosphoproteomics of Human Platelets 211 11.2.1 Cortactin 213 11.2.2 Myosin Regulatory Light Chain 213 11.2.3 Protein Disulfide Isomerase 214 11.3 Identification of cAMP- and cGMP-Dependent Protein Kinase Substrates in Human Platelets 216 11.4 Identification of a New Therapeutic Target for Anti-Inflammatory Therapy byAnalyzing Differences in the Phosphoproteome of Wild Type and Knock Out Mice 218 11.5 Concluding Remarks and Outlook 219 References 220 12 Biomarker Discovery in Renal Cell Carcinoma Applying Proteome-Based Studies in Combination with Serology 223 Barbara Seliger and Roland Kellner 12.1 Introduction 224 12.1.1 Renal Cell Carcinoma 224 12.2 Rational Approaches Used for Biomarker Discovery 225 12.3 Advantages of Different Proteome-Based Technologies for the Identification ofBiomarkers 226 Contents X 12.4 Type of Biomarker 228 12.5 Proteome Analysis of Renal Cell Carcinoma Cell Lines and Biopsies 229 12.6 Validation of Differentially Expressed Proteins 234 12.7 Conclusions 235 References 235 13 Studies of Drug Resistance Using Organelle Proteomics 241 Catherine Fenselau and Zongming Fu 13.1 Introduction 242 13.1.1 The Clinical Problem and the Proteomics Response 242 13.2 Objectives and Experimental Design 243 13.2.1 The Cell Lines 243 13.2.2 Organelle Isolation 244 13.2.2.1 Criteria for Isolation 244 13.2.2.2 Plasma Membrane Isolation 245 13.2.3 Protein Fractionation and Identification 247 13.2.4 Quantitative Comparisons of Protein Abundances 249 13.3 Changes in Plasma Membrane and Nuclear Proteins in MCF-7 Cells Resistant toMitoxantrone 252 References 254 14 Clinical Neuroproteomics of Human Body Fluids: CSF and Blood Assays forEarly and Differential Diagnosis of Dementia 259 Jens Wiltfang and Piotr Lewczuk 14.1 Introduction 259 14.2 Neurochemical Markers of Alzheimer’s Disease 260 14.2.1 β-Amyloid Precursor Protein (β-APP): Metabolismand ImpactonADDiagnosis 261 14.2.2 Tau Protein and its Phosphorylated Forms 263 14.2.2.1 Hyperphosphorylation of Tau as a Pathological Event 264 14.2.2.2 Phosphorylated Tau in CSF as a Biomarker of Alzheimer’s Disease 265 14.2.3 Apolipoprotein E (ApoE) Genotype 266 14.2.4 Other Possible Factors 267 14.2.5 Combined Analysis of CSF Parameters 267 14.2.6 Perspectives: Novel Techniques to Search for AD Biomarkers – Mass Spectrometry (MS), Differential Gel Electrophoresis (DIGE), and Multiplexing 270 14.3 Conclusions 271 References 272 15 Proteomics in Alzheimer’s Disease 279 Michael Fountoulakis, Sophia Kossida and Gert Lubec 15.1 Introduction 279 Contents XI 15.2 Proteomic Analysis 280 15.2.1 Sample Preparation 280 15.2.2 Two-Dimensional Electrophoresis 282 15.2.3 Protein Quantification 282 15.2.4 Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectroscopy 283 15.3 Proteins with Deranged Levels and Modifications in AD 284 15.3.1 Synaptosomal Proteins 290 15.3.2 Guidance Proteins 291 15.3.3 Signal Transduction Proteins 291 15.3.4 Oxidized Proteins 292 15.3.5 Heat Shock Proteins 293 15.3.6 Proteins Enriched in Amyloid Plaques 293 15.4 Limitations 294 References 294 16 Cardiac Proteomics 299 Emma McGregor and Michael J. Dunn 16.1 Heart Proteomics 300 16.1.1 Heart 2-D Protein Databases 300 16.1.2 Dilated Cardiomyopathy 300 16.1.3 Animal Models of Heart Disease 301 16.1.4 Subproteomics of the Heart 302 16.1.4.1 Mitochondria 302 16.1.4.2 PKC Signal Transduction Pathways 304 16.1.5 Proteomics of Cultured Cardiac Myocytes 305 16.1.6 Proteomic Characterization of Cardiac Antigens in Heart Disease and Transplantation 306 16.1.7 Markers of Acute Allograft Rejection 307 16.2 Vessel Proteomics 307 16.2.1 Proteomics of Intact Vessels 307 16.2.2 Proteomics of Isolated Vessel Cells 308 16.2.3 Laser Capture Microdissection 311 16.3 Concluding Remarks 312 References 312 IV To the Market 319 17 Innovation Processes 321 Sven Rüger 17.1 Introduction 321 17.2 Innovation Process Criteria 322 17.3 The Concept 322 17.4 Market Attractiveness 323 Contents XII 17.5 Competitive Market Position 323 17.6 Competitive Technology Position 324 17.7 Strengthen the Fit 325 17.8 Reward 325 17.9 Risk 325 17.10 Innovation Process Deliverables for each Stage 326 17.11 Stage Gate-Like Process 326 17.11.1 Designation as an Evaluation Project (EvP) 327 17.11.2 Advancement to Exploratory Project (EP) 329 17.11.3 For Advancement to Progressed Project (PP) 331 17.11.4 Advancement to Market Preparation 334 17.12 Conclusion 335 Subject Index 337

GAT_蛋白质组学 GAT进行蛋白质组学网络分类 工作流程 先决条件 用户需要安装python（ ）和一些python软件包： [火炬] [dgl] [numpy] [熊猫] [networkx] [matplotlib] 数据准备和模型训练 将网络/一组网络的边缘文件，节点特征文件和标签文件添加到文件夹“数据”中。 对于图分类，需要一组图。 运行python脚本“ graph_classification.py”来训练和验证GAT模型。 训练有素的模型将存储在“模型”文件夹中。 python graph_classification.py根edge.file node.feature.file graph.label 运行python脚本“ graph_evaluation.py.py”以使用经过训练的模型对其他数据集进行预测。 python graph_evaluat

MSFragger MSFragger是用于基于质谱的蛋白质组学中肽段鉴定的超快速数据库搜索工具。 它在各种数据集和应用程序中均表现出出色的性能。 MSFragger适用于标准shot弹枪蛋白质组学分析以及大型数据集（包括timsTOF PASEF数据），酶无限制搜索（例如肽组），“开放式”数据库搜索（即前体质量耐受性设置为数百道尔顿），用于鉴定修饰的肽， MSFragger Glyco模式鉴定糖肽（N链和O链）。 MSFragger用跨平台的Java编程语言实现，可以使用三种不同的方式： 使用 GUI（图形用户界面）。 通过 作为独立的Java可执行文件（JAR） MSFragger以表格或pepXML格式编写输出，使其与下游数据分析管道（如跨蛋白质组学管道和完全兼容。 请参阅，包括的列表。 示例参数文件可在找到。 支持的乐器和文件格式 mzML / mzXML ：可以使用来

分子生物学方法--蛋白组学 springer protocol

使用QFeatures进行蛋白质组学数据分析 这个为期1天的课程将讨论使用Bioconductor QFeatures从蛋白质组学实验中获得的数据的计算分析。 贡献 我们欢迎您为改进本课程而做出的所有贡献！ 如果您在此过程中有任何疑问，疑虑或遇到任何困难，维护人员将竭尽所能为您提供帮助。 我们想请您熟悉我们的《 ，并查看有关正确格式，在本地呈现课程的方式，甚至如何编写新剧集的。 请参阅当前列表，以获取有关对此存储库做出贡献的想法。 为了做出您的贡献，我们使用GitHub流程，该流程在Scott Chacon为Pro Git中章中很好的解释。 寻找标签 。 这表示维护人员将欢迎提出修复此问题的请求。 维护者 本课程的当前维护者是 玛丽亚·德米特（Maria Dermit） 作者 可以在“找到该课程的参与者列表 引文 要引用本课程，请向咨询