This document describes a new method for absolute quantification of proteins using LC-MS/MS. The method is based on the discovery that the average MS signal response for the three most intense tryptic peptides per mole of protein is constant, regardless of protein. Given an internal standard, this relationship can be used to calculate a universal signal response factor to determine absolute protein concentrations without using protein-specific standards. The method was shown to accurately quantify both exogenous proteins in simple mixtures and endogenous serum proteins within 15% error. It also determined stoichiometries of known protein complexes in E. coli lysates.
A miniaturized sandwich immunoassay platformQing Chen
This document describes a new miniaturized sandwich immunoassay platform (MSIP) for detecting protein-protein interactions (PPIs) in a high-throughput manner. The MSIP combines antibody microarray technology with co-immunoprecipitation methods to allow simple, rapid, and large-scale PPI detection using small amounts of cell lysate. Evaluation of the MSIP showed it could accurately identify both known interacting and non-interacting protein pairs. Compared to traditional resin-based co-immunoprecipitation, the MSIP has higher sensitivity and throughput while being simpler and more cost-effective. The MSIP is presented as an effective method for validating PPIs identified by other techniques like yeast two-hybrid screening
2013-09-03 Radboudumc NCMLS Technical ForumAlain van Gool
The document provides information about the Radboud Proteomics Center. It summarizes that the center was established in 2003 to initiate, coordinate and facilitate proteomics research activities. It offers technological tools and knowledge transfer for proteomics research. The center has played a crucial role in numerous research projects using various proteomics approaches like bottom-up proteomics, targeted proteomics, and top-down proteomics. It provides expertise, resources, and services to both internal and external academic and industrial researchers.
Proteomics is the large-scale study of proteins and how they function. [1] It uses techniques like mass spectrometry and protein chips to study post-translational modifications and interactions that cannot be predicted from genomic data alone. [2] Proteomics provides insights into biological processes by identifying proteins, analyzing modifications, detecting interactions, and comparing expression levels between cell states. [3] Studying proteomics is necessary to understand how genes are functionally expressed at the protein level.
This document discusses protein extraction and fractionation techniques used in food proteomics. It begins by outlining various methods for disrupting plant cell walls including mechanical, ultrasonic, pressure and temperature-based techniques. It then describes approaches for solubilizing and precipitating proteins from foods using organic solvents and aqueous solutions. Key steps in a typical proteomics workflow are outlined including protein extraction, separation, identification and data analysis. The challenges of analyzing complex food proteomes due to heterogeneity and abundance differences are also noted. Finally, an integrated view of various extraction and fractionation methods employed in food proteomics is presented.
This document discusses aptamers and their applications. It notes that aptamers are single-stranded DNA or RNA molecules that can bind targets like proteins with high affinity and specificity. They are generated through a process called SELEX that selects aptamers that bind to a target. However, conventional SELEX only selects aptamers for one target at a time, limiting throughput. IceNine has addressed this by parallelizing the SELEX process to select aptamers for multiple targets simultaneously. The document also discusses additional applications of aptamers and their advantages over antibodies.
For more information, you can visit https://www.creative-proteomics.com/services/protein-post-translational-modification-analysis.htm. In this video, we introduce some commonly used methods to detect PPIs and techniques for proteome-scale interactome maps.
The document discusses proteomics, which is the study of the entire complement of proteins in a cell or organism. It defines key proteomics terms like proteome and describes techniques used in proteomics like protein separation, 2D gel electrophoresis, mass spectrometry, and protein digestion. The goals of proteomics include detecting and comparing protein expression profiles to understand biological processes and discover drug targets. Proteomics provides important insights not available through genomics alone.
A miniaturized sandwich immunoassay platformQing Chen
This document describes a new miniaturized sandwich immunoassay platform (MSIP) for detecting protein-protein interactions (PPIs) in a high-throughput manner. The MSIP combines antibody microarray technology with co-immunoprecipitation methods to allow simple, rapid, and large-scale PPI detection using small amounts of cell lysate. Evaluation of the MSIP showed it could accurately identify both known interacting and non-interacting protein pairs. Compared to traditional resin-based co-immunoprecipitation, the MSIP has higher sensitivity and throughput while being simpler and more cost-effective. The MSIP is presented as an effective method for validating PPIs identified by other techniques like yeast two-hybrid screening
2013-09-03 Radboudumc NCMLS Technical ForumAlain van Gool
The document provides information about the Radboud Proteomics Center. It summarizes that the center was established in 2003 to initiate, coordinate and facilitate proteomics research activities. It offers technological tools and knowledge transfer for proteomics research. The center has played a crucial role in numerous research projects using various proteomics approaches like bottom-up proteomics, targeted proteomics, and top-down proteomics. It provides expertise, resources, and services to both internal and external academic and industrial researchers.
Proteomics is the large-scale study of proteins and how they function. [1] It uses techniques like mass spectrometry and protein chips to study post-translational modifications and interactions that cannot be predicted from genomic data alone. [2] Proteomics provides insights into biological processes by identifying proteins, analyzing modifications, detecting interactions, and comparing expression levels between cell states. [3] Studying proteomics is necessary to understand how genes are functionally expressed at the protein level.
This document discusses protein extraction and fractionation techniques used in food proteomics. It begins by outlining various methods for disrupting plant cell walls including mechanical, ultrasonic, pressure and temperature-based techniques. It then describes approaches for solubilizing and precipitating proteins from foods using organic solvents and aqueous solutions. Key steps in a typical proteomics workflow are outlined including protein extraction, separation, identification and data analysis. The challenges of analyzing complex food proteomes due to heterogeneity and abundance differences are also noted. Finally, an integrated view of various extraction and fractionation methods employed in food proteomics is presented.
This document discusses aptamers and their applications. It notes that aptamers are single-stranded DNA or RNA molecules that can bind targets like proteins with high affinity and specificity. They are generated through a process called SELEX that selects aptamers that bind to a target. However, conventional SELEX only selects aptamers for one target at a time, limiting throughput. IceNine has addressed this by parallelizing the SELEX process to select aptamers for multiple targets simultaneously. The document also discusses additional applications of aptamers and their advantages over antibodies.
For more information, you can visit https://www.creative-proteomics.com/services/protein-post-translational-modification-analysis.htm. In this video, we introduce some commonly used methods to detect PPIs and techniques for proteome-scale interactome maps.
The document discusses proteomics, which is the study of the entire complement of proteins in a cell or organism. It defines key proteomics terms like proteome and describes techniques used in proteomics like protein separation, 2D gel electrophoresis, mass spectrometry, and protein digestion. The goals of proteomics include detecting and comparing protein expression profiles to understand biological processes and discover drug targets. Proteomics provides important insights not available through genomics alone.
proteomics, mass spectrometry, science, bioinformatics, electrophoresis, liqu...Amit Yadav
This document discusses quantitative mass spectrometry techniques for protein quantitation. It describes both labeling approaches, such as stable isotope labeling with amino acids in cell culture (SILAC) and isobaric tags for relative and absolute quantitation (iTRAQ), as well as label-free approaches including intensity-based measurements, spectral counting, and normalized spectral abundance factor. The labeling approaches involve metabolic, enzymatic, or chemical labeling of proteins prior to mass spectrometry analysis, while label-free methods quantify proteins based on spectral properties without labeling.
The document discusses aptamers, which are oligonucleotides or peptides that bind to targets with high affinity and specificity due to their unique three-dimensional structures. It describes how aptamers are produced through an in vitro selection process called SELEX and classified based on their structure and selection technique. The document also compares aptamers to antibodies and outlines several applications of aptamers in therapeutics, drug delivery, bioimaging, diagnostics, and more.
The document discusses proteomics, which is the study of the proteome or total protein complement of a biological system. Proteomics aims to understand protein expression, functions, interactions, and modifications through various analytical techniques and faces many challenges due to the complexity of proteins. Key approaches in proteomics include expression profiling to compare protein levels between healthy and disease states, structural analysis to determine protein structures, and network mapping to study protein interactions. Mass spectrometry and bioinformatics tools play important roles in proteomic studies, which have applications in characterizing protein complexes and identifying disease biomarkers.
Application of proteomics for identification of abiotic stress tolerance in c...Vivek Zinzala
It is the study of “Proteome”.
The word "proteome" is a blend of "protein" and "genome”.
Large scale study of Proteins.
Particularly their structures and functions.
Study of full set of proteins in a cell type or tissue, and changes during various conditions
Proteins – Basics you need to know for ProteomicsLionel Wolberger
The document provides an overview of key concepts in proteomics, including:
1) It discusses protein structure and function, the 20 common amino acids, and post-translational modifications that proteins undergo.
2) It introduces common techniques used in proteomics like chromatography, electrophoresis, mass spectrometry, and bioinformatics.
3) It summarizes protein analysis methods like gel electrophoresis, isoelectric focusing, and immunological assays used to detect and purify proteins of interest.
Peptide Mass Fingerprinting (PMF) and Isotope Coded Affinity Tags (ICAT)Suresh Antre
Analytical technique for identifying unknown protein. The peptide mass are compared to database containing the theoretical peptide masses of all known protein sequences.
This document summarizes a study that used label-free LC-MS to analyze changes in the proteome of Escherichia coli (E. coli) when grown in different carbon sources (glucose, lactose, acetate). E. coli is a commonly used model organism and understanding its response to environmental changes provides insights into microbial physiology. The LC-MS approach simultaneously identified and quantified proteins across conditions without isotopic labeling. Relative protein abundances ranged from 0.1- to 90-fold changes between conditions. The results correlated well with known E. coli biochemistry and previous transcriptional profiling studies. This label-free LC-MS method provides an effective way to characterize microbial proteomes.
A brief introfuction of label-free protein quantification methodsCreative Proteomics
If you want to know more about our services, please visit https://www.creative-proteomics.com/services/label-free-quantification.htm.
Label-free protein quantification is a mass spectrometry-based method for identifying and quantifying relative changes in two or more biological samples instead of using a stable isotope-containing compound to label proteins.
Overview Radboudumc Center for Proteomics, Glycomics and Metabolomics april 2015Alain van Gool
An overview of the proteomics, glycomics and metabolomics expertise and capabilities within the Translational Metabolic Laboratory of the Radboudumc. We're interested in collaboration with academic and industrial partners, either bilateral or as part of multi-partner consortia.
This document discusses microbial proteomics and various proteomics techniques. It describes structural proteomics which analyzes protein structures to identify gene functions and interaction sites. Interaction proteomics analyzes protein-protein interactions to determine functions. Expression proteomics identifies proteins differentially expressed between related samples like healthy and diseased tissue. Microbial proteomics studies microorganism proteins using techniques like mass spectrometry, electrospray ionization, and matrix-assisted laser desorption-ionization. Gel-based separation techniques including 1D, 2D, and 3D gel electrophoresis are also discussed.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
This document summarizes proteomics and metabolomics techniques for mapping biochemical regulations. It discusses how proteomics uses techniques like gel electrophoresis, liquid chromatography, and mass spectrometry to separate and identify proteins on a large scale. Metabolomics similarly aims to analyze all metabolites in a biological system using techniques like fingerprinting, profiling, and integrating with other omics data. Together, proteomics and metabolomics provide multiple levels of insight into cellular processes by examining changes in gene expression, protein abundance, and metabolic activity.
This document provides an overview of proteomics. It discusses the goals of proteomics including global protein analysis, expression, function, and biomarker discovery. It covers different types of proteomics like expression, structural, and functional proteomics. Methods for protein measurement like mass spectrometry and 2D gel electrophoresis are described. The document discusses clinical applications of proteomics in areas like cancer, infectious diseases, CNS disorders and cardiovascular disease. It also touches on challenges like target discovery and costs, as well as future perspectives and conclusions on the potential of proteomics.
Proteins interact with other proteins to carry out most biological processes in cells. Understanding these protein-protein interactions (PPIs) is important for determining how proteins function. There are several methods for detecting and analyzing PPIs, including affinity chromatography, immunoprecipitation, cross-linking, phage display, and genetic techniques like using suppressor mutations or observing synthetic lethal effects. Each method has advantages and disadvantages for elucidating different types of PPIs that are integral to cellular processes.
This document provides an overview of proteomics, including its definition, types, and analysis steps. It discusses several techniques used in proteomics, such as chromatography, ELISA, Western blotting, protein microarrays, Edman sequencing, SDS-PAGE, 2D gel electrophoresis, 2D-DIGE, SILAC, iTRAQ, X-ray crystallography, and mass spectrometry. The techniques allow for studying protein expression, structure, functions, interactions, and modifications to better understand cellular processes.
Proteomics is the study of the complete set of proteins expressed in an organism under particular conditions. It aims to understand protein expression in response to changing conditions like disease. Tools in proteomics include cell lysis, fractionation, protein concentration and quantification, digestion, and peptide cleanup prior to mass spectrometry analysis. Key techniques discussed are molecular techniques like SAGE, separation techniques like gel electrophoresis and chromatography, and protein identification techniques like mass spectrometry.
The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.
Aptamers provide opportunities for structure-based drug design strategies relevant to therapeutic intervention. Recent advances in the chemical modifications of nucleic acids suggest that one of the major barriers to use, stability, can be overcome. The high affinity and specificity of aptamers rival antibodies and make them a promising tool in diagnostic and therapeutic application. We should expect more aptamers to be isolated in the near future against an ever increasing repertoire of targets, using these different SELEX approaches with increased speed and efficiency. Aptamers are poised to successfully compete with monoclonal Abs in therapeutics and drug development within the next few decades.
Aptamers are single-stranded DNA or RNA molecules that can bind to specific molecular targets with high affinity and specificity. They are identified through an in vitro selection process called Systematic Evolution of Ligands by EXponential enrichment (SELEX). Aptamers have various applications as diagnostic and therapeutic agents. They can serve as biosensors for pathogens by utilizing techniques like electrochemical, fluorescence, and colorimetric detection. Aptamers have been developed against viruses like influenza, HIV, hepatitis C, and others. They show potential for blocking viral fusion and inhibiting viral replication through binding to proteins and enzymes.
This document describes a study comparing data acquired from data-independent LC-MS to data acquired from data-dependent LC-MS/MS. The study analyzed mixtures of four proteins alone and with a complex E. coli protein digest. Each sample was run in triplicate by both acquisition methods. The data-independent LC-MS provided more comprehensive detection of precursor and product ions than the combined data-dependent LC-MS/MS experiments. Over 90% of masses detected by LC-MS/MS were also detected by data-independent LC-MS at the correct retention times with similar fragmentation patterns. The data-independent LC-MS was able to detect more components than the individual data-dependent LC-MS/MS experiments.
This document describes a novel database search algorithm for identifying proteins from data independent acquisitions where multiple precursor ions are fragmented simultaneously. The algorithm uses an iterative process to incrementally increase selectivity, specificity, and sensitivity. It accounts for peptide retention time, ion intensities, charge states, and accurate masses of precursors and products. The algorithm was tested on simple and complex protein mixtures and validated independently, demonstrating its ability to correctly identify proteins across a wide dynamic range with high sensitivity and specificity.
proteomics, mass spectrometry, science, bioinformatics, electrophoresis, liqu...Amit Yadav
This document discusses quantitative mass spectrometry techniques for protein quantitation. It describes both labeling approaches, such as stable isotope labeling with amino acids in cell culture (SILAC) and isobaric tags for relative and absolute quantitation (iTRAQ), as well as label-free approaches including intensity-based measurements, spectral counting, and normalized spectral abundance factor. The labeling approaches involve metabolic, enzymatic, or chemical labeling of proteins prior to mass spectrometry analysis, while label-free methods quantify proteins based on spectral properties without labeling.
The document discusses aptamers, which are oligonucleotides or peptides that bind to targets with high affinity and specificity due to their unique three-dimensional structures. It describes how aptamers are produced through an in vitro selection process called SELEX and classified based on their structure and selection technique. The document also compares aptamers to antibodies and outlines several applications of aptamers in therapeutics, drug delivery, bioimaging, diagnostics, and more.
The document discusses proteomics, which is the study of the proteome or total protein complement of a biological system. Proteomics aims to understand protein expression, functions, interactions, and modifications through various analytical techniques and faces many challenges due to the complexity of proteins. Key approaches in proteomics include expression profiling to compare protein levels between healthy and disease states, structural analysis to determine protein structures, and network mapping to study protein interactions. Mass spectrometry and bioinformatics tools play important roles in proteomic studies, which have applications in characterizing protein complexes and identifying disease biomarkers.
Application of proteomics for identification of abiotic stress tolerance in c...Vivek Zinzala
It is the study of “Proteome”.
The word "proteome" is a blend of "protein" and "genome”.
Large scale study of Proteins.
Particularly their structures and functions.
Study of full set of proteins in a cell type or tissue, and changes during various conditions
Proteins – Basics you need to know for ProteomicsLionel Wolberger
The document provides an overview of key concepts in proteomics, including:
1) It discusses protein structure and function, the 20 common amino acids, and post-translational modifications that proteins undergo.
2) It introduces common techniques used in proteomics like chromatography, electrophoresis, mass spectrometry, and bioinformatics.
3) It summarizes protein analysis methods like gel electrophoresis, isoelectric focusing, and immunological assays used to detect and purify proteins of interest.
Peptide Mass Fingerprinting (PMF) and Isotope Coded Affinity Tags (ICAT)Suresh Antre
Analytical technique for identifying unknown protein. The peptide mass are compared to database containing the theoretical peptide masses of all known protein sequences.
This document summarizes a study that used label-free LC-MS to analyze changes in the proteome of Escherichia coli (E. coli) when grown in different carbon sources (glucose, lactose, acetate). E. coli is a commonly used model organism and understanding its response to environmental changes provides insights into microbial physiology. The LC-MS approach simultaneously identified and quantified proteins across conditions without isotopic labeling. Relative protein abundances ranged from 0.1- to 90-fold changes between conditions. The results correlated well with known E. coli biochemistry and previous transcriptional profiling studies. This label-free LC-MS method provides an effective way to characterize microbial proteomes.
A brief introfuction of label-free protein quantification methodsCreative Proteomics
If you want to know more about our services, please visit https://www.creative-proteomics.com/services/label-free-quantification.htm.
Label-free protein quantification is a mass spectrometry-based method for identifying and quantifying relative changes in two or more biological samples instead of using a stable isotope-containing compound to label proteins.
Overview Radboudumc Center for Proteomics, Glycomics and Metabolomics april 2015Alain van Gool
An overview of the proteomics, glycomics and metabolomics expertise and capabilities within the Translational Metabolic Laboratory of the Radboudumc. We're interested in collaboration with academic and industrial partners, either bilateral or as part of multi-partner consortia.
This document discusses microbial proteomics and various proteomics techniques. It describes structural proteomics which analyzes protein structures to identify gene functions and interaction sites. Interaction proteomics analyzes protein-protein interactions to determine functions. Expression proteomics identifies proteins differentially expressed between related samples like healthy and diseased tissue. Microbial proteomics studies microorganism proteins using techniques like mass spectrometry, electrospray ionization, and matrix-assisted laser desorption-ionization. Gel-based separation techniques including 1D, 2D, and 3D gel electrophoresis are also discussed.
If you want to know more, please visit https://www.creative-proteomics.com/s...
Stable isotope labeling using amino acids in cell culture (SILAC) is a powerful method based on mass spectrometry that identifies and quantifies relative differential changes in protein abundance. First used in quantitative proteomics in 2002, it provides accurate relative quantification without any chemical derivatization or manipulation.
This document summarizes proteomics and metabolomics techniques for mapping biochemical regulations. It discusses how proteomics uses techniques like gel electrophoresis, liquid chromatography, and mass spectrometry to separate and identify proteins on a large scale. Metabolomics similarly aims to analyze all metabolites in a biological system using techniques like fingerprinting, profiling, and integrating with other omics data. Together, proteomics and metabolomics provide multiple levels of insight into cellular processes by examining changes in gene expression, protein abundance, and metabolic activity.
This document provides an overview of proteomics. It discusses the goals of proteomics including global protein analysis, expression, function, and biomarker discovery. It covers different types of proteomics like expression, structural, and functional proteomics. Methods for protein measurement like mass spectrometry and 2D gel electrophoresis are described. The document discusses clinical applications of proteomics in areas like cancer, infectious diseases, CNS disorders and cardiovascular disease. It also touches on challenges like target discovery and costs, as well as future perspectives and conclusions on the potential of proteomics.
Proteins interact with other proteins to carry out most biological processes in cells. Understanding these protein-protein interactions (PPIs) is important for determining how proteins function. There are several methods for detecting and analyzing PPIs, including affinity chromatography, immunoprecipitation, cross-linking, phage display, and genetic techniques like using suppressor mutations or observing synthetic lethal effects. Each method has advantages and disadvantages for elucidating different types of PPIs that are integral to cellular processes.
This document provides an overview of proteomics, including its definition, types, and analysis steps. It discusses several techniques used in proteomics, such as chromatography, ELISA, Western blotting, protein microarrays, Edman sequencing, SDS-PAGE, 2D gel electrophoresis, 2D-DIGE, SILAC, iTRAQ, X-ray crystallography, and mass spectrometry. The techniques allow for studying protein expression, structure, functions, interactions, and modifications to better understand cellular processes.
Proteomics is the study of the complete set of proteins expressed in an organism under particular conditions. It aims to understand protein expression in response to changing conditions like disease. Tools in proteomics include cell lysis, fractionation, protein concentration and quantification, digestion, and peptide cleanup prior to mass spectrometry analysis. Key techniques discussed are molecular techniques like SAGE, separation techniques like gel electrophoresis and chromatography, and protein identification techniques like mass spectrometry.
The document discusses protein-protein interactions (PPIs), including an introduction to PPIs, the types of interactions, techniques used to study them like X-ray crystallography, NMR spectroscopy and cryo-electron microscopy, and factors that affect PPIs. It also covers methods to investigate PPIs such as affinity purification coupled with mass spectrometry and yeast two-hybrid screening. Applications of understanding PPIs include developing therapeutic drugs and identifying functions of unknown proteins.
Aptamers provide opportunities for structure-based drug design strategies relevant to therapeutic intervention. Recent advances in the chemical modifications of nucleic acids suggest that one of the major barriers to use, stability, can be overcome. The high affinity and specificity of aptamers rival antibodies and make them a promising tool in diagnostic and therapeutic application. We should expect more aptamers to be isolated in the near future against an ever increasing repertoire of targets, using these different SELEX approaches with increased speed and efficiency. Aptamers are poised to successfully compete with monoclonal Abs in therapeutics and drug development within the next few decades.
Aptamers are single-stranded DNA or RNA molecules that can bind to specific molecular targets with high affinity and specificity. They are identified through an in vitro selection process called Systematic Evolution of Ligands by EXponential enrichment (SELEX). Aptamers have various applications as diagnostic and therapeutic agents. They can serve as biosensors for pathogens by utilizing techniques like electrochemical, fluorescence, and colorimetric detection. Aptamers have been developed against viruses like influenza, HIV, hepatitis C, and others. They show potential for blocking viral fusion and inhibiting viral replication through binding to proteins and enzymes.
This document describes a study comparing data acquired from data-independent LC-MS to data acquired from data-dependent LC-MS/MS. The study analyzed mixtures of four proteins alone and with a complex E. coli protein digest. Each sample was run in triplicate by both acquisition methods. The data-independent LC-MS provided more comprehensive detection of precursor and product ions than the combined data-dependent LC-MS/MS experiments. Over 90% of masses detected by LC-MS/MS were also detected by data-independent LC-MS at the correct retention times with similar fragmentation patterns. The data-independent LC-MS was able to detect more components than the individual data-dependent LC-MS/MS experiments.
This document describes a novel database search algorithm for identifying proteins from data independent acquisitions where multiple precursor ions are fragmented simultaneously. The algorithm uses an iterative process to incrementally increase selectivity, specificity, and sensitivity. It accounts for peptide retention time, ion intensities, charge states, and accurate masses of precursors and products. The algorithm was tested on simple and complex protein mixtures and validated independently, demonstrating its ability to correctly identify proteins across a wide dynamic range with high sensitivity and specificity.
1) Researchers used a new LC/MS technique called LCMSE to analyze the proteomes of Mycobacterium bovis bacteria that were either untreated or exposed to the tuberculosis drug isoniazid (INH).
2) They identified over 6,600 peptides and 103 proteins, with 24 proteins showing differing levels between the untreated and INH-exposed bacteria.
3) This provides new insights into how INH works and the bacterial response, and could help identify new drug targets through studying changes in the proteome over time or between drug-resistant and non-resistant strains.
This document describes a label-free quantitative proteomics method using liquid chromatography coupled to mass spectrometry (LC/MS). The method relies on comparing changes in peptide signal responses and retention times (accurate mass retention time or AMRT components) between control and experimental samples to determine relative protein abundance changes. The method was tested by spiking increasing amounts of standard proteins into human serum samples and observing a linear relationship between signal response and protein concentration. The quantitative proteomics strategy provides a simple LC/MS-based method for comparing protein profiles between samples without using stable isotope labeling.
This document describes a study that used quantitative proteomic analysis with liquid chromatography and mass spectrometry to analyze changes in protein expression in mycobacteria in response to isoniazid treatment. Key findings include:
1) Proteins encoded by the kas operon (AcpM, KasA, KasB, Accd6), which are involved in fatty acid synthesis, were significantly overexpressed in response to isoniazid treatment.
2) Proteins involved in iron metabolism and cell division were also overexpressed, suggesting a complex interplay of metabolic events leading to cell death upon isoniazid treatment.
3) The study provides insights into mycobacterial responses to chemotherapeutics
NIH's Glen Hortin discusses translating new biomarkers into clinical laboratory tests. As a proteomics pioneer, Hortin has worked on protein processing and post-translational modifications for over 20 years. He advises focusing biomarker research on clinically relevant problems and validating candidates rigorously before developing diagnostic tests. Hortin also stresses the need to standardize assays and educate clinicians on appropriate test usage.
Demonstration of alternate scanning LCMS for simultanous acquisition of precursor and product ions without precursor mass selection (ie Multiplex LCMS).
This document summarizes a study that used label-free LC-MS/MS to simultaneously identify and quantify proteins in E. coli grown in different carbon sources (glucose, lactose, acetate). The methodology involved growing E. coli in different media, extracting and digesting proteins, and performing LC-MS/MS analysis using alternating low and elevated collision energies to obtain precursor and fragment ion data. Over 7,000 accurate mass measurements of precursors and 25,000 of fragments were obtained. Multiple peptides were identified for relative protein quantitation. Results showed differential expression of proteins involved in carbon utilization pathways between the growth conditions. The label-free quantitation was consistent with other omics methods.
Proteomics, definatio , general concept, signficanceKAUSHAL SAHU
INTRODUCTION
GENERAL CONCEPT
WHY PROTEIOMIC NECESERY?
WHAT PROTEOMIC CAN ANSWER?
PRTEOMICS- ANALYSIS AND IDENTIFICATION OF PROTEIN
TWO-DIMENSIONAL SDS-PAGE
MASS SPECTROMETERS
SIGNIFICANCE OF STUDY AN ITS IMPORTANCE
APPLICATIONS
CHALLENGES
CONCLUSIONS
REFERENCES
Yeast two-hybrid is based on the reconstitution of a functional transcription factor (TF) when two proteins or polypeptides of interest interact. Upon interaction between the bait and the prey, the DBD and AD are brought in close proximity and a functional TF is reconstituted upstream of the reporter gene.
Genomics and proteomics in drug discovery and developmentSuchittaU
This document discusses the role of genomics and proteomics in drug discovery and development. It explains that genomics and proteomics technologies can help identify new drug targets by comparing gene and protein expression between healthy and diseased cells. Proteomics in particular analyzes changes in protein levels and can quantify individual proteins using techniques like 2D gel electrophoresis and mass spectrometry. The integration of genomics and proteomics provides a more comprehensive understanding of biological systems and is improving the drug discovery process.
Functional proteomics, methods and toolsKAUSHAL SAHU
INTRODUCTION
HISTORY
DEFINITION
PROTEOMICS
FUNCTIONAL PROTEOMICS
PROTEOMICS SOFTWARE
PROTEOMICS ANALYSIS
TOOLS FOR PROTEOM ANALYSIS
DIFFERENTS METHODS FOR STUDY OF FUNCTIONAL PROTEOMICS
APLLICATIONS
LIMITATIONS
CONCLUSION
This document discusses genomics and proteomics based drug discovery. It explains that genomics involves sequencing genomes to understand gene functions and interactions, while proteomics studies protein expression and interactions. The document outlines how structural bioinformatics and techniques like protein-ligand docking can help in drug target identification and rational drug design. It also discusses how proteomics can aid in various stages of drug discovery like target identification and validation.
Structural genomics is a field that aims to determine the 3D structures of all proteins encoded by a genome. It involves determining structures on a large scale using techniques like X-ray crystallography and NMR. This allows identification of novel protein folds and potential drug targets. Comparative genomics compares genomic features between organisms and provides insights into evolution and conserved sequences and functions. It is a key tool in fields like medicine and agriculture.
This document discusses protein microarrays, which are a high-throughput screening method. Protein microarrays allow researchers to study protein interactions and activities on a large scale by placing many proteins on a solid surface in defined locations. There are two main types of protein microarrays: analytical microarrays which use antibodies or other capture molecules to probe complex protein solutions, and functional microarrays which contain full-length functional proteins to study protein-protein and other biochemical interactions. Protein microarrays have applications in areas like biomarker identification, studying post-translational modifications, and host-pathogen interactions. However, several challenges remain in developing stable and sensitive protein microarrays.
Proteomics is the study of the proteome, which is the entire set of proteins expressed by a genome, cell, tissue or organism. This document discusses several techniques used in proteomics including 2D gel electrophoresis, mass spectrometry, and protein databases. It provides examples of applications such as biomarker identification for disease diagnosis and drug target discovery. Limitations include the complexity of proteomes and no single technique being adequate for complete analysis. Overall, proteomics techniques help further our understanding of protein structure, function and interactions to gain insights into biological processes and diseases.
Proteomics in VSC for crop improvement programmeSumanthBT1
Proteomics techniques such as gel electrophoresis and mass spectrometry are used to separate and identify proteins. Two-dimensional gel electrophoresis separates proteins by size and charge, while MALDI-TOF mass spectrometry relies on mass spectrometry to analyze proteins and peptides. Protein-protein interactions can be studied using techniques like yeast two-hybrid systems and co-immunoprecipitation. Databases such as UniProt, PDB, and KEGG provide information on protein sequences, structures, and pathways.
Proteomics aims to comprehensively describe the biological systems of a species by obtaining a sufficient density of observations on all the proteins expressed by a cell or tissue. Initial goals were to rapidly identify all proteins expressed, but this has yet to be achieved for any species due to the large and complex nature of proteomes. Various technologies are used to study proteins including gene ontology, biochemical analysis, tagging, mass spectrometry, and protein interaction mapping to better understand protein functions, localizations, modifications, and connections within cellular pathways and systems.
Genomics and proteomics have many applications in fields like medicine, biotechnology, and social sciences. Genomics allows for better understanding of disease bases and drug responses by integrating genomic data with other data types. Proteomics identifies protein structures, functions, and interactions through techniques like identifying biomarkers, studying post-translational modifications, and analyzing protein expression profiles. These 'omics technologies continue to provide insights into disease mechanisms and potential drug targets.
Protein-protein interactions (PPIs) are important for many cellular functions. There are two main types of PPIs - transient interactions which are brief, and stable interactions which form multiprotein complexes. Crosslinking can capture both transient and stable PPIs by covalently binding interacting proteins. In vivo crosslinking studies PPIs in their native environment while in vitro crosslinking allows better reaction control. Pull-down assays use affinity purification to isolate stable protein complexes and identify binding partners of a bait protein. SDS-PAGE is commonly used to separate and visualize proteins isolated by techniques like pull-down.
Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.
Target discovery and Validation - Role of proteomicsShivanshu Bajaj
This presentation include how important is the branch proteomics in target discovery and validation for new drugs. It also include proteomic technology and current approaches in targeted proteomics
role of proteomics in target discovery and validation
1 target of drug action
2 proteomics
3 facts about proteins
4 post translational modification
5 additional modification
6 methods of studying proteins
7 hybrid technologies
nternational Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
This document discusses various bioinformatics approaches for analyzing molecular interactions, including protein-protein interaction, protein-ligand interaction, docking, pharmacophore, and virtual screening. It provides details on each topic, describing things like how protein-protein interactions occur and are classified, common methods for studying protein-ligand interactions, the basic process and types of docking, and the definition of a pharmacophore. The key topics covered are protein-protein interaction, protein-ligand interaction analysis through methods like docking, and virtual screening using pharmacophore models.
Structural genomics aims to determine the 3D structure of all proteins in a genome. It uses high-throughput methods like X-ray crystallography and NMR on a genomic scale. This allows determination of protein structures for entire proteomes. It provides insights into protein function and can aid drug discovery by identifying potential drug targets like in Mycobacterium tuberculosis. Structural genomics leverages completed genome sequences to clone and express all encoded proteins for structural characterization.
This document discusses proteomics and systems biology. It explains that systems biology analyzes relationships between elements in a biological system in response to genetic or environmental changes, with the goal of understanding the system. Proteomics aims to characterize proteins, including structure, function, interactions, and expression levels. Integrating omics data like genomics, proteomics and metabolomics can provide insights into biological processes and disease. Proteomics can also play roles in drug development by aiding biomarker discovery, target identification, and toxicity prediction.
2. Absolute Quantification of Proteins
each protein has not been described. Recently Ishihama et al. peptide along with the endogenous peptide. This intensity
(12) reported an emPAI1 value that the authors suggest is signal response is compared with an intensity calibration
directly proportional to the protein content in a protein mix- curve created using the introduced synthetic molecule to
ture. The authors reported a quantitative deviation of 63% determine the amount of the endogenous protein in the mix-
from the actual abundance using the described emPAI ture. A disadvantage with using synthetic peptides is that
method. The method describes the correlation between the extra steps are required to synthesize an authentic sample
number of observed peptides to a protein and its absolute and to later “spike” the synthetic standard prior to being able
amount. As the amount of protein increases, the number of to determine the absolute quantity of the protein itself. To
observed peptides to the protein also increases. This method perform the absolute quantification for a number of proteins
is useful within a narrow protein concentration range whereby within a mixture would require one to provide a synthetic
the observed peptides continue to increase linearly as a func- standard for each protein of interest.
tion of the amount of protein. However, once a higher protein Another technique for absolute quantification of proteins
concentration is reached and all the observable peptides have uses radiolabeled amino acids such as [35S]methionine,
Downloaded from www.mcponline.org at CELL SIGNALING TECHNOLOGY on May 22, 2007
been identified, the relationship deteriorates to an asymptotic whose specific activity is known (15, 16). In this type of
limit. Additionally this method relies on characterizing pep- experiment, an amino acid, such as [35S]methionine, is incor-
tides using traditional data-directed MS/MS and may there- porated in the culture medium of the growing cell(s). As pro-
fore be sensitive enough only to quantify the more abundant teins are synthesized, [35S]methionine is incorporated into the
proteins present in a mixture. cellular proteins. Based on the extent of incorporation of the
A more traditional approach to determine the absolute con- radiolabel, the absolute amount of the peptide or protein can
centration of a protein (or proteins) in a complex mixture be determined. These types of experiments are costly, require
involves the use of stable isotope-labeled peptides spiked good standard operating procedures and specialized quanti-
into the mixture. This allows direct correlation between the tative techniques, and can also be deleterious to the subject
stable isotope-labeled peptide and its naturally occurring an- under study. Consequently determining absolute quantifica-
alog (13). Kuhn et al. (13) have carried out this stable isotope tion of proteins using radiolabel techniques is limited to ex-
dilution strategy by using synthetic 13C-labeled peptides and pendable biological systems such as microbes, plants, and
multiple reaction monitoring as an analytical method for pre- cell cultures.
screening candidate protein biomarkers in human serum prior In this work we describe a method that provides absolute
to antibody and immunoassay development. quantification of proteins from LCMS data of simple or com-
Typically absolute quantification of proteins requires the plex mixtures of tryptic peptides without requiring the use of
use of one or more external reference peptides to generate a numerous external reference peptide(s) or the implementation
calibration-response curve for specific polypeptides from that of radiolabeling methods. The method describes how to ob-
protein (i.e. synthetic tryptic polypeptide product). The abso- tain a single point calibration for the mass spectrometer that
lute quantification of the given protein is determined from the is applicable to the subsequent absolute quantification of all
observed signal response for the specific polypeptide in the other characterized proteins within the complex mixture.
sample relative to that generated in the calibration curve. If the
absolute quantification of a number of different proteins is to EXPERIMENTAL PROCEDURES
be determined, separate calibration curves are necessary for Preparation of Simple Protein Mixture—A 50 pmol/ l stock of each
each specific external reference peptide for each protein. predigested protein was prepared in 50 mM ammonium bicarbonate
Absolute quantification allows one not only to determine (pH 8.5) with 0.05% RapiGestTM to assist in the redissolving the
changes between two conditions but also to perform quanti- lyophilized tryptic peptides (17). The samples were incubated at 60 °C
tative protein comparisons within the same sample. for 15 min to redissolve the lyophilized samples. The stock solutions
of the individual protein digests were used to prepare a single stock
Gerber et al. (14) describe a conventional technique for solution of the six standard digested proteins (yeast enolase and
absolute quantification of proteins and their corresponding alcohol dehydrogenase, rabbit glycogen phosphorylase, and bovine
modified states in complex mixtures using a synthesized pep- serum albumin and hemoglobin). The single stock solution of the six
tide as a reference standard. The reference peptide is chem- predigested proteins was prepared in 50 mM ammonium bicarbonate
ically identical to one of the naturally occurring tryptic pep- with 0.05% RapiGest such that each protein was present at the
following concentration: glycogen phosphorylase B, 2.4 pmol/ l; he-
tides of a given protein. The reference standard is introduced
moglobin, 4.0 pmol/ l; alcohol dehydrogenase, 4.0 pmol/ l; serum
to a complex mixture. The mixture is analyzed using LCMS to albumin, 5.0 pmol/ l; and enolase, 6.0 pmol/ l. Six additional protein
measure the corresponding signal intensity for the derivative samples were prepared from a dilution series of the following stock
solutions in ammonium bicarbonate with 0.05% RapiGest: 2-, 5-, 10-,
20-, and 50-fold. Each sample was diluted with an equal volume of 50
1
The abbreviations used are: emPAI, exponentially modified pro- mM ammonium bicarbonate (pH 8.5) to reduce the concentration of
tein abundance index; CapLC, capillary LC; PLGS, ProteinLynx Glo- RapiGest to 0.025%. The simple protein mixtures were centrifuged at
bal Server; Cv, coefficient of variation; RSD, relative standard devia- 13,000 rpm for 10 min, and the supernatant was transferred into an
tion; SR, signal response, peak intensity. autosampler vial for peptide analysis via LCMSE.
Molecular & Cellular Proteomics 5.1 145
3. Absolute Quantification of Proteins
Preparation of Simple Protein Mixture in Human Serum—An addi- Samples (5- l injection) were loaded onto the column with 6% mobile
tional protein digest stock solution of the six standard proteins was phase B. Peptides were eluted from the column with a gradient of
prepared in human serum ( 1.5 g/ l of total serum protein) con- 6 – 40% mobile phase B over 100 min at 4.4 l/min followed by a
taining 0.05% RapiGest such that each protein digest was at the 10-min rinse of 99% mobile phase B. The column was immediately
following concentration: glycogen phosphorylase B from rabbit, 2.4 re-equilibrated at initial conditions (6% mobile phase B) for 20 min.
pmol/ l; hemoglobin from a cow, 4.0 pmol/ l; alcohol dehydrogen- The lock mass, [Glu1]fibrinopeptide at 100 fmol/ l, was delivered from
ase from yeast, 4.0 pmol/ l; serum albumin from a cow, 5.0 pmol/ l; the auxiliary pump of the CapLC system at 1 l/min to the reference
and enolase from yeast, 6.0 pmol/ l. Six additional samples were sprayer of the NanoLockSprayTM source. All samples were analyzed
prepared from a dilution series of the following stock solution in in triplicate.
human serum with 0.05% RapiGest: 2-, 5-, 10-, 20-, and 50-fold. Mass Spectrometer Configuration—Mass spectrometry analysis of
Preparation of Human Serum for Biological Replicates—Human tryptic peptides was performed using a Waters/Micromass Q-TOF
serum was prepared from seven individuals using BD Biosciences Ultima API. For all measurements, the mass spectrometer was oper-
VacutainersTM with clot activator as suggested by the manufacturer. ated in V-mode with a typical resolving power of at least 10,000. All
A total of 300 – 400 g of total serum protein (5 l) was digested analyses were performed using positive mode ESI using a NanoLock-
according to the procedure outlined under “Protein Digest Prepara- Spray source. The lock mass channel was sampled every 30 s. The
tion.” After digesting the serum proteins, 1 pmol of purified tryptic mass spectrometer was calibrated with a [Glu1]fibrinopeptide solution
Downloaded from www.mcponline.org at CELL SIGNALING TECHNOLOGY on May 22, 2007
enolase digest was added to each sample prior to LCMS analysis. (100 fmol/ l) delivered through the reference sprayer of the
Media and Escherichia coli Growth Conditions—Frozen E. coli NanoLockSpray source. Accurate mass LCMS data was collected in
(ATCC10798, K-12) cell stocks were streaked onto Luria-Bertani (LB) an alternating, low energy (MS) and elevated energy (MSE) mode of
plates and grown at 37 °C. An individual colony was subsequently acquisition. The spectral acquisition time in each mode was 1.8 s with
streaked onto a plate of M9 minimal medium supplemented with a 0.2-s interscan delay. In low energy MS mode, data was collected
0.5% sodium acetate and grown at 37 °C. Seed cultures were gen- at a constant collision energy of 10 eV. In elevated MSE mode,
erated by transferring single colonies into flasks of M9 minimal me- collision energy was ramped from 28 to 35 eV during each 1.8-s data
dium supplemented with 0.5% sodium acetate. Seed culture flasks collection cycle. One cycle of MS and MSE data was acquired every
were shaken at 250 rpm at 37 °C until midlog phase (A600 0.9 –1.1). 4.0 s. The RF applied to the quadrupole mass analyzer was adjusted
The seed culture was diluted 1 ml:500 ml into separate M9 minimal such that ions from m/z 300 to 2000 were efficiently transmitted,
medium supplemented with 0.5% glucose. Flasks were shaken at 250 ensuring that any ions observed in the LCMSE data less than m/z 300
rpm at 37 °C until midlog phase (A600 0.9 –1.1). The E. coli cell were known to arise from dissociations in the collision cell.
cultures were harvested by centrifugation, and pellets were frozen at Data Processing and Protein Identification—The continuum LCMSE
80 °C. Frozen cells were suspended in 5 ml/1 g of biomass in lysis data were processed and searched using ProteinLynx Global Server
buffer (Dulbecco’s phosphate-buffered saline 1:100 protease in- (PLGS) version 2.2. Protein identifications were obtained by searching
either a human database to which data from the six standard proteins
hibitor mixture (Sigma catalog number 8340)) in a 50-ml Falcon tube.
were appended or an E. coli database. The ion detection, clustering,
The cells were lysed by sonication in a Microson XL ultrasonic cell
and normalization were processed using PLGS as described earlier
disrupter (Misonix, Inc.) at 4 °C. The cell debris were removed by
(8). Additional data analysis was performed with Spotfire Decision Site
centrifugation at 15,000 g for 30 min at 4 °C, and the resulting
version 7.2 and Microsoft Excel.
soluble protein extracts were diluted to 5 mg/ml with lysis buffer,
dispensed into 50- l aliquots (250 g), and stored at 80 °C for
RESULTS AND DISCUSSION
subsequent analysis. The E. coli protein extract was spiked with
tryptic peptides from yeast enolase to a final concentration of 400 Method for Absolute Quantification—The method for abso-
fmol/ l before storing at 80 °C. lute quantification of proteins requires that a known quantity
Protein Digest Preparation—A 100- l aliquot of the human serum
of intact protein be spiked into the protein mixture of interest
samples was reduced in the presence of 10 mM dithiothreitol at 60 °C
for 30 min. The protein was alkylated in the dark in the presence of 50 prior to digestion with trypsin or that a known quantity of
mM iodoacetamide at room temperature for 30 min. Proteolytic di- predigested protein be spiked into the mixture after it has
gestion was initiated by adding modified trypsin (Promega) at a con- been digested. The average MS signal response for the three
centration of 75:1 (total protein to trypsin, by weight) and incubated most intense tryptic peptides is calculated for each well char-
overnight at 37 °C. Each digestion mixture was diluted to a final
acterized protein in the mixture, including those to the internal
volume of 200 l with 50 mM ammonium bicarbonate (pH 8.5) to
reduce the concentration of RapiGest detergent to 0.025%. Each
standard protein(s). The average MS signal response from the
sample was analyzed in triplicate. The tryptic peptide solution was internal standard protein(s) is used to determine a universal
centrifuged at 13,000 rpm for 10 min, and the supernatant was signal response factor (counts/mol of protein), which is then
transferred into an autosampler vial for peptide analysis via LCMS. applied to the other identified proteins in the mixture to de-
The LCMSE analysis was performed using 5 l of the final peptide termine their corresponding absolute concentration. The ab-
mixture.
solute quantity of each well characterized protein in the mix-
Approximately 250 g (50 l) of E. coli protein was suspended in a
final volume of 100 l containing 50 mM ammonium bicarbonate (pH ture is determined by dividing the average MS signal response
8.5) and 0.05% RapiGest. The protein mixture was reduced and of the three most intense tryptic peptides of each well char-
alkylated as described above. acterized protein by the universal signal response factor de-
HPLC Configuration—Capillary LC (CapLC) of tryptic peptides was scribed above.
performed with a Waters CapLC system equipped with a Waters
Analysis of Peptides from the Standard Six-protein Mix-
NanoEaseTM AtlantisTM C18, 300- m 15-cm reverse phase column.
The aqueous mobile phase (mobile phase A) contained 1% acetoni- ture—The serial dilutions of the six standard protein digests
trile in water with 0.1% formic acid. The organic mobile phase (mobile were analyzed using an alternate scanning mode of data
phase B) contained 80% acetonitrile in water with 0.1% formic acid. acquisition (LCMSE) described previously by us (8). The proc-
146 Molecular & Cellular Proteomics 5.1
4. Absolute Quantification of Proteins
TABLE I
Protein coverage from the LCMSE analysis of the six-protein mixture
These results reflect those peptides that replicated in at least two of the three replicate injections.
Molecular Percent protein coverage, number of peptides, concentrationa
Description
weight Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6
Enolase 46,624 77, 32, 15.00 62, 29, 7.50 61, 27, 3.00 51, 23, 1.50 37, 17, 0.75 22, 11, 0.30
Serum albumin 69,248 79, 55, 12.50 79, 54, 6.25 77, 53, 2.50 69, 47, 1.25 49, 28, 0.63 31, 18, 0.25
Alcohol dehydrogenase I 36,669 72, 27, 10.00 71, 26, 5.00 71, 26, 2.00 47, 20, 1.00 31, 12, 0.50 25, 8, 0.25
Phosphorylase B 97,097 69, 62, 6.00 67, 60, 3.00 55, 49, 1.20 34, 32, 0.60 14, 12, 0.30 2, 2, 0.12
Hemoglobin ( ) 15,044 84, 9, 5.00 84, 9, 2.50 84, 9, 1.00 59, 7, 0.50 45, 6, 0.25 10, 1, 0.10
Hemoglobin ( ) 15,944 91, 14, 5.00 82, 12, 2.50 82, 12, 1.00 71, 10, 0.50 39, 5, 0.25 8, 1, 0.10
a
Concentration is reported as picomoles loaded onto a 300- m column.
TABLE II
Identified peptides to P02070 bovine hemoglobin ( chain) (5 pmol) obtained from the LCMSE analysis of the stock protein mixture in Table I
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The table includes the average mass, retention time, and intensity measurements calculated from the triplicate analysis. The residues in
parentheses are the N- (left) and C-terminal (right) residues flanking the tryptic peptide as determined from the intact protein sequence.
a
MH Mass RSD Rta Rt RSD Intensitya Int RSD Actual MH Sequence
ppm
740.3977 1.0 10.12 2.4 35,792 23.8 740.3943 4.6 (K)HLDDLK(G)
821.4150 0.9 12.35 3.4 14,798 16.4 821.4079 9.3 ( )MLTAEEK(A)
950.5161 1.9 51.64 0.7 46,047 8.9 950.5099 6.5 (K)AAVTAFWGK(V)
1,097.5346 0.5 39.59 1.4 22,656 12.4 1,097.5301 4.0 (K)VLDSFSNGMK(H)
1,098.5621 0.9 37.00 1.3 43,353 8.6 1,098.5583 3.4 (K)LHVDPENFK(L)
1,101.5625 0.9 30.26 1.0 47,977 5.4 1,101.5541 7.6 (K)VDEVGGEALGR(L)
1,177.6773 0.4 37.84 1.5 25,346 14.1 1,177.6805 2.7 (K)VVAGVANALAHR(Y)
1,265.8287 3.9 94.01 0.7 623 29.7 1,265.8309 1.8 (K)LLGNVLVVVLAR(N)
1,274.7292 0.9 72.38 0.9 112,603 2.2 1,274.7261 2.4 (R)LLVVYPWTQR(F)
1,328.7156 0.8 34.91 1.4 39,273 13.1 1,328.7174 1.4 (K)VKVDEVGGEALGR(L)
1,422.7315 0.8 61.32 1.0 172,329 2.4 1,422.7269 3.2 (K)EFTPVLQADFQK(V)
1,448.6856 0.3 50.32 1.2 80,876 4.7 1,448.6844 1.1 (K)GTFAALSELHCDK(L)
2,089.9633 1.1 77.59 0.9 110,291 3.4 2,089.9541 4.4 (R)FFESFGDLSTADAVMNNPK(V)
2,105.9592 0.6 69.59 1.0 11,327 3.6 2,105.9490 4.8 (R)FFESFGDLSTADAVM*NNPK(V)
Average 1.1b 1.3b 10.6b 4.7c
a
The relative standard deviation was determined from the replicate mass, retention time, and intensity measurements of each detected
peptide. The mass accuracy is reported for each identified peptide. An overall relative standard deviation is reported for the mass RSD,
retention time RSD (rt RSD), and intensity RSD (int RSD).
b
An overall root mean square error is reported for all the identified peptides to bovine beta hemoglobin.
c
Oxidized methionine is denoted as M*.
essing software is capable of generating properly integrated the analysis to a 75- m chromatography system would cor-
peptide signal intensity measurements (deisotoped and respond to 6 –900 fmol of total protein for the analysis of the
charge state-reduced) and accurately mass-measured, pep- same sample after diluting 16-fold, 1/R2). These detection
tide ion lists that are used for subsequent qualitative identifi- limits are within acceptable levels for existing technologies. A
cations and relative quantification across 3– 4 orders of mag- minimum limit of 100 fmol of a single protein was determined
nitude dynamic range in ion detection. Because this mode of for the purpose of this analysis using a 300- m chromatog-
data acquisition does not bias the LCMS analysis by gas raphy system and a standard nanoelectrospray source at 5
phase preselection of candidate peptide precursors, an in- l/min. At 100 fmol of a single protein, the top three most
ventory of all the peptide components (precursor and frag- intense tryptic peptides to most of the proteins could be
ments) above the limit of detection of the instrument is pro- identified with a high degree of confidence. The experiment
duced. The time-resolved mass measurements provide the was set up so that each protein spanned a 50-fold dynamic
ability to properly align detected fragment ions with their range with an overall dynamic range of 2.2 orders of mag-
respective precursor ions for accurate identification. nitude for the entire set of protein standards. The protein
The protein sequence coverage obtained from the PLGS- sequence coverage of each protein is shown to decrease in a
processed LCMSE data of the six protein mixtures is outlined concentration-dependent manner, as one would expect,
in Table I. The total amount of standard protein loaded onto ranging from 84 to 2% throughout the entire data set. Repli-
the 300- m column ranged from 100 to 15,000 fmol (scaling cate analysis of each sample produced signal intensity meas-
Molecular & Cellular Proteomics 5.1 147
5. Absolute Quantification of Proteins
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FIG. 1. Characterized peptides to hemoglobin (A) and phosphorylase B (B) using LCMSE. A bar plot of the identified peptides to
hemoglobin (x axis) and their corresponding average signal response (y axis) from the LCMSE analyses of each sample of the six-protein mixture
is shown. The absolute quantity of hemoglobin (A) loaded onto the analytical column is indicated by the following color coding: red, 100 fmol;
dark blue, 250 fmol; yellow, 500 fmol; black, 1000 fmol; green, 2500 fmol; and white, 5000 fmol. The average relative standard deviation of the
mass, intensity, and retention time measurements for the characterized peptides to hemoglobin across the triplicate analyses were 1.2 ppm,
148 Molecular & Cellular Proteomics 5.1
6. Absolute Quantification of Proteins
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FIG. 2. Signal responses for peptides to the standard proteins. A 5- l injection of the following six standard proteins was analyzed by
LCMS: glycogen phosphorylase B (6 pmol, 97 kDa) from rabbit, hemoglobin (10 pmol, 31 kDa total, (15 kDa) and (16 kDa)) from a cow,
alcohol dehydrogenase (10 pmol, 25 kDa) from yeast, serum albumin (12.5 pmol, 70 kDa) from a cow, and enolase (15 pmol, 50 kDa) from yeast.
The LCMS data were processed by PLGS, and the identified peptides were organized by decreasing intensity for each protein. The bar plots
illustrate the peptides identified to alcohol dehydrogenase (A), serum albumin (B), enolase (C), hemoglobin (D), hemoglobin (E), and
phosphorylase B (F). The y axis is the average intensity of the detected peptide from the triplicate analysis, and the x axis is the peptide number
(sorted by descending, average intensity). The three most intense peptides for each protein are highlighted in blue. The average signal response
for the three most intense peptides was calculated from the three tryptic peptides and is shown in A, B, C, D, E, and F for each of the
corresponding proteins, respectively. Three ionization efficiency tiers are labeled for yeast enolase (red bar).
urements with a coefficient of variation (Cv) of less than 30% structural identification of that peptide and its corresponding
for each peptide and an average Cv of less than 15% for all parent protein. From this analysis, the MS data from the
characterized peptides. A summary of the replicate analysis of tryptic peptides of a protein and the associated sequence
one of the protein standards, hemoglobin, can be seen in information from the MSE data for each peptide produce a
Table II. The 14 characterized peptides to hemoglobin pro- comprehensive analysis for each protein present in the mix-
vided 91% protein sequence coverage. The replicate anal- ture. Fig. 1A illustrates those peptides identified to hemo-
yses typically produced mass measurements with a precision globin (14 kDa) found in each of the six dilutions. An interest-
of less than 5 ppm (RSD). An average mass accuracy error of ing observation obtained from the comprehensive coverage
4.7 ppm (root mean square) was achieved for all of the pep- of hemoglobin shows that the pattern of intensity measure-
tides to hemoglobin. Similar levels of analytical reproduc- ments of the characterized peptides remains preserved
ibility (mass, retention time, and signal response) were ob- throughout the dilution series. As hemoglobin decreases in
served for the tryptic peptides from the other proteins. concentration, the total number of observed tryptic peptides
Due to the parallel mode of data acquisition, all tryptic and their corresponding signal responses decrease in a pre-
peptides that are of sufficient intensity will produce associ- dictable fashion while maintaining a consistent relative inten-
ated product ion fragmentation data that can be used for sity pattern for the remaining tryptic peptides. Fig. 1B illus-
16.7%, and 1.5%, respectively. The median relative standard deviation of the mass, intensity, and retention time measurements across the
triplicate analyses were 0.9 ppm, 11.7%, and 1.1%, respectively. The absolute quantity of phosphorylase B (B) loaded onto the analytical
column is indicated by the following color coding: red, 120 fmol; dark blue, 300 fmol; yellow, 600 fmol; black, 1200 fmol; green, 3000 fmol; and
white, 6000 fmol. Similar statistics were observed for phosphorylase B.
Molecular & Cellular Proteomics 5.1 149
7. Absolute Quantification of Proteins
TABLE III
Summary of the absolute quantification results obtained from the analysis of the six-protein mixture described in Table I
Alcohol dehydrogenase (bold) was used as the internal reference standard in both studies as discussed in the text.
Average SR of
Relative ratio Protein Theoretical Calculated Error SR/pmol
top three peptides
pmol pmol %
1.47 395,716 Enolase 15.0 14.7 2.2 26,381
1.25 337,505 Serum albumin 12.5 12.5 0.1 27,000
1.00 269,861 Alcohol dehydrogenase 10.0 10.0 0.0 26,986
0.60 161,116 Phosphorylase B 6.0 6.0 0.5 26,853
0.48 129,280 Hemoglobin ( ) 5.0 4.8 4.2 25,856
0.44 118,244 Hemoglobin ( ) 5.0 4.4 12.4 23,649
269,861 Normalization Average SR/pmol 26,121
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Cv 4.9
trates those peptides identified to phosphorylase B (97 kDa) from alcohol dehydrogenase. From these results we observed
found in each of the six dilutions, showing a similar behavior that the average MS signal response for the three most in-
throughout the dilution series. The identified peptides to the tense tryptic fragments is constant per unit quantity of protein.
other proteins in the sample also illustrate the same From these observations, we propose that the average signal
phenomenon. response for the three most intense tryptic peptides can be
The characterized tryptic peptides from each protein in used to estimate the absolute quantity of other well charac-
Sample 1 (Table I) were sorted by descending intensity as terized protein within the same mixture.
shown in Fig. 2 (A–F) to illustrate the observed signal re- The six serial dilutions were analyzed by LCMSE and sub-
sponse for all detected tryptic peptides to each protein. The sequently processed using PLGS version 2.2. The relationship
bar plot illustrates the varying signal responses obtained from between the absolute quantity of protein loaded onto the
the observed tryptic peptides to each protein. The compre- column and the average signal response of the three most
hensive analysis of the protein samples obtained from the intense tryptic peptides from each of the six proteins was
parallel MS acquisition provided the ability to determine a used to generate the results illustrated in Fig. 3. The data
relationship between the observed signal response of the points from the dilution series of the proteins were determined
three most intense peptides of a protein and the absolute to lie on a linear response curve ranging from 100 to 15,000
protein concentration. Alternative LCMS methods, such as fmol (x axis). The average signal response of the proteins
data-dependent analysis or even targeted MS/MS, would only extends up to 400,000 counts (y axis). A linear curve fit to
provide partial sampling of this sample and would therefore the data produces an R2 value of 0.9939. These results
not provide the comprehensive inventory of peptides required prove that the average MS signal response of the three most
to perform this quantitative analysis. The details of this rela- intense tryptic peptides is constant for all the proteins.
tionship have been summarized in Table III. The average Because the response curve is independent of the protein,
signal response of the three most intense tryptic peptides was it can therefore be used as a universal means to obtain an
determined for each protein. The relationship between the absolute quantity of any other well characterized protein
average MS signal response of the three most intense tryptic present in the mixture.
peptides and the absolute quantity of protein can be imme- Although six proteins represent a small sample size, the
diately inferred from the relative ratio of the average MS signal response curve illustrated in Fig. 3 illustrates a linear correla-
responses. The relative ratios of the average MS signal re- tion between the three most intense tryptic peptides of each
sponses are proportional to the absolute quantities of each protein and its corresponding absolute concentration. Taken
protein present in the sample. An average signal response of together with the results obtained later from both human
26,121 counts was consistently associated to 1 pmol of pro- serum proteins (Fig. 4) and E. coli proteins (Fig. 5), the data
tein on column with a Cv of 4.9%. Because the proteins support the foundation of this hypothesis. The information
spanned a wide range of molecular masses (14 –97 kDa), the provided by the sequences of the three most intense tryptic
relationship appears to be independent of the protein molec- peptides in these studies and those obtained in future studies
ular mass. Alcohol dehydrogenase was treated as the internal will further our understanding of this phenomenon. Future
standard protein for this analysis. The universal signal re- studies with well defined protein complexes will help identify
sponse factor (counts/mol, SR/pmol) was determined from proteins that do not fit the proposed model. A thorough anal-
the three most intense tryptic peptides to alcohol dehydro- ysis of the highest and lowest ionizing peptides may help
genase. The absolute quantity of the remaining proteins was define possible exceptions. It is our intent to include an ex-
calculated using the normalized signal response obtained planation of the foundation of the absolute quantification
150 Molecular & Cellular Proteomics 5.1
8. Absolute Quantification of Proteins
FIG. 3. Universal signal-response
curve for the absolute quantification
of the standard proteins. The average
signal response for the three most in-
tense tryptic peptides to each of the six
proteins was obtained for all proteins
found in each of the six samples. A sin-
gle scatter plot of the average signal re-
sponse (y axis) and the corresponding
protein concentration (x axis) was pro-
duced for all the proteins in all six sam-
ples and found to be linear for over 2
orders of magnitude. The data from each
of the proteins are color-coded as fol-
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lows: red, yeast alcohol dehydrogenase;
dark blue, bovine serum albumin; yellow,
enolase; black, bovine hemoglobin;
light blue, bovine hemoglobin; green,
bovine / hemoglobin; and gray, rabbit
phosphorylase B. A linear curve fit was
calculated for the entire data set (y
27.6 (X) 7401, R2 0.9939).
method and address possible exceptions to the method in a identified, and the corresponding average signal responses
manuscript in progress.2 A list of the three most intense were calculated. These results are outlined in Table IV and
tryptic peptides from a subset of proteins identified in this were found to be similar to the results obtained from the
study have been provided in Supplemental Table 1. analysis of the simple protein mixtures. These results again
It is worth highlighting at this point that because the quan- showed that the relative ratio of the average signal response
titation relies on correctly identifying the top three most in- for the three most intense tryptic peptides was consistent with
tense tryptic peptides, larger errors will occur with smaller the relative ratio of the absolute quantity of the individual
proteins. The results illustrated in Figs. 1 and 2 demonstrate proteins with the complex protein mixture. Although there was
this dependence. The magnitude of the error is dependent on approximately a 20% decrease in the resulting signal re-
the size of the protein because there are fewer peptides to sponse factor (counts/pmol), the results are internally consist-
choose from within the highest intensity region. Smaller pro- ent within the same dataset. The variability (Cv) of the nor-
teins will have fewer tryptic peptides that may have a wide malized signal response per picomole increased slightly from
range from the most intense to the next most intense tryptic 4.9 to 8.4% when obtained from the more complex sample.
peptide. With larger proteins there are many more tryptic The error associated with the absolute quantification of the six
peptides of higher intensity so that if one (or more) of the three standard proteins increased slightly as well. These results
most intense tryptic peptides is not accurately identified, sev- show that the determination of the absolute concentration of
eral other peptides will be found close to the same intensity, the six proteins was not affected by the additional complexity
keeping the altered average intensity value close to the true of the digested serum protein sample matrix and that the
average intensity value of the three most intense tryptic quantification method is capable of determining the absolute
peptides. concentration of all properly characterized proteins present in
Analysis of the Standard Six-protein Mixture in Human Se- a complex sample.
rum—A second series of samples contained identical concen- Absolute Quantification of Serum Proteins—The signal re-
trations of the standard digest with each sample containing sponse of 20,597 counts/pmol (Table IV) was used to deter-
equivalent amounts of human serum ( 8.75 g of serum mine the absolute concentration of 11 identified serum pro-
protein in a 5- l injection). These complex protein mixtures teins from the average intensity of the three most intensely
were analyzed by LCMSE and processed with PLGS as before ionizing tryptic peptides. The concentration of the 11 proteins
to obtain corresponding protein identifications. The three was determined from a single serum sample to illustrate the
most intense peptides to each of the six spiked proteins were variability associated with the analytical method (Fig. 4A). The
11 proteins were a subset of the 46 well characterized pro-
2
M. V. Gorenstein J. C. Silva, G. F. Li, and S. J. Geromanos, teins described by Anderson and Anderson (18) and Anderson
manuscript in preparation. et al. (19). The results obtained from the replicate analysis of
Molecular & Cellular Proteomics 5.1 151
9. Absolute Quantification of Proteins
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FIG. 4. Absolute quantity of human serum proteins from analytical (A) and biological (B) replicates. The absolute quantity of 11 well
characterized human serum proteins was calculated using the described method to produce the following average concentration measure-
ments (log10(pg/ml), blue circle). The average concentration values (red circle) for the human serum proteins were obtained from Specialty
Laboratories and were plotted along with their expected minimum and maximum values (red whiskers). The absolute quantities obtained from
152 Molecular & Cellular Proteomics 5.1
10. Absolute Quantification of Proteins
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FIG. 5. Relative levels of estimated absolute protein abundance within a single sample of E. coli. The absolute quantity of protein was
determined for a subset of E. coli proteins known to exist as multiprotein complexes. A relative ratio was calculated for each of the proteins
listed in the following set of protein complexes (GroEL-GroES, ribosomes, and SucC-SucD). GroES, RS2, and SucD were used as the reference
(or normalization) proteins for each of the protein complexes, respectively.
TABLE IV
Summary of the absolute quantification results obtained from the analysis of the six-protein mixture described in Table I in human serum
Alcohol dehydrogenase (bold) was used as the internal reference standard in both studies as discussed in the text.
Relative ratio Average SR of top three peptides Protein Theoretical Calculated Error SR/pmol
pmol pmol %
1.36 287,764 Enolase 15.0 13.6 9.3 19,184
1.29 273,241 Serum albumin 12.5 12.9 3.3 21,859
1.00 211,572 Alcohol dehydrogenase 10.0 10.0 0.0 21,157
0.65 137,933 Phosphorylase B 6.0 6.5 8.7 22,989
0.47 100,208 Hemoglobin ( ) 5.0 4.7 5.3 20,042
0.43 91,745 Hemoglobin ( ) 5.0 4.3 13.3 18,349
211,572 Normalization Average SR/pmol 20,597
Cv 8.4
the analytical replicates were typically less than 15%. Because the y axis is presented as a log10 scale, the analytical error is not shown. The
average (blue circle), minimum, and maximum concentration values obtained from the analysis of the biological replicates are indicated (blue
whiskers) in B.
Molecular & Cellular Proteomics 5.1 153
11. Absolute Quantification of Proteins
TABLE V
Absolute quantification of human serum proteins
log10
Protein kDa Intensitya pmolb ngc g/ ld pmol/mle pg/ml
(pg/ml)
Albumin 70.0 1,896,530 92.08 6,445.46 51.56 736,624 51,563,664,611 10.7
2-Macroglobulin 163.0 66,801 3.24 528.65 4.23 25,946 4,229,184,056 9.6
Transferrin 77.0 113,485 5.51 424.25 3.39 44,078 3,394,026,315 9.5
C3 complement 187.0 35,593 1.73 323.15 2.59 13,825 2,585,188,523 9.4
Apolipoprotein A-I 31.0 146,274 7.10 220.15 1.76 56,814 1,761,225,033 9.2
IgG total 36.0 107,714 5.23 188.27 1.51 41,837 1,506,123,804 9.2
Haptoglobin 38.0 77,994 3.79 143.89 1.15 30,293 1,151,147,060 9.1
1-Antitrypsin 47.0 33,501 1.63 76.45 0.61 13,012 611,563,626 8.8
Ceruloplasmin 122.0 10,789 0.52 63.91 0.51 4,191 511,242,608 8.7
IgM total 49.5 25,064 1.22 60.24 0.48 9,735 481,882,993 8.7
1-Acid glycoprotein 23.5 38,420 1.87 43.84 0.35 14,923 350,680,196 8.5
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a
Average signal response of the three most intense tryptic peptides.
b
The calculated mole quantity of protein loaded onto the analytical column (pmol).
c
The calculated mass quantity of protein loaded onto the analytical column (ng).
d
The calculated concentration of protein in the original sample ( g/ml).
e
The calculated concentration of protein in the original sample (pmol/ml).
the single serum sample are outlined in Table V. The analytical biological variability from a much larger population. The ma-
variability associated with these measurements was typically jority of the concentration values calculated on the basis of
within a relative error of 15%. These errors were consistent the LCMSE results for the identified proteins in Fig. 4B were
with what has been described previously using this method found to lie within the expected concentration ranges, provid-
(8). The results in Table V indicate that albumin and the ing further validation to the absolute quantification method
immunoglobulins account for the majority of the mass of the (18, 19).3 Accumulation of data from more samples would
total protein loaded onto the column ( 71%) for LCMS anal- provide a better correlation with the literature values due to
ysis. This value is similar to the mass fraction for these pro- the improved sampling statistics.
teins as determined from the concentration values provided The absolute quantification method outlined in this work
by Specialty Laboratories ( 67%). The absolute concentra- provides a means to carry out a mass balance analysis as a
tions were found to be close to the expected concentration useful accounting mechanism that can be applied to the
values for a number of the characterized serum proteins with inventory of peptides from any given LCMSE analysis. The
four proteins outside the typical range as indicated by Spe- total amount of protein present in human serum is 60 – 80
cialty Laboratories.3 This variability can be attributed to the mg/ml. Using an estimated concentration of 70 mg/ml of total
lack of proper sampling statistics from a single sample of serum protein, a 5- l sample of human serum would contain
human serum. 350 g of total protein. According to the digestion protocol
Another study was performed using serum from six individ- described under “Experimental Procedures,” the 350 g of
uals to determine the quantity of endogenous serum proteins total protein was digested with trypsin in a final volume of 200
within this small sample set. The concentration of the 11 l to produce a digested protein solution containing 1.75
proteins was determined from seven different serum samples g/ l. A total of 5 l of digest was loaded onto the chroma-
to demonstrate the observed biological variability of these tography column ( 8.75 g of total digested protein). The
proteins in human serum (Fig. 4B). A plot of the calculated results from the absolute quantification accounts for 10.0
concentrations, log10(pg/ml), of 11 well characterized human g of protein digest from the 11 identified proteins as indi-
serum proteins is illustrated in Fig. 4B along with the typically cated in Table V; this is in good agreement with the theoretical
observed concentration values available from Specialty Lab- value.
oratories.3 Fig. 4B illustrates the range of protein concentra- Absolute Quantification of E. coli Proteins—Having the abil-
tion calculated from the six different serum samples and ity to determine absolute quantification of a protein allows one
reflects the associated errors inherent to the analytical to determine the stoichiometric relationship of proteins within
method and to the biological variability. These results better the same sample. To explore this possibility a yeast enolase
illustrate the correlation between the literature values ob- protein digest of known concentration was spiked into the
tained from Specialty Laboratories, which incorporate the whole cell lysate of E. coli. The sample was analyzed in trip-
licate using the LCMS method described above. The average
3
Directory of Services and Use and Interpretation of Tests, Spe- intensity value of the top three ionizing peptides to yeast
cialty Laboratories, Santa Monica, CA (www.specialtylabs.com/ enolase was used to convert the average intensity of the top
default.htm). three ionizing peptides for a number of well characterized
154 Molecular & Cellular Proteomics 5.1
12. Absolute Quantification of Proteins
E. coli proteins to the corresponding absolute quantity of * The costs of publication of this article were defrayed in part by the
protein loaded on column. The relative levels of the estimated payment of page charges. This article must therefore be hereby
marked “advertisement” in accordance with 18 U.S.C. Section 1734
absolute concentrations of a number of these proteins were
solely to indicate this fact.
found to be consistent with known quaternary structural in- □ The on-line version of this article (available at http://www.
S
formation of these proteins. The results obtained from this mcponline.org) contains supplemental material.
quantitative assessment are outlined in Fig. 5. A number of § To whom correspondence should be addressed: Waters Corp.,
identified ribosomal proteins were found to exist at the same 34 Maple St., Milford, MA 01757-3696. Tel.: 508-482-3005; Fax:
508-482-2055; E-mail: jeff_silva@waters.com.
relative abundance (1:1), consistent with the structure of the
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156 Molecular & Cellular Proteomics 5.1