The document summarizes the quantitative proteomics workflow developed by the Manchester Centre for Integrative Systems Biology to develop a quantitative kinetic model of yeast metabolism. The workflow involves purifying enzymes in vitro to determine kinetic parameters, using QconCAT and LC-MS to determine absolute protein concentrations, selecting pathways from a genome-scale yeast model, acquiring LC-MSMS data and adding metadata, selecting signature peptides, parameterizing the kinetic model with concentration data, and allowing data querying and browsing.
Mascot is a software package from Matrix Science that interprets mass spectral data into protein identities.
In this presentation we will study about MASCOT and also on how to use it.
Mass Spectrometry-Based Proteomics Quantification: iTRAQ Creative Proteomics
For more information, please visit: https://www.creative-proteomics.com/services/itraq-based-proteomics-analysis.htm
iTRAQ (isobaric tag for relative and absolute quantitation), is an isobaric labeling method to determine the amount of proteins from different sources in just one single experiment by mass spectrometry, which was developed by Applied Biosystems Incorporation in 2004.
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.
Mascot is a software package from Matrix Science that interprets mass spectral data into protein identities.
In this presentation we will study about MASCOT and also on how to use it.
Mass Spectrometry-Based Proteomics Quantification: iTRAQ Creative Proteomics
For more information, please visit: https://www.creative-proteomics.com/services/itraq-based-proteomics-analysis.htm
iTRAQ (isobaric tag for relative and absolute quantitation), is an isobaric labeling method to determine the amount of proteins from different sources in just one single experiment by mass spectrometry, which was developed by Applied Biosystems Incorporation in 2004.
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.
Presentation for Network Biology SIG 2013 by Thomas Kelder, Bioinformatics Scientist at TNO in The Netherlands. “Functional Network Signatures Link Anti-diabetic Interventions with Disease Parameters”
Peptide mass fingerprinting is a technology to identify proteins. It is a high throughput protein identification technique in which the mass of an unknown protein can be determined. PMF is always performed with MALDI-TOF mass spectrometry
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.
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.
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
Proteomics studies play an increasing role in the field of biology. The use of mass spectrometry (MS) in combination with a range of separation methods is the main principal methodology for proteomics. The two principal approaches to identifying and characterizing proteins using MS are the “bottom-up”, which analyze peptides by proteolytic digestion, and “top-down”, which analyze intact proteins.
Presentation for Network Biology SIG 2013 by Thomas Kelder, Bioinformatics Scientist at TNO in The Netherlands. “Functional Network Signatures Link Anti-diabetic Interventions with Disease Parameters”
Peptide mass fingerprinting is a technology to identify proteins. It is a high throughput protein identification technique in which the mass of an unknown protein can be determined. PMF is always performed with MALDI-TOF mass spectrometry
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.
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.
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
Proteomics studies play an increasing role in the field of biology. The use of mass spectrometry (MS) in combination with a range of separation methods is the main principal methodology for proteomics. The two principal approaches to identifying and characterizing proteins using MS are the “bottom-up”, which analyze peptides by proteolytic digestion, and “top-down”, which analyze intact proteins.
Harnessing The Proteome With Proteo Iq Quantitative Proteomics Softwarejatwood3
Learn how successful researchers are using ProteoIQ to streamline their proteomic data analysis.
Centralize data analysis on a single software platform
Most laboratories have multiple MS platforms with different software packages. ProteoIQ simplifies data analysis as a vendor independent software platform supporting qualitative and quantitative analysis.
Learn how to achieve robust peptide and protein quantification
ProteoIQ is the only commercial software platform supporting all popular forms of quantification. Learn how ProteoIQ performs protein and peptide quantification using isobaric tags, isotopic labels and label free methods including intensity based peptide profiling.
Elucidate biological significance
Learn how to integrate biological databases with ProteoIQ. Quickly move from MS results to the discovery of novel biological insights through an integrated biological annotation pipeline.
Protein qualitative analysis based on mass spectrometry explores protein expression within organisms. Mass spectrometry offers highly efficient, robust, and accurate results and is one of the core technologies for proteomic research. Protein identification is a common topic for biochemistry research, and mass spectrometry is considered one of the most useful techniques that solve this issue. Two major strategies that are widely used for protein identification by mass spectrometry are MALDI-TOF-based protein fingerprinting and LC-MS/MS-based peptide sequencing. Meanwhile, LC-MS/MS reserved higher sensitivity and ability than MALDl-TOF and can accurately identify multiple protein components from a single sample. https://www.creative-proteomics.com/services/protein-identification.htm
MULISA : A New Strategy for Discovery of Protein Functional Motifs and Residuescsandit
To predict and identify details regarding function
from protein sequences is an emergency task
since the growing number and diversity of protein s
equence. Here, we develop a novel approach
for identifying conservation residues and motifs of
ligand-binding proteins. In this method,
called MuLiSA (Multiple Ligand-bound Structure Alig
nment), we first superimpose the ligands
of ligand-binding proteins and then the residues of
ligand-binding sites are naturally aligned.
We identify important residues and patterns based o
n the z-scores of the residue entropy and
residue-segment entropy. After identifying new patt
ern candidates, the profiles of patterns are
generated to predict the protein function from only
protein sequences. We tested our approach
on ATP-binding proteins and HEM-binding proteins. T
he experiments show that MuLiSA can
identify the conservation residues and novel patter
ns which are really correlated with protein
functions of certain ligand-binding proteins. We fo
und that our MuLiSA can identify
conservation patterns and is better than traditiona
l alignments such as CE and CLUSTALW in
some ligand-binding proteins. We believe that our M
uLiSA is useful to discover ligand-binding
specificity-determining residues and functional imp
ortant patterns of proteins.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
A NEW TECHNIQUE INVOLVING DATA MINING IN PROTEIN SEQUENCE CLASSIFICATIONcscpconf
Feature selection is more accurate technique in protein sequence classification. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP, Rough Set Classifier etc for extracting features.This paper presents a review is with
three different classification models such as fuzzy ARTMAP model, neural network model and Rough set classifier model.This is followed by a new technique for classifying protein
sequences.The proposed model is typically implemented with an own designed tool using JAVA and tries to prove that it reduce the computational overheads encountered by earlier
approaches and also increase the accuracy of classification.
Delineation of techniques to implement on the enhanced proposed model using d...ijdms
In post genomic era with the advent of new technologies a huge amount of complex molecular data are
generated with high throughput. The management of this biological data is definitely a challenging task
due to complexity and heterogeneity of data for discovering new knowledge. Issues like managing noisy
and incomplete data are needed to be dealt with. Use of data mining in biological domain has made its
inventory success. Discovering new knowledge from the biological data is a major challenge in data
mining technique. The novelty of the proposed model is its combined use of intelligent techniques to classify
the protein sequence faster and efficiently. Use of FFT, fuzzy classifier, String weighted algorithm, gram
encoding method, neural network model and rough set classifier in a single model and in an appropriate
place can enhance the quality of the classification system .Thus the primary challenge is to identify and
classify the large protein sequences in a very fast and easy but intellectual way to decrease the time
complexity and space complexity.
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...Ellen Berg
Predictive Models for Mechanism of Action Classification from Phenotypic Assay Data – Application to Phenotypic Drug Discovery
Presentation at SLAS 2014 conference in San Diego, 21 January 2014
EnrichNet: Graph-based statistic and web-application for gene/protein set enr...Enrico Glaab
EnrichNet is a web-application and web-service to identify and visualize functional associations between a user-defined list of genes/proteins and known cellular pathways. As a complement to classical overlap-based enrichment analysis methods, the EnrichNet approach integrates a novel graph-based statistic with a new interactive visualization of network sub-structures to enable a direct molecular interpretation of how a set of genes or proteins is related to a specific cellular pathway. Available at: http://www.enrichnet.org
An Integrative System For Prediction Of Nac Proteins In Rice Using Different ...ijsc
The NAC gene family encodes a large family of plant-specific transcription factors with diverse roles in various developmental processes and stress responses in plants. Creation of genome wide prediction tools for NAC proteins will have a significant impact on gene annotation in rice. In the present study, NACSVM, a tool for computational genome-scale prediction of NAC proteins in rice was developed integrating compositional and evolutionary information of NAC proteins. Initially, support vector machine (SVM)- based modules were developed using combinatorial presence of diverse protein features such as traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Later, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, through which an overall accuracy of 83% and 79% was achieved. NACSVM was also evaluated using position-specific iterated – basic local alignment search tool which resulted in a lower accuracy of 50%. In order to benchmark NACSVM , the tool was evaluated using independent data test and cross validation methods. The different statistical analyses carried out revealed that the proposed algorithm is an useful tool for annotating NAC proteins in genome of rice.
AN INTEGRATIVE SYSTEM FOR PREDICTION OF NAC PROTEINS IN RICE USING DIFFERENT ...ijsc
The NAC gene family encodes a large family of plant-specific transcription factors with diverse roles in
various developmental processes and stress responses in plants. Creation of genome wide prediction tools
for NAC proteins will have a significant impact on gene annotation in rice. In the present study, NACSVM,
a tool for computational genome-scale prediction of NAC proteins in rice was developed integrating
compositional and evolutionary information of NAC proteins. Initially, support vector machine (SVM)-
based modules were developed using combinatorial presence of diverse protein features such as
traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall
accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Later, two hybrid modules were
developed based on amino acid, dipeptide and tripeptide composition, through which an overall accuracy
of 83% and 79% was achieved. NACSVM was also evaluated using position-specific iterated – basic local
alignment search tool which resulted in a lower accuracy of 50%. In order to benchmark NACSVM ,
the
tool was evaluated using independent data test and cross validation methods. The different statistical
analyses carried out revealed that the proposed algorithm is an useful tool for annotating NAC proteins in
genome of rice.
A novel optimized deep learning method for protein-protein prediction in bioi...IJECEIAES
Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence- dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
Network cheminformatics: gap filling and identifying new reactions in metabol...Neil Swainston
The number of published metabolic network reconstructions are increasing, as are their applications. However, such reconstructions commonly include gaps (see Figure 1), which are due to incomplete source databases or holes in biochemical knowledge reported in literature. The filling of such gaps has been aided through automated techniques which attempt to mitigate these gaps by adding reactions from external resources such as KEGG.
The approach introduced here is to apply cheminformatics to determine and quantify chemical similarity across all metabolites in a metabolic network of S. cerevisiae. The hypothesis is that those metabolite pairs of high chemical similarity are likely to form reaction pairs, in which one metabolite can be converted to the other by a single chemical reaction. The similar scoring pairs that do not currently form a reaction pair in the network can be analysed, by either comparison with existing data resources or by literature searches, to determine whether they take part in a metabolic reaction.
Following this approach, preliminary results have led to the discovery of missing information from KEGG, and the assignment of function and determination of kinetic constants to a gene of previously unknown function.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
Quantitative Proteomics: From Instrument To Browser
1. Quantitative proteomics: from instrument to browser Neil Swainston, Daniel Jameson, Kathleen Carroll, Catherine Winder, Pedro Mendes Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester M1 7ND, UK This work has been supported by the BBSRC/EPSRC grant: the Manchester Centre for Integrative Systems Biology 1 Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry. Teusink B, et al. Eur J Biochem. (2000) 267 (17):5313-29. 2 Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes. Pratt JM, et al. Nat Protoc. (2006) 1 (2):1029-43. 3 A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Herrgård MJ, et al. Nat Biotechnol. (2008) 26 (10):1155-60. 4 PRIDE Converter: making proteomics data-sharing easy. Barsnes H, et al. Nat Biotechnol. (2009) 27 (7):598-9. Introduction The Manchester Centre for Integrative Systems Biology is following a bottom-up systems biology approach to develop a quantitative, kinetic model of yeast metabolism. In contrast to previous approaches 1 in which enzyme kinetic assays were performed on cell lysate to determine v max parameters, we are following an approach in which assays are performed in vitro on known concentrations of purified enzymes to determine k cat values. By combining this approach with absolute protein concentrations , we separate kinetic parameters from concentration variables , allowing us to determine the influence of isoenzymes and fluctuating enzyme concentrations on the system (such as those caused by gene expression). Determination of absolute enzyme concentrations is performed using LC-MS and the QconCAT 2 approach, in which known concentrations of labelled signature peptides are spiked into the sample, allowing absolute quantitation to be performed by determination of relative peak intensities. An informatics workflow has been developed to support the full cycle of work from labelled peptide selection, to identification, quantitation and ultimately data browsing and model parameterisation. Modelling A genome-scale model of yeast metabolism 3 is used to select individual pathways to be studied. As the model is fully annotated according to the MIRIAM 4 specification, enzymes of interest can be easily extracted as UniProt terms. Data acquisition Labelled peptides are spiked into the sample and data acquired by LC- MSMS. Any instrument may be used provided that data can be exported in a common, vendor-independent format (e.g. mzData, mzXML). Metadata capture with PRIDE Converter The EBI-developed tool 4 is used to allow the addition of metadata to the data; providing information on sample conditions and instrument acquisition parameters in the standard PRIDE XML format. Peptide selection with PepSelecta Signature peptides must be found for each protein to be quantified. PepSelecta has been developed to Automate the process of finding suitable signature peptides for a given set of UniProt terms. Model parameterisation with Taverna A web service has been developed allowing protein concentrations to be extracted from the PRIDE XML database. Taverna 7 workflows can be written to query the database and parameterise the SBML model. Data querying and browsing A queryable web interface has been developed on an XML database, allowing the identifications and quantitations, along with spectra and chromatograms to be queried and viewed. Identification and quantitation The Pride Wizard 5 was extended for QconCAT analyses. Spectra are submitted to Mascot 6 , with labelled peptides identified and used for automated quantitation of analyte Peptides by peak area comparison. References 5 An informatic pipeline for the data capture and submission of quantitative proteomic data using iTRAQ. Siepen JA, et al. Proteome Sci. (2007) 1 ;5:4. 6 Probability-based protein identification by searching sequence databases using mass spectrometry data. Perkins DN, et al. Electrophoresis. (1999) 20 (18):3551-67. 7 Taverna: a tool for building and running workflows of services. Hull D, et al. Nucleic Acids Res. (2006) 34 :W729-32.