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Exam Code: 350-901 Developing Applications using Cisco Core Platforms and APIs (DEVCOR) techniques January 2024 by Killexams.com team

350-901 Developing Applications using Cisco Core Platforms and APIs (DEVCOR)

350-901 DEVCOR

Certifications: Cisco Certified DevNet Professional, Cisco Certified DevNet Specialist - Core

Duration: 120 minutes



This test tests your knowledge of software development and design, including:

- Using APIs

- Cisco platforms

- Application deployment and security

- Infrastructure and automation



Exam Description

The Developing Applications Using Cisco Core Platforms and APIs v1.0 (DEVCOR 350-901) test is a 120-minute test associated with the Cisco Certified DevNet Professional certification. This test tests a candidate's knowledge of software development and design including using APIs, Cisco platforms, application deployment and security, and infrastructure and automation. The course, Developing Applications Using Cisco Core Platforms and APIs, helps candidates to prepare for this exam.



20% 1.0 Software Development and Design

1.1 Describe distributed applications related to the concepts of front-end, back-end, and load balancing
1.2 Evaluate an application design considering scalability and modularity
1.3 Evaluate an application design considering high-availability and resiliency (including onpremises, hybrid, and cloud)

1.4 Evaluate an application design considering latency and rate limiting

1.5 Evaluate an application design and implementation considering maintainability

1.6 Evaluate an application design and implementation considering observability

1.7 Diagnose problems with an application given logs related to an event

1.8 Evaluate choice of database types with respect to application requirements (such as relational, document, graph, columnar, and Time Series)

1.9 Explain architectural patterns (monolithic, services oriented, microservices, and event driven)

1.10 Utilize advanced version control operations with Git

1.10.a Merge a branch

1.10.b Resolve conflicts

1.10.c git reset

1.10.d git checkout

1.10.e git revert

1.11 Explain the concepts of release packaging and dependency management

1.12 Construct a sequence diagram that includes API calls

20% 2.0 Using APIs

2.1 Implement robust REST API error handling for time outs and rate limits

2.2 Implement control flow of consumer code for unrecoverable REST API errors

2.3 Identify ways to optimize API usage through HTTP cache controls

2.4 Construct an application that consumes a REST API that supports pagination

2.5 Describe the steps in the OAuth2 three-legged authorization code grant flow

20% 3.0 Cisco Platforms

3.1 Construct API requests to implement chatops with Webex Teams API

3.2 Construct API requests to create and delete objects using Firepower device management (FDM)

3.3 Construct API requests using the Meraki platform to accomplish these tasks

3.3.a Use Meraki Dashboard APIs to enable an SSID

3.3.b Use Meraki location APIs to retrieve location data

3.4 Construct API calls to retrieve data from Intersight

3.5 Construct a Python script using the UCS APIs to provision a new UCS server given a template

3.6 Construct a Python script using the Cisco DNA center APIs to retrieve and display wireless health information

3.7 Describe the capabilities of AppDynamics when instrumenting an application

3.8 Describe steps to build a custom dashboard to present data collected from Cisco APIs

20% 4.0 Application Deployment and Security

4.1 Diagnose a CI/CD pipeline failure (such as missing dependency, incompatible versions of components, and failed tests)

4.2 Integrate an application into a prebuilt CD environment leveraging Docker and Kubernetes

4.3 Describe the benefits of continuous testing and static code analysis in a CI pipeline

4.4 Utilize Docker to containerize an application

4.5 Describe the tenets of the "12-factor app"

4.6 Describe an effective logging strategy for an application

4.7 Explain data privacy concerns related to storage and transmission of data

4.8 Identify the secret storage approach relevant to a given scenario

4.9 Configure application specific SSL certificates

4.10 Implement mitigation strategies for OWASP threats (such as XSS, CSRF, and SQL injection)

4.11 Describe how end-to-end encryption principles apply to APIs

20% 5.0 Infrastructure and Automation

5.1 Explain considerations of model-driven telemetry (including data consumption and data storage)

5.2 Utilize RESTCONF to configure a network device including interfaces, static routes, and VLANs (IOS XE only)

5.3 Construct a workflow to configure network parameters with:

5.3.a Ansible playbook

5.3.b Puppet manifest

5.4 Identify a configuration management solution to achieve technical and business requirements

5.5 Describe how to host an application on a network device (including Catalyst 9000 and Cisco IOx-enabled devices)

Developing Applications using Cisco Core Platforms and APIs (DEVCOR)
Cisco Applications techniques

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Cisco
350-901
Developing Applications using Cisco Core Platforms
and APIs (DEVCOR)
http://killexams.com/pass4sure/exam-detail/350-901
Question: 303
Which statement about microservices architecture is true?
A. Applications are written in a single unit.
B. It is a complex application composed of multiple independent parts.
C. It is often a challenge to scale individual parts.
D. A single faulty service can bring the whole application down.
Answer: B
Question: 304
Application sometimes store configuration as constants in the code, which is a violation of strict separation of
configuration from code.
Where should application configuration be stored?
A. environment variables
B. YAML files
C. Python libraries
D. Dockerfiles
E. INI files
Answer: B
Question: 305
Which two methods are API security best practices? (Choose two.)
A. Use tokens after the identity of a client has been established.
B. Use the same operating system throughout the infrastructure.
C. Use encryption and signatures to secure data.
D. Use basic auth credentials over all internal API interactions.
E. Use cloud hosting services to manage security configuration.
Answer: AC
Question: 306
DRAG DROP
Drag and drop the steps from the left into the correct sequence on the right to describe how to use Git to maintain the
current HEAD and revert back to a previous commit, while undoing all intermediate commits.
Answer:
Question: 307
Refer to the exhibit.
The cURL POST request creates an OAuth access token for authentication with FDM API requests.
What is the purpose of the file ď@token_dataĒ that cURL is handling?
A. This file is a container to log possible error responses in the request.
B. This file is given as input to store the access token received from FD
D. This file is used to send authentication related headers.
E. This file contains raw data that is needed for token authentication.
Answer: B
Question: 308
A user is receiving a 429 Too Many Requests error.
Which scheme is the server employing that causes this error?
A. rate limiting
B. time outs
C. caching
D. redirection
Answer: A
Question: 309
Which two situations are flagged by software tools designed for dependency checking in continuous integration
environments, such as OWASP? (Choose two.)
A. publicly disclosed vulnerabilities related to the included dependencies
B. mismatches in coding styles and conventions in the included dependencies
C. incompatible licenses in the included dependencies
D. test case failures introduced by bugs in the included dependencies
E. buffer overflows to occur as the result of a combination of the included dependencies
Answer: AE
Question: 310
Which two data encoding techniques are supported by gRPC? (Choose two.)
A. XML
B. JSON
C. ASCII
D. ProtoBuf
E. YAML
Answer: BE
Question: 311
An organization manages a large cloud-deployed application that employs a microservices architecture across multiple
data centers. Reports have received about application slowness. The container orchestration logs show that faults have
been raised in a variety of containers that caused them to fail and then spin up brand new instances.
Which two actions can Boost the design of the application to identify the faults? (Choose two.)
A. Automatically pull out the container that fails the most over a time period.
B. Implement a tagging methodology that follows the application execution from service to service.
C. Add logging on exception and provide immediate notification.
D. Do a write to the datastore every time there is an application failure.
E. Implement an SNMP logging system with alerts in case a network link is slow.
Answer: BC
Question: 312
DRAG DROP
Drag and drop the git commands from the left into the correct order on the right to create a feature branch
from the master and then incorporate that feature branch into the master.
Answer:
Question: 313
DRAG DROP
Refer to the exhibit.
Drag and drop parts of the URL from the left onto the item numbers on the right that match the missing sections in the
exhibit to create the appropriate RESTCONF URL to query the VLAN configuration given this YANG model. Not all
URL parts are used.
Answer:
Question: 314
DRAG DROP
Drag and drop the expressions from below onto the code to implement error handling. Not all options are used.
Answer:
Question: 315
Refer to the exhibit.
The YAML represented is using the ios_vrf module.
As part of the Ansible playbook workflow, what is the result when this task is run?
A. VRFs not defined in the host_vars file are removed from the device.
B. VRFs not defined in the host_vars file are added to the device, and any other VRFs on the device remain.
C. VRFs defined in the host_vars file are removed from the device.
D. VRFs are added to the device from the host_vars file, and any other VRFs on the device are removed.
Answer: D
Question: 316
User report that they can no longer process transactions with the online ordering application, and the logging
dashboard is displaying these messages.
Fri Jan 10 19:37:31.123 EST 2020 [FRONTEND] INFO: Incoming request to add item to cart from user 45834534858
Fri Jan 10 19:37:31 247 EST 2020 [BACKEND] INFO: Attempting to add item to cart
Fri Jan 10 19:37:31 250 EST 2020 [BACKEND] ERROR: Failed to add item: MYSQLDB ERROR: Connection
refused
What is causing the problem seen in these log messages?
A. The database server container has crashed.
B. The backend process is overwhelmed with too many transactions.
C. The backend is not authorized to commit to the database.
D. The user is not authorized to add the item to their cart.
Answer: A
Question: 317
Refer to the exhibit.
What is the output of this IOS-XE configuration program?
A. interface operational status in IPv6 addresses
B. interface administrative status in IPv4 addresses
C. interface operational status in IPv4 addresses
D. interface administrative status in IPv6 addresses
Answer: D
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Cisco Applications techniques - BingNews https://killexams.com/pass4sure/exam-detail/350-901 Search results Cisco Applications techniques - BingNews https://killexams.com/pass4sure/exam-detail/350-901 https://killexams.com/exam_list/Cisco New Report: Businesses Suffer Serious, Measurable Damage From Data Breaches No result found, try new keyword!People who own, run, or work for businesses should take note of several findings of a accurate Cisco study of the impact of data breaches. Sun, 31 Dec 2023 22:30:00 -0600 en-us text/html https://www.msn.com/ Cisco Launches New Business Performance Insight and Visibility for Modern Applications on AWS

Business Metrics for Cisco Cloud Observability Capability Enables Customers to Protect Revenue, Boost Digital Experiences and Manage Brand Reputation.

News Summary:  

  • New business metrics for Cisco Cloud Observability enable customers to significantly enhance critical business context when observing the end-to-end flow of modern applications.
  • Business metrics‚ÄĮand‚ÄĮAWS cloud services integrations enrich and expand Cisco's business transaction monitoring to allow customers to quickly connect digital experiences to business outcomes and make faster, better decisions and prioritizations.
  • Cloud service expansion, based on customer feedback, enables Cisco to unite applications and business metrics with the AWS services that impact customers within business transactions.

LAS VEGAS, Nov. 28, 2023 /PRNewswire/ -- AWS re:Invent -- Cisco CSCO today announced new business metrics in Cisco Cloud Observability. Powered by the Cisco Observability Platform to enhance business context for modern applications running on Amazon Web Services (AWS). This latest release also supports integration with AWS services and application performance monitoring (APM) correlation and provides end-to-end visibility into the performance of cloud native applications.   

Traditional application monitoring tools only provide visibility of application and infrastructure performance metrics. This leaves teams‚ÄĒ including ITOps, DevOps and SREs‚ÄĒ managing modern applications without clear sight into the relationship between application performance and critical business KPIs such as customer conversion rates and real-time impact on business revenue. ¬†

As a result, these teams are unable to make prioritizations based on business impact.‚ÄĮ¬†

Cisco's latest innovations in full-stack observability deliver teams with the‚ÄĮenhanced business context they need to manage modern applications and protect revenue, customer experiences and brand reputation, bridging the gap between business goals and IT.¬†

This new capability empowers users with:  

  • Support for multiple business metrics within a business transaction.
  • Easy identification of business transactions configured with business metrics for troubleshooting.
  • User-friendly configuration interface that enables users to preview business transaction attributes for accuracy and set up mission-critical metric alerts.
  • Advanced KPI visualization including baseline performance and a historical analysis trend line, to easily identify when business performance is abnormal.
  • Data segmentation by selected attribute values for quick visibility of customer segments being affected most.

For Cisco customers such as Royal Caribbean, these insights are critical. "With Cisco Full-Stack Observability, we've gone from reactive to proactive. Cisco Cloud Observability will allow us to visualize and correlate metrics, events, logging, and tracing (MELT) data so we can identify, triage, and troubleshoot problems at an even greater velocity," said Alice McElroy, Director, IT Operational Excellence, Royal Caribbean.  

Supporting integration with more AWS services, DevOps teams can also now observe AWS Lambda functions as an entity within Cisco Cloud Observability APM pages, helping them to understand the functions' contribution to an application, correlate their performance to overall user experience and quickly troubleshoot unexpected behavior.

"By elevating business metrics to first-class status, similar to other performance-related metrics, we enable organizations to mature their observability practice by empowering technical teams to prioritize technical issues that are aligned with business outcomes," said Ronak Desai, Senior Vice President and General Manager for Cisco AppDynamics and Full-Stack Observability.

Cisco also announced support for 10 additional AWS services that are now pre-integrated with Cisco Cloud Observability. By tying together applications, business transactions, business metrics and expanded support for AWS infrastructure services, application owners can gain deep cross-domain visibility across the full stack. 

Business metrics for Cisco Cloud Observability is now available. For more information, register for our upcoming webinar here. 

Additional Resources: 

For more information and live demos of new Cisco Full-Stack Observability innovations in AWS, re:Invent 2023 attendees can visit the Cisco booth (#680) located within the expo. 

Demos include: 

  • Observability for modern applications
  • Business risk observability for cloud native applications
  • Extending observability on the Cisco Observability Platform

Cisco product experts will be hosting live sessions in the booth theater, and meetings are available. 

About Cisco‚ÄĮ ¬†
Cisco CSCO is the worldwide technology leader that securely connects everything to make anything possible. Our purpose is to power an inclusive future for all by helping our customers reimagine their applications, power hybrid work, secure their enterprise, transform their infrastructure, and meet their sustainability goals. Discover more on The Newsroom‚ÄĮand follow us on X at @Cisco.¬†

Cisco and the Cisco logo are trademarks or registered trademarks of Cisco and/or its affiliates in the U.S. and other countries. A listing of Cisco's trademarks can be found at www.cisco.com/go/trademarks. Third-party trademarks mentioned are the property of their respective owners. The use of the word partner does not imply a partnership relationship between Cisco and any other company. 

Amazon Web Services and AWS are trademarks of Amazon.com, Inc. or its affiliates. 

SOURCE Cisco Systems, Inc.

© 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.

Mon, 27 Nov 2023 21:00:00 -0600 en text/html https://www.benzinga.com/pressreleases/23/11/n35982733/cisco-launches-new-business-performance-insight-and-visibility-for-modern-applications-on-aws
Cisco Throws Down $150 Million Challenge To Application Developers

Cisco is driving its application development community to build custom apps and integration tools on Cisco Spark by offering a pool of $150 million in financing.

Solution providers hoping to cash in on their developer skills are joining the recently launched Cisco Spark Innovation Fund, which puts up funding for partners to cover direct investments, joint development and additional enhancements and to develop support around the Spark collaboration platform.

Partners say the networking giant's investment in the channel shows that Cisco understands it can't drive innovation alone anymore.

[Related: Cisco Widens Leadership Gap Against Microsoft In Collaboration Space]

"If you're going to drive innovation, the partners have to be part of that," said Kent MacDonald, vice president of converged infrastructure and network services at Long View Systems, a Calgary, Alberta-based solution provider and Cisco Gold partner that is participating in the fund.

"To do innovation with the partners being on the sidelines doesn't scale," said MacDonald. "For me, this is very complementary to the acquisitions of technology, in that they're now investing in the acceleration of all partners to be able to keep pace with the innovation [course] Cisco is on."

Cisco Spark is an end-to-end collaboration solution allowing customers to create virtual rooms with group messaging, content sharing, video calling and desktop sharing on a single platform.

The San Jose, Calif.-based network leader created Spark for Developers in December, through which partners can build custom solutions for customers on an API-centric platform. The platform offers organizations a set of tools to extend and embed cloud collaboration services.

Partners with app development skills can now apply for financing to implement their ideas and start acquiring customers, according to Jason Goecke, general manager of the Tropo Business Unit inside Cisco's Collaboration Technology Group.

"The Spark Innovation Fund is bringing new revenue opportunities for our developers' ecosystem as well as giving them an opportunity to work with Cisco to change how teams are collaborating across the globe," said Goecke, in a video.

Rick Snyder, vice president of Cisco's Americas Partner Organization, said application development skills for Cisco channel partners are going to be "very important" moving forward.

"All the secret sauce in the cloud is going to be orchestration management and the ability to build applications that help run a digital business. … [It's] going to be really important for us in the future," said Snyder in a accurate interview with CRN.

Partners say they're either expanding or enhancing their application development practices to better align with Cisco.

"We have developers on staff, but we're expanding that team," said Chris Bottger, senior vice president of collaboration services at IVCi, a Hauppauge, N.Y.-based solution provider and Cisco Gold partner that is participating in the Spark fund. "We have been evolving our business. We saw this coming and the good thing about Cisco is they tell you where they're going and what they're doing, so you can plan your business and make sure you cross over in the right point in time."

Cisco unveiled Spark in 2014 as a messaging-centric application. The networking giant overhauled the application in late 2015 making it a complete end-to-end collaboration solution.

Cisco is now working with a variety of technology partners including Dimension Data, Verizon, West Unified Communications Services and IntelePeer to create services based on Spark. For example, Cisco says, Verizon will integrate Spark Message and Spark Meet features into its business collaboration services.

"They're encouraging all these people to develop around their platform to make Spark a much more richer -- and as [Cisco] says, 'magical' -- experience for the user," said Bottger.

Fri, 25 Mar 2016 07:54:00 -0500 text/html https://www.crn.com/news/networking/300080152/cisco-throws-down-150-million-challenge-to-application-developers
Cisco AppDynamics‚Äô ‚ÄėApp-Driven‚Äô Security Tool Puts Application Performance First

‚ÄėThe kicker here is, we‚Äėre bringing security to something [app teams] are already using and we‚Äôve made sure the impact is extremely low to the application -- basically, unnoticeable,‚Äô Cisco Security and AppDynamics tells CRN about the new Secure Application add-on to the AppDynamics‚Äô APM license.

ARTICLE TITLE HERE

AppDynamics, Cisco’s cloud application monitoring business, is unveiling a security product that combines AppDynamics’ application expertise and Cisco’s security know-how to help enterprises defend against attacks, while avoiding application slow-down.

Secure Application is an ‚Äúapplication-led‚ÄĚ security and application performance monitoring (APM) offering that comes at a time in which millions of employees and students are working and accessing applications from home. The tool can help ensure app security, without sacrificing speed or user experience, Gregg Ostrowski, AppDynamics‚Äô regional CTO, said.

‚ÄúThe user population is demanding these high-performing applications, but security is one of those pieces that has to be included. Data is being distributed all over the place, so you have to think of an app-driven security mindset as a better way of providing threat detection,‚ÄĚ Ostrowski said.

Cisco AppDynamics’ app-focused Secure Application, in collaboration with Cisco Secure, gives customers protection at the run time of the application so that IT teams can monitor and detect for vulnerabilities within the application and immediately act on that information, Ostrowski said.

[Related: Dashbase Buy Will ‚ÄėStrengthen‚Äô Cisco AppDynamics Foundation]

The product offers automatic runtime protection for visibility into an application’s behavior, access at the code level to detect dependency and configuration-level security vulnerabilities in production, security details correlated with the application topology, and improved collaboration between application and security teams, according to San Francisco-based AppDynamics.

Security and development teams are still relatively siloed, but Secure Application can help bring those two groups together to understand the context of the application and security issues, said Nils Swart, senior director of product management, Cisco Security.

‚ÄúThe reason this market hasn‚Äôt really taken off is because app teams really don‚Äôt want to onboard agents or any other technologies that don‚Äôt benefit them. The application team is a key constituent because everything you do to an application runs the risk of reducing performance or creating erratic behavior,‚ÄĚ Swart said. ‚ÄúThe kicker here is, we‚Äôre bringing security to something [app teams] are already using and we‚Äôve made sure the impact is extremely low to the application -- basically, unnoticeable.‚ÄĚ

AppDynamics has historically been framed around business outcomes. The tool will provide security teams the insights they need from a business context so they can prioritize where to troubleshoot and what to fix first, Ostrowski added.

Secure Application offers relevant information in a dashboard view to the right teams and filters out any information that doesn‚Äôt matter to the application performance, Swart said. ‚ÄúThat really gets us to faster cycle times and solving for vulnerabilities,‚ÄĚ he said.

Security and application teams are under more pressure than ever before since many employees are still working from home and are reliant on corporate application access to get their jobs done, Ostrowski said. ‚ÄúYou can see a big area where this product would help ‚Ķ As we come out of the pandemic, it‚Äôs going to be just as critical because work from home and applications are going to continue to become more prevalent,‚ÄĚ he said. ‚ÄúThe timing is very right for this to be in the market.‚ÄĚ

AppDynamics partners can choose to use the tool to manage application security on behalf of their end customers, or hand off the product to their customers for better visibility into their own environments. ‚ÄúIt‚Äôs another solution that they can use to go get in front of the problem at hand, and offer an app-centric security model to complete the overall picture,‚ÄĚ Ostrowski. It‚Äôs a benefit to partners because many solution providers are building practices around APM and AIops, he added.

Secure Application will be sold as an add-on to the AppDynamics’ APM license. The offering is currently accessible through early availability, the company said.

Cisco AppDynamics was named as a Leader in Gartner’s 2020 Magic Quadrant for Application Performance Monitoring report for the eight consecutive time.

Wed, 03 Feb 2021 23:00:00 -0600 text/html https://www.crn.com/news/networking/cisco-appdynamics-app-driven-security-tool-puts-application-performance-first
Proteomics: Principles, Techniques and Applications

What is the proteome?

Proteins are biological molecules made up of building blocks called amino acids. Proteins are essential to life, with structural, metabolic, transport, immune, signaling and regulatory functions among many other roles.1

The term ‚Äúproteome‚ÄĚ was coined by an Australian PhD student, Marc Wilkins, in a 1994 symposium held in Siena, Italy.2¬†It is a blanket term that refers to all of the proteins that an organism¬†can¬†express. Each species has its own, unique proteome.

Unlike the genome (the complete set of genes within each organism), the composition of the proteome is in a constant state of flux over time and throughout the organism.3 Therefore, when scientists refer to the proteome, they are also sometimes referring to the proteome at a given point in time (such as the embryo versus the mature organism), or to the proteome of a particular cell type or tissue within the organism.

A diagram showing the quantities of the genome, transcriptome and proteome. Figure 1: There are approximately 20,000 genes in the human genome, ~100,000 transcripts in the human transcriptome and over >1000000 proteoforms in the human proteome. Credit: Technology Networks

What is proteomics?

Proteomics is the study of the proteome ‚Äď investigating how different proteins interact with each other and the roles they play within the organism.4

Although protein expression can be inferred by studying the expression of mRNA, which is the middle man between genes and proteins, mRNA expression levels do not always correlate well with protein expression levels.1,3 Furthermore, the study of mRNA does not consider protein posttranslational modifications, cleavage, complex formation and localization, or the many variant mRNA transcripts that can be produced; all of which are key to protein function.

The first experiments that fit the label of ‚Äúproteomic‚ÄĚ studies were performed in 1975 with the development of 2D protein electrophoresis.5¬†

However, truly high-throughput identification of multiple proteins per trial only became possible with the development of mass spectrometry (MS) technology over 20 years later.6 

Since then, the sensitivity and accuracy of MS have advanced to the point where proteins can be reliably detected down to the attomolar range (1 target protein molecule per 1018 molecules),7 and various other proteomic techniques have been developed and optimized.

What are the key questions that proteomics can answer?

Broadly speaking, proteomic research provides a global view of the processes underlying healthy and diseased cellular processes at the protein level.3,4 To do this, each proteomic study typically focuses on one or more of the following aspects of a target organism’s proteome at a time to slowly build on existing knowledge:

Protein identification 

Which proteins are normally expressed in a particular cell type, tissue or organism as a whole, or which proteins are differentially expressed?

Protein quantification

Measures total (‚Äústeady-state‚ÄĚ) protein abundance, as well as investigating the rate of protein turnover (i.e., how quickly proteins cycle between being produced and undergoing degradation).

Protein localization

Where a protein is expressed and/or accumulates is just as crucial to protein function as the timing of expression, as cellular localization controls which molecular interaction partners and targets are available. 

Post-translational modifications

Post-translational modifications can affect protein activation, localization, stability, interactions and signal transduction among other protein characteristics, thereby adding a significant layer of biological complexity.

Functional proteomics

This area of proteomics is focused on identifying the biological functions of specific individual proteins, classes of proteins (e.g., kinases) or whole protein interaction networks.

Structural proteomics

Structural studies yield important insights into protein function, the ‚Äúdruggability‚ÄĚ of protein targets for drug discovery, and drug design.

Protein-protein interactions

Investigates how proteins interact with each other, which proteins interact, and when and where they interact. 

Proteomics: Techniques

Low-throughput methods:

1. Antibody-based methods

Techniques such as ELISA (enzyme-linked immunosorbent assay) and western blotting rely on the availability of antibodies targeted toward specific proteins or epitopes to identify proteins and quantify their expression levels.  

2. Gel-based methods

Two-dimensional gel electrophoresis (2DE or 2D-PAGE), the first proteomic technique developed, uses an electric current to separate proteins in a gel based on their charge (1st dimension) and mass (2nd dimension). Differential gel electrophoresis (DIGE) is a modified form of 2DE that uses different fluorescent dyes to allow the simultaneous comparison of two to three protein samples on the same gel. These gel-based methods are used to separate proteins before further analysis by e.g., mass spectrometry (MS), as well as for relative expression profiling.

3. Chromatography-based methods

Chromatography-based methods can be used to separate and purify proteins from complex biological mixtures such as cell lysates. For example, ion-exchange chromatography separates proteins based on charge, size exclusion chromatography separates proteins based on their molecular size, and affinity chromatography employs reversible interactions between specific affinity ligands and their target proteins (e.g., the use of lectins for purifying IgM and IgA molecules). These methods can be used to purify and identify proteins of interest, as well as to prepare proteins for further analysis by e.g., downstream MS. 8


High-throughput methods:

1. Analytical, functional and reverse-phase microarrays

Protein microarrays apply small amounts of trial to a ‚Äúchip‚ÄĚ for analysis (this is sometimes in the form of a glass slide with a chemically modified surface). Specific antibodies can be immobilized to the chip surface and used to capture target proteins in a complex sample. This is termed an analytical protein microarray, and these types of microarray are used to measure the expression levels and binding affinities of proteins in a sample. Functional protein microarrays are used to characterize protein functions such as protein‚ÄďRNA interactions and enzyme-substrate turnover. In a reverse-phase protein microarray, proteins from e.g., healthy vs. diseased tissues or untreated vs. treated cells are bound to the chip, and the chip is then probed with antibodies against the target proteins.¬†

A figure showing differences between forward phase and reverse phase protein microarrays.Figure 2: The differences between forward phase and reverse phase protein microarrays. Credit: Technology Networks.

2. Mass spectrometry-based proteomics

There are several ‚Äúgel-free‚ÄĚ methods for separating proteins, including isotope-coded affinity tag (ICAT), stable isotope labeling with amino acids in cell culture (SILAC) and isobaric tags for relative and absolute quantitation (iTRAQ). These approaches allow for both quantitation and comparative/differential proteomics. There are also other, less quantitative techniques such as multidimensional protein identification technology (MudPIT), which offer the advantages of being faster and simpler. Other gel-free, chromatographic techniques for protein separation include¬†gas chromatography¬†(GC) and liquid chromatography (LC).¬†8,9

Mass spectrometry workflow 

Regardless of how the protein trial is separated, the downstream MS workflow comprises three main steps:

1. The proteins/peptides are ionized by the ion source of the mass spectrometer.

2. The resulting ions are separated according to their mass to charge ratio by the mass analyze.

3. The ions are detected. 

When using gel-free techniques upstream of MS such as iTRAQ or SILAC, the samples are used directly for input into the mass spectrometer. When using gel-based techniques, the protein spots are first cut out of the gel and digested before being either separated by LC or directly analyzed by MS.


There are two main ionization sources, namely:

Matrix-assisted laser desorption/ionization (MALDI) and Electrospray ionization (ESI).Figure 3: The two main ionization sources in MS-based proteomics. Credit: Technology Networks.


Other, less common sources include chemical ionization, electron impact and glow discharge ionization.

There are four main mass analyzers:

  • Time-of-flight (TOF)
  • Ion trap
  • QuadrupoleFourier-transform ion cyclotron (FTIC)
  • Electrostatic sector and magnetic sector are two other, less commonly adopted mass analyzers.

What is tandem MS?

Peptides can be subjected to multiple rounds of fragmentation and mass analysis ‚Äď a process termed tandem-MS, MS/MS or MSn. By combining the same or different mass analyzers in tandem, such as quadrupole-TOF (Q-TOF) or triple-quadrupole (QQQ) MS, the strengths of different mass analyzers can be leveraged to further Boost the capacity for proteome-wide analysis. Simple MS setups such as MALDI-TOF are only used for peptide mass measurements, whereas tandem mass spectrometers are used to determine peptide sequences.

Top-down proteomics vs. bottom-up proteomics

In top-down proteomics, the proteins in a trial of interest are first separated before being individually characterized.1,10


With bottom-up proteomics ‚Äď also termed ‚Äúshotgun‚ÄĚ proteomics ‚Äď all the proteins in the trial are first digested into a complex mixture of peptides, and these peptides are then analyzed to identify which proteins were present in the sample.1,10

Approach

Description

Method

Top-down proteomics

Proteins in a trial of interest are first separated before being individually characterized. 
Protein separation is performed based on mass and charge with e.g., 2DE, DIGE or MS. When using 2D electrophoresis techniques, the proteins are first resolved on the gel and then individually digested into peptides that are analyzed by a mass spectrometer. When using MS directly, the undigested trial containing the whole proteins is injected into the mass spectrometer, the proteins are separated, and individual proteins are then selected for digestion and a further round of MS for analysis of the peptides.

Bottom-up proteomics, or "shotgun proteomics"

All the proteins in the trial are first digested into a complex mixture of peptides, and these peptides are then analyzed to identify which proteins were present in the sample.
Proteins are first digested, and the digested peptide mixture is fractionated and subjected to MS, frequently in an LC-MS/MS configuration. The resulting peptide sequences are compared to existing databases using automated search algorithms. These search engines match the experimentally obtained peptide spectra to the predicted spectra of proteins produced by in silico digestion (this is called ‚Äúpeptide-spectrum matching‚ÄĚ). There are several different¬†bottom-up workflows¬†possible, including data-dependent and data-independent methods, as well as hybrids of these.

Both the top-down and bottom-up approaches have their own set of advantages and disadvantages and applications to which each is more suited.10,11 For example, top-down MS is more appropriate for research on different PTMs and protein isoforms. However, it is limited by difficulties inherent in separating complex mixtures of proteins and the decreasing sensitivity of MS toward larger proteins (particularly > 50 to 70 kDa).1


In contrast, while the peptides used in bottom-up MS (~5 to 20 amino acids in length) are much easier to fractionate, ionize and fragment, this approach provides an indirect measure of the proteins originally present in samples and relies heavily on inference.1¬†A hybrid ‚Äúmiddle-down‚ÄĚ approach has been developed, which employs larger peptide fragments than conventional bottom-up proteomics, thereby potentially allowing more unique peptide matches.

A picture showing the differences between top-down and bottom-up proteomics.

Figure 4: The differences between top-down and bottom-up proteomics. Credit: Technology Networks.

Data analysis in proteomics

Proteomic studies, particularly those employing high-throughput technologies, can generate huge amounts of data.12 In addition to the sheer quantity of data produced, proteomic data analysis can also be relatively complex for certain techniques such as shotgun MS.13 Adding to this complexity is the range of bioinformatics tools available for proteomic analyses.14-17

Proteomic researchers are faced with many hurdles when attempting to optimize how they warehouse and analyze their proteomic data.12 


When planning proteomic experiments, scientists need to factor in not only the costs of the reagents and laboratory equipment but also that of data storage and analysis, and they have to appraise the level of bioinformatics skills and computational resources required.


Proteomic studies often require multiple data processing and analysis steps that need to be performed in a specific sequence.12 To address this need, researchers are increasingly assembling the needed scripts, tools and software into customized proteomic analysis pipelines suited to their particular research questions.

Applications of proteomics

The applications of proteomics are incredibly numerous and varied. The table below lists just some of these applications and provides links to examples of studies using these approaches. 

Proteomics application

Description and examples

Personalized medicine

Tailoring disease treatment to each patient based on their genetic and epigenetic makeup, so as to Boost efficacy and reduce adverse effects. While genomics and transcriptomics have been the main focus of such studies to date, proteomics data will likely add a further dimension for patient-specific management.

Biomarker discovery

Identification of protein markers e.g., for the diagnosis and prognosis of glioblastoma, and evaluating patients’ response to therapeutic interventions such as stem cell transplantation.

Drug discovery and development

Identifying potential drug targets, examining the druggability of selected protein targets, and developing drugs aimed at candidate therapeutic protein targets (e.g., for hepatocellular carcinoma).

Systems biology

System-wide investigations of disease pathways and¬†host‚Äďpathogen interactions¬†to identify potential biomarkers and therapeutic targets; system-wide investigations of¬†drug action, toxicity, resistance and efficacy.¬†

Agriculture

Investigations of¬†plant‚Äďpathogen interactions, crop engineering for increased resilience to e.g.,¬†flooding, drought and other environmental stresses.¬†

Food science

Food safety and quality control, allergen detection and improving the nutritional value of foods.

Paleoproteomics

The study of ancient proteins to further our understanding of evolution and archeology.

Astrobiology

Investigations of how mammals’ immune systems may respond to exo-microbes found in space and studies of the prebiotic organic matter found on meteorites.

The future of proteomics

Currently, proteomic workflows rely heavily on MS.1¬†As powerful as this technology has proven, researchers are now looking ahead to a future for proteomics that lies ‚Äúbeyond MS.‚ÄĚ Despite the attomolar sensitivity of MS, millions of the target molecule still need to be present in the trial for it to be detected. This implies that low-concentration target molecules (e.g., serum biomarkers) can be undetectable in complex milieu such as human serum unless first enriched for.

Scientists are still searching for the holy grail of high-throughput proteomic techniques that 1) has excellent sensitivity across the dynamic range of the target proteome (e.g., 107 for the human proteome), 2) can directly read entire protein sequences and identify their PTMs, and, therefore, 3) does not need to draw inferences from databases of theoretical protein matches.1


There are several promising technologies that, while currently hampered by limitations in sensitivity, throughput or cost, may yet come to dominate the proteomic field.1 These include nascent fluorescent fingerprinting methods and yet-to-be-developed subnanopore arrays for the high-throughput single-molecule sequencing of proteins.


Along with the expected advances in proteomic techniques, approaches to proteomic data analysis are expected to evolve just as rapidly. For example, there is a strong impetus towards developing data technologies such as cloud computing, software containers and workflow systems, which will ‚Äúdemocratize‚ÄĚ access to top-notch computing resources for proteomic data analysis regardless of researchers‚Äô location, IT infrastructure or computational expertise.12,18,19

References:

1)      Timp W, Timp G. Beyond mass spectrometry, the next step in proteomics. Sci Adv. 2020;6(2):eaax8978. doi: 10.1126/sciadv.aax8978.

2)      Wilkins M. Proteomics data mining. Expert Rev Proteomics. 2009;6(6):599-603. doi: 10.1586/epr.09.81.

3)      Beynon RJ. The dynamics of the proteome: strategies for measuring protein turnover on a proteome-wide scale. Brief Funct Genomic Proteomic. 2005;3(4):382-390. doi: 10.1093/bfgp/3.4.382.

4)      Garrels JI. Proteome. In: Brenner S, Miller JH, eds. Encyclopaedia of Genetics. London: Academic Press; 2001:1575-1578.

5)      Graves PR, Haystead TA. Molecular biologist's guide to proteomics. Microbiol Mol Biol Rev. 2002;66(1):39-63. doi: 10.1128/mmbr.66.1.39-63.2002.

6)      Andersen JS, Mann M. Functional genomics by mass spectrometry. FEBS Lett. 2000;480(1):25-31. doi: 10.1016/s0014-5793(00)01773-7.

7)      Bekker-Jensen DB, Martínez-Val A, Steigerwald S, et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol Cell Proteomics. 2020;19(4):716-729. doi: 10.1074/mcp.TIR119.001906.

8)      Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. Proteomics: Technologies and their applications. J Chromatogr Sci. 2017;55(2):182-196. doi: 10.1093/chromsci/bmw167.

9)      Chandramouli K, Qian PY. Proteomics: challenges, techniques and possibilities to overcome biological trial complexity. Hum Genomics Proteomics. 2009;2009:239204. doi: 10.4061/2009/239204.

10)   Zhang Y, Fonslow BR, Shan B, Baek MC, Yates JR 3rd. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 2013;113(4):2343-2394. doi: 10.1021/cr3003533.

11)   Zhang H, Ge Y. Comprehensive analysis of protein modifications by top-down mass spectrometry. Circ Cardiovasc Genet. 2011;4(6):711. doi: 10.1161/CIRCGENETICS.110.957829.

12)¬†¬†¬†Perez‚ÄźRiverol Y, Moreno P. Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines.¬†Proteomics. 2020;20:1900147. doi:¬†10.1002/pmic.201900147.

13)   Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics: new developments in data-independent acquisition. F1000Res. 2016;5:F1000 Faculty Rev-419. doi: 10.12688/f1000research.7042.1.

14)   Ison J, Rapacki K, Ménager H, et al. Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res. 2016;44(D1):D38-D47. doi: 10.1093/nar/gkv1116

15)   Henry VJ, Bandrowski AE, Pepin AS, Gonzalez BJ, Desfeux A. OMICtools: an informative directory for multi-omic data analysis. Database. 2014;2014:bau069. doi: 10.1093/database/bau069.

16)   Afgan E, Baker D, Batut B, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537-W544. doi: 10.1093/nar/gky379.

17)   Tsiamis V, Ienasescu H, Gabrielaitis D, Palmblad M, Schwämmle V, Ison J. One thousand and one software for proteomics: Tales of the toolmakers of science. J Proteome Res. 2019;18(10):3580-3585. doi: 10.1021/acs.jproteome.9b00219.

18)   Cole BS, Moore JH. Eleven quick tips for architecting biomedical informatics workflows with cloud computing. PLoS Comput Biol. 2018;14(3):e1005994. doi: 10.1371/journal.pcbi.1005994.

19)   Lawlor B, Sleator RD. The democratization of bioinformatics: A software engineering perspective. GigaScience. 2020;9(6):giaa063. doi: 10.1093/gigascience/giaa063.

What is the proteome?
The term ‚Äúproteome‚ÄĚ was coined by an Australian Ph.D. student, Marc Wilkins, in a 1994 symposium held in Siena, Italy. It is a blanket term that refers to all of the proteins that an organism can express. Each species has its own, unique proteome.

What is proteomics?
Proteomics is the study of the proteome‚ÄĒinvestigating how different proteins interact with each other and the roles they play within the organism.

What is tandem MS?
Peptides can be subjected to multiple rounds of fragmentation and mass analysis‚ÄĒa process termed tandem-MS, MS/MS or MSn. By combining the same or different mass analyzers in tandem, such as quadrupole-TOF (Q-TOF) or triple-quadrupole (QQQ) MS, the strengths of different mass analyzers can be leveraged to further Boost the capacity for proteome-wide analysis. Simple MS setups such as MALDI-TOF are only used for peptide mass measurements, whereas tandem mass spectrometers are used to determine peptide sequences.

Tue, 08 Dec 2020 10:00:00 -0600 en text/html https://www.technologynetworks.com/neuroscience/articles/proteomics-principles-techniques-and-applications-343804
Cisco Launches New Business Performance Insight and Visibility for Modern Applications on AWS

Business Metrics for Cisco Cloud Observability Capability Enables Customers to Protect Revenue, Boost Digital Experiences and Manage Brand Reputation.

News Summary:

  • New business metrics for Cisco Cloud Observability enable customers to significantly enhance critical business context when observing the end-to-end flow of modern applications.
  • Business metrics and AWS cloud services integrations enrich and expand Cisco's business transaction monitoring to allow customers to quickly connect digital experiences to business outcomes and make faster, better decisions and prioritizations.
  • Cloud service expansion, based on customer feedback, enables Cisco to unite applications and business metrics with the AWS services that impact customers within business transactions.

LAS VEGAS, Nov. 28, 2023 /PRNewswire/ -- AWS re:Invent -- Cisco (NASDAQ: CSCO) today announced new business metrics in Cisco Cloud Observability. Powered by the Cisco Observability Platform to enhance business context for modern applications running on Amazon Web Services (AWS). This latest release also supports integration with AWS services and application performance monitoring (APM) correlation and provides end-to-end visibility into the performance of cloud native applications.

Cisco Logo (PRNewsfoto/Cisco)

Traditional application monitoring tools only provide visibility of application and infrastructure performance metrics. This leaves teams‚ÄĒ including ITOps, DevOps and SREs‚ÄĒ managing modern applications without clear sight into the relationship between application performance and critical business KPIs such as customer conversion rates and real-time impact on business revenue.

As a result, these teams are unable to make prioritizations based on business impact.

Cisco's latest innovations in full-stack observability deliver teams with the enhanced business context they need to manage modern applications and protect revenue, customer experiences and brand reputation, bridging the gap between business goals and IT.

This new capability empowers users with:

  • Support for multiple business metrics within a business transaction.
  • Easy identification of business transactions configured with business metrics for troubleshooting.
  • User-friendly configuration interface that enables users to preview business transaction attributes for accuracy and set up mission-critical metric alerts.
  • Advanced KPI visualization including baseline performance and a historical analysis trend line, to easily identify when business performance is abnormal.
  • Data segmentation by selected attribute values for quick visibility of customer segments being affected most.

For Cisco customers such as Royal Caribbean, these insights are critical. "With Cisco Full-Stack Observability, we've gone from reactive to proactive. Cisco Cloud Observability will allow us to visualize and correlate metrics, events, logging, and tracing (MELT) data so we can identify, triage, and troubleshoot problems at an even greater velocity," said Alice McElroy, Director, IT Operational Excellence, Royal Caribbean.

Supporting integration with more AWS services, DevOps teams can also now observe AWS Lambda functions as an entity within Cisco Cloud Observability APM pages, helping them to understand the functions' contribution to an application, correlate their performance to overall user experience and quickly troubleshoot unexpected behavior.

"By elevating business metrics to first-class status, similar to other performance-related metrics, we enable organizations to mature their observability practice by empowering technical teams to prioritize technical issues that are aligned with business outcomes," said Ronak Desai, Senior Vice President and General Manager for Cisco AppDynamics and Full-Stack Observability.

Cisco also announced support for 10 additional AWS services that are now pre-integrated with Cisco Cloud Observability. By tying together applications, business transactions, business metrics and expanded support for AWS infrastructure services, application owners can gain deep cross-domain visibility across the full stack.

Business metrics for Cisco Cloud Observability is now available. For more information, register for our upcoming webinar here.

Additional Resources:

For more information and live demos of new Cisco Full-Stack Observability innovations in AWS, re:Invent 2023 attendees can visit the Cisco booth (#680) located within the expo.

Demos include:

  • Observability for modern applications
  • Business risk observability for cloud native applications
  • Extending observability on the Cisco Observability Platform

Cisco product experts will be hosting live sessions in the booth theater, and meetings are available.

About Cisco
Cisco (NASDAQ: CSCO) is the worldwide technology leader that securely connects everything to make anything possible. Our purpose is to power an inclusive future for all by helping our customers reimagine their applications, power hybrid work, secure their enterprise, transform their infrastructure, and meet their sustainability goals. Discover more on The Newsroom and follow us on X at @Cisco.

Cisco and the Cisco logo are trademarks or registered trademarks of Cisco and/or its affiliates in the U.S. and other countries. A listing of Cisco's trademarks can be found at www.cisco.com/go/trademarks. Third-party trademarks mentioned are the property of their respective owners. The use of the word partner does not imply a partnership relationship between Cisco and any other company.

Amazon Web Services and AWS are trademarks of Amazon.com, Inc. or its affiliates.

Cision View original content to get multimedia:https://www.prnewswire.com/news-releases/cisco-launches-new-business-performance-insight-and-visibility-for-modern-applications-on-aws-301998458.html

SOURCE Cisco Systems, Inc.

Mon, 27 Nov 2023 10:00:00 -0600 text/html https://stockhouse.com/news/press-releases/2023/11/28/cisco-launches-new-business-performance-insight-and-visibility-for-modern
Proteomics: Principles, Techniques and Applications

What is the proteome?

Proteins are biological molecules made up of building blocks called amino acids. Proteins are essential to life, with structural, metabolic, transport, immune, signaling and regulatory functions among many other roles.1

The term ‚Äúproteome‚ÄĚ was coined by an Australian PhD student, Marc Wilkins, in a 1994 symposium held in Siena, Italy.2¬†It is a blanket term that refers to all of the proteins that an organism¬†can¬†express. Each species has its own, unique proteome.

Unlike the genome (the complete set of genes within each organism), the composition of the proteome is in a constant state of flux over time and throughout the organism.3 Therefore, when scientists refer to the proteome, they are also sometimes referring to the proteome at a given point in time (such as the embryo versus the mature organism), or to the proteome of a particular cell type or tissue within the organism.

A diagram showing the quantities of the genome, transcriptome and proteome. Figure 1: There are approximately 20,000 genes in the human genome, ~100,000 transcripts in the human transcriptome and over >1000000 proteoforms in the human proteome. Credit: Technology Networks

What is proteomics?

Proteomics is the study of the proteome ‚Äď investigating how different proteins interact with each other and the roles they play within the organism.4

Although protein expression can be inferred by studying the expression of mRNA, which is the middle man between genes and proteins, mRNA expression levels do not always correlate well with protein expression levels.1,3 Furthermore, the study of mRNA does not consider protein posttranslational modifications, cleavage, complex formation and localization, or the many variant mRNA transcripts that can be produced; all of which are key to protein function.

The first experiments that fit the label of ‚Äúproteomic‚ÄĚ studies were performed in 1975 with the development of 2D protein electrophoresis.5¬†

However, truly high-throughput identification of multiple proteins per trial only became possible with the development of mass spectrometry (MS) technology over 20 years later.6 

Since then, the sensitivity and accuracy of MS have advanced to the point where proteins can be reliably detected down to the attomolar range (1 target protein molecule per 1018 molecules),7 and various other proteomic techniques have been developed and optimized.

What are the key questions that proteomics can answer?

Broadly speaking, proteomic research provides a global view of the processes underlying healthy and diseased cellular processes at the protein level.3,4 To do this, each proteomic study typically focuses on one or more of the following aspects of a target organism’s proteome at a time to slowly build on existing knowledge:

Protein identification 

Which proteins are normally expressed in a particular cell type, tissue or organism as a whole, or which proteins are differentially expressed?

Protein quantification

Measures total (‚Äústeady-state‚ÄĚ) protein abundance, as well as investigating the rate of protein turnover (i.e., how quickly proteins cycle between being produced and undergoing degradation).

Protein localization

Where a protein is expressed and/or accumulates is just as crucial to protein function as the timing of expression, as cellular localization controls which molecular interaction partners and targets are available. 

Post-translational modifications

Post-translational modifications can affect protein activation, localization, stability, interactions and signal transduction among other protein characteristics, thereby adding a significant layer of biological complexity.

Functional proteomics

This area of proteomics is focused on identifying the biological functions of specific individual proteins, classes of proteins (e.g., kinases) or whole protein interaction networks.

Structural proteomics

Structural studies yield important insights into protein function, the ‚Äúdruggability‚ÄĚ of protein targets for drug discovery, and drug design.

Protein-protein interactions

Investigates how proteins interact with each other, which proteins interact, and when and where they interact. 

Proteomics: Techniques

Low-throughput methods:

1. Antibody-based methods

Techniques such as ELISA (enzyme-linked immunosorbent assay) and western blotting rely on the availability of antibodies targeted toward specific proteins or epitopes to identify proteins and quantify their expression levels.  

2. Gel-based methods

Two-dimensional gel electrophoresis (2DE or 2D-PAGE), the first proteomic technique developed, uses an electric current to separate proteins in a gel based on their charge (1st dimension) and mass (2nd dimension). Differential gel electrophoresis (DIGE) is a modified form of 2DE that uses different fluorescent dyes to allow the simultaneous comparison of two to three protein samples on the same gel. These gel-based methods are used to separate proteins before further analysis by e.g., mass spectrometry (MS), as well as for relative expression profiling.

3. Chromatography-based methods

Chromatography-based methods can be used to separate and purify proteins from complex biological mixtures such as cell lysates. For example, ion-exchange chromatography separates proteins based on charge, size exclusion chromatography separates proteins based on their molecular size, and affinity chromatography employs reversible interactions between specific affinity ligands and their target proteins (e.g., the use of lectins for purifying IgM and IgA molecules). These methods can be used to purify and identify proteins of interest, as well as to prepare proteins for further analysis by e.g., downstream MS. 8


High-throughput methods:

1. Analytical, functional and reverse-phase microarrays

Protein microarrays apply small amounts of trial to a ‚Äúchip‚ÄĚ for analysis (this is sometimes in the form of a glass slide with a chemically modified surface). Specific antibodies can be immobilized to the chip surface and used to capture target proteins in a complex sample. This is termed an analytical protein microarray, and these types of microarray are used to measure the expression levels and binding affinities of proteins in a sample. Functional protein microarrays are used to characterize protein functions such as protein‚ÄďRNA interactions and enzyme-substrate turnover. In a reverse-phase protein microarray, proteins from e.g., healthy vs. diseased tissues or untreated vs. treated cells are bound to the chip, and the chip is then probed with antibodies against the target proteins.¬†

A figure showing differences between forward phase and reverse phase protein microarrays.Figure 2: The differences between forward phase and reverse phase protein microarrays. Credit: Technology Networks.

2. Mass spectrometry-based proteomics

There are several ‚Äúgel-free‚ÄĚ methods for separating proteins, including isotope-coded affinity tag (ICAT), stable isotope labeling with amino acids in cell culture (SILAC) and isobaric tags for relative and absolute quantitation (iTRAQ). These approaches allow for both quantitation and comparative/differential proteomics. There are also other, less quantitative techniques such as multidimensional protein identification technology (MudPIT), which offer the advantages of being faster and simpler. Other gel-free, chromatographic techniques for protein separation include¬†gas chromatography¬†(GC) and liquid chromatography (LC).¬†8,9

Mass spectrometry workflow 

Regardless of how the protein trial is separated, the downstream MS workflow comprises three main steps:

1. The proteins/peptides are ionized by the ion source of the mass spectrometer.

2. The resulting ions are separated according to their mass to charge ratio by the mass analyze.

3. The ions are detected. 

When using gel-free techniques upstream of MS such as iTRAQ or SILAC, the samples are used directly for input into the mass spectrometer. When using gel-based techniques, the protein spots are first cut out of the gel and digested before being either separated by LC or directly analyzed by MS.


There are two main ionization sources, namely:

Matrix-assisted laser desorption/ionization (MALDI) and Electrospray ionization (ESI).Figure 3: The two main ionization sources in MS-based proteomics. Credit: Technology Networks.


Other, less common sources include chemical ionization, electron impact and glow discharge ionization.

There are four main mass analyzers:

  • Time-of-flight (TOF)
  • Ion trap
  • QuadrupoleFourier-transform ion cyclotron (FTIC)
  • Electrostatic sector and magnetic sector are two other, less commonly adopted mass analyzers.

What is tandem MS?

Peptides can be subjected to multiple rounds of fragmentation and mass analysis ‚Äď a process termed tandem-MS, MS/MS or MSn. By combining the same or different mass analyzers in tandem, such as quadrupole-TOF (Q-TOF) or triple-quadrupole (QQQ) MS, the strengths of different mass analyzers can be leveraged to further Boost the capacity for proteome-wide analysis. Simple MS setups such as MALDI-TOF are only used for peptide mass measurements, whereas tandem mass spectrometers are used to determine peptide sequences.

Top-down proteomics vs. bottom-up proteomics

In top-down proteomics, the proteins in a trial of interest are first separated before being individually characterized.1,10


With bottom-up proteomics ‚Äď also termed ‚Äúshotgun‚ÄĚ proteomics ‚Äď all the proteins in the trial are first digested into a complex mixture of peptides, and these peptides are then analyzed to identify which proteins were present in the sample.1,10

Approach

Description

Method

Top-down proteomics

Proteins in a trial of interest are first separated before being individually characterized. 
Protein separation is performed based on mass and charge with e.g., 2DE, DIGE or MS. When using 2D electrophoresis techniques, the proteins are first resolved on the gel and then individually digested into peptides that are analyzed by a mass spectrometer. When using MS directly, the undigested trial containing the whole proteins is injected into the mass spectrometer, the proteins are separated, and individual proteins are then selected for digestion and a further round of MS for analysis of the peptides.

Bottom-up proteomics, or "shotgun proteomics"

All the proteins in the trial are first digested into a complex mixture of peptides, and these peptides are then analyzed to identify which proteins were present in the sample.
Proteins are first digested, and the digested peptide mixture is fractionated and subjected to MS, frequently in an LC-MS/MS configuration. The resulting peptide sequences are compared to existing databases using automated search algorithms. These search engines match the experimentally obtained peptide spectra to the predicted spectra of proteins produced by in silico digestion (this is called ‚Äúpeptide-spectrum matching‚ÄĚ). There are several different¬†bottom-up workflows¬†possible, including data-dependent and data-independent methods, as well as hybrids of these.

Both the top-down and bottom-up approaches have their own set of advantages and disadvantages and applications to which each is more suited.10,11 For example, top-down MS is more appropriate for research on different PTMs and protein isoforms. However, it is limited by difficulties inherent in separating complex mixtures of proteins and the decreasing sensitivity of MS toward larger proteins (particularly > 50 to 70 kDa).1


In contrast, while the peptides used in bottom-up MS (~5 to 20 amino acids in length) are much easier to fractionate, ionize and fragment, this approach provides an indirect measure of the proteins originally present in samples and relies heavily on inference.1¬†A hybrid ‚Äúmiddle-down‚ÄĚ approach has been developed, which employs larger peptide fragments than conventional bottom-up proteomics, thereby potentially allowing more unique peptide matches.

A picture showing the differences between top-down and bottom-up proteomics.

Figure 4: The differences between top-down and bottom-up proteomics. Credit: Technology Networks.

Data analysis in proteomics

Proteomic studies, particularly those employing high-throughput technologies, can generate huge amounts of data.12 In addition to the sheer quantity of data produced, proteomic data analysis can also be relatively complex for certain techniques such as shotgun MS.13 Adding to this complexity is the range of bioinformatics tools available for proteomic analyses.14-17

Proteomic researchers are faced with many hurdles when attempting to optimize how they warehouse and analyze their proteomic data.12 


When planning proteomic experiments, scientists need to factor in not only the costs of the reagents and laboratory equipment but also that of data storage and analysis, and they have to appraise the level of bioinformatics skills and computational resources required.


Proteomic studies often require multiple data processing and analysis steps that need to be performed in a specific sequence.12 To address this need, researchers are increasingly assembling the needed scripts, tools and software into customized proteomic analysis pipelines suited to their particular research questions.

Applications of proteomics

The applications of proteomics are incredibly numerous and varied. The table below lists just some of these applications and provides links to examples of studies using these approaches. 

Proteomics application

Description and examples

Personalized medicine

Tailoring disease treatment to each patient based on their genetic and epigenetic makeup, so as to Boost efficacy and reduce adverse effects. While genomics and transcriptomics have been the main focus of such studies to date, proteomics data will likely add a further dimension for patient-specific management.

Biomarker discovery

Identification of protein markers e.g., for the diagnosis and prognosis of glioblastoma, and evaluating patients’ response to therapeutic interventions such as stem cell transplantation.

Drug discovery and development

Identifying potential drug targets, examining the druggability of selected protein targets, and developing drugs aimed at candidate therapeutic protein targets (e.g., for hepatocellular carcinoma).

Systems biology

System-wide investigations of disease pathways and¬†host‚Äďpathogen interactions¬†to identify potential biomarkers and therapeutic targets; system-wide investigations of¬†drug action, toxicity, resistance and efficacy.¬†

Agriculture

Investigations of¬†plant‚Äďpathogen interactions, crop engineering for increased resilience to e.g.,¬†flooding, drought and other environmental stresses.¬†

Food science

Food safety and quality control, allergen detection and improving the nutritional value of foods.

Paleoproteomics

The study of ancient proteins to further our understanding of evolution and archeology.

Astrobiology

Investigations of how mammals’ immune systems may respond to exo-microbes found in space and studies of the prebiotic organic matter found on meteorites.

The future of proteomics

Currently, proteomic workflows rely heavily on MS.1¬†As powerful as this technology has proven, researchers are now looking ahead to a future for proteomics that lies ‚Äúbeyond MS.‚ÄĚ Despite the attomolar sensitivity of MS, millions of the target molecule still need to be present in the trial for it to be detected. This implies that low-concentration target molecules (e.g., serum biomarkers) can be undetectable in complex milieu such as human serum unless first enriched for.

Scientists are still searching for the holy grail of high-throughput proteomic techniques that 1) has excellent sensitivity across the dynamic range of the target proteome (e.g., 107 for the human proteome), 2) can directly read entire protein sequences and identify their PTMs, and, therefore, 3) does not need to draw inferences from databases of theoretical protein matches.1


There are several promising technologies that, while currently hampered by limitations in sensitivity, throughput or cost, may yet come to dominate the proteomic field.1 These include nascent fluorescent fingerprinting methods and yet-to-be-developed subnanopore arrays for the high-throughput single-molecule sequencing of proteins.


Along with the expected advances in proteomic techniques, approaches to proteomic data analysis are expected to evolve just as rapidly. For example, there is a strong impetus towards developing data technologies such as cloud computing, software containers and workflow systems, which will ‚Äúdemocratize‚ÄĚ access to top-notch computing resources for proteomic data analysis regardless of researchers‚Äô location, IT infrastructure or computational expertise.12,18,19

References:

1)      Timp W, Timp G. Beyond mass spectrometry, the next step in proteomics. Sci Adv. 2020;6(2):eaax8978. doi: 10.1126/sciadv.aax8978.

2)      Wilkins M. Proteomics data mining. Expert Rev Proteomics. 2009;6(6):599-603. doi: 10.1586/epr.09.81.

3)      Beynon RJ. The dynamics of the proteome: strategies for measuring protein turnover on a proteome-wide scale. Brief Funct Genomic Proteomic. 2005;3(4):382-390. doi: 10.1093/bfgp/3.4.382.

4)      Garrels JI. Proteome. In: Brenner S, Miller JH, eds. Encyclopaedia of Genetics. London: Academic Press; 2001:1575-1578.

5)      Graves PR, Haystead TA. Molecular biologist's guide to proteomics. Microbiol Mol Biol Rev. 2002;66(1):39-63. doi: 10.1128/mmbr.66.1.39-63.2002.

6)      Andersen JS, Mann M. Functional genomics by mass spectrometry. FEBS Lett. 2000;480(1):25-31. doi: 10.1016/s0014-5793(00)01773-7.

7)      Bekker-Jensen DB, Martínez-Val A, Steigerwald S, et al. A compact quadrupole-orbitrap mass spectrometer with FAIMS interface improves proteome coverage in short LC gradients. Mol Cell Proteomics. 2020;19(4):716-729. doi: 10.1074/mcp.TIR119.001906.

8)      Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. Proteomics: Technologies and their applications. J Chromatogr Sci. 2017;55(2):182-196. doi: 10.1093/chromsci/bmw167.

9)      Chandramouli K, Qian PY. Proteomics: challenges, techniques and possibilities to overcome biological trial complexity. Hum Genomics Proteomics. 2009;2009:239204. doi: 10.4061/2009/239204.

10)   Zhang Y, Fonslow BR, Shan B, Baek MC, Yates JR 3rd. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 2013;113(4):2343-2394. doi: 10.1021/cr3003533.

11)   Zhang H, Ge Y. Comprehensive analysis of protein modifications by top-down mass spectrometry. Circ Cardiovasc Genet. 2011;4(6):711. doi: 10.1161/CIRCGENETICS.110.957829.

12)¬†¬†¬†Perez‚ÄźRiverol Y, Moreno P. Scalable data analysis in proteomics and metabolomics using BioContainers and workflows engines.¬†Proteomics. 2020;20:1900147. doi:¬†10.1002/pmic.201900147.

13)   Hu A, Noble WS, Wolf-Yadlin A. Technical advances in proteomics: new developments in data-independent acquisition. F1000Res. 2016;5:F1000 Faculty Rev-419. doi: 10.12688/f1000research.7042.1.

14)   Ison J, Rapacki K, Ménager H, et al. Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res. 2016;44(D1):D38-D47. doi: 10.1093/nar/gkv1116

15)   Henry VJ, Bandrowski AE, Pepin AS, Gonzalez BJ, Desfeux A. OMICtools: an informative directory for multi-omic data analysis. Database. 2014;2014:bau069. doi: 10.1093/database/bau069.

16)   Afgan E, Baker D, Batut B, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537-W544. doi: 10.1093/nar/gky379.

17)   Tsiamis V, Ienasescu H, Gabrielaitis D, Palmblad M, Schwämmle V, Ison J. One thousand and one software for proteomics: Tales of the toolmakers of science. J Proteome Res. 2019;18(10):3580-3585. doi: 10.1021/acs.jproteome.9b00219.

18)   Cole BS, Moore JH. Eleven quick tips for architecting biomedical informatics workflows with cloud computing. PLoS Comput Biol. 2018;14(3):e1005994. doi: 10.1371/journal.pcbi.1005994.

19)   Lawlor B, Sleator RD. The democratization of bioinformatics: A software engineering perspective. GigaScience. 2020;9(6):giaa063. doi: 10.1093/gigascience/giaa063.

What is the proteome?
The term ‚Äúproteome‚ÄĚ was coined by an Australian Ph.D. student, Marc Wilkins, in a 1994 symposium held in Siena, Italy. It is a blanket term that refers to all of the proteins that an organism can express. Each species has its own, unique proteome.

What is proteomics?
Proteomics is the study of the proteome‚ÄĒinvestigating how different proteins interact with each other and the roles they play within the organism.

What is tandem MS?
Peptides can be subjected to multiple rounds of fragmentation and mass analysis‚ÄĒa process termed tandem-MS, MS/MS or MSn. By combining the same or different mass analyzers in tandem, such as quadrupole-TOF (Q-TOF) or triple-quadrupole (QQQ) MS, the strengths of different mass analyzers can be leveraged to further Boost the capacity for proteome-wide analysis. Simple MS setups such as MALDI-TOF are only used for peptide mass measurements, whereas tandem mass spectrometers are used to determine peptide sequences.

Tue, 08 Dec 2020 10:00:00 -0600 en text/html https://www.technologynetworks.com/proteomics/articles/proteomics-principles-techniques-and-applications-343804




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