Graph theory Application
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This document discusses using graph theory and protein-protein interaction (PPI) networks in bioinformatics. It provides examples of PPI networks for yeast and humans with nodes representing proteins and edges representing interactions. Studying PPI networks is useful because it allows researchers to predict protein function based on binding partners and map cellular pathways by understanding the network of interactions. The structure of PPI networks can also help relate the network organization to biological function.
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Proteins by Salman Ul Islam.
This document discusses methods for detecting protein-protein interactions. It begins by introducing the importance of protein-protein interactions for cell biology and biochemistry. It then describes several common techniques for studying these interactions, including gel filtration chromatography, co-immunoprecipitation, affinity chromatography, fluorescence resonance energy transfer (FRET) microscopy, yeast two-hybrid assays, and confocal microscopy. The document emphasizes that protein-protein interactions are non-covalent and reversible, and detecting them requires accurate techniques.
Experimental methods and the big data sets
1. Experiments in systems biology use quantitative data from multiple omics techniques like microarrays, sequencing, proteomics, lipidomics, and metabolomics to study biological systems.
2. Computational models are used to simulate dynamic changes in molecules over time based on precise quantification from experiments.
3. Both hypothesis-generating and hypothesis-driven studies are important in systems biology, with the latter focusing on targeted subsets of molecules or organelles.
Techniques used for separation in proteomics
Proteomics aims to characterize the complete set of proteins in a biological system. It faces challenges due to sample complexity and wide protein concentration ranges. Common separation techniques include 2D electrophoresis, 2D-DIGE, ICAT, SILAC, iTRAQ, MudPIT, and protein microarrays. Mass spectrometry is central to protein identification. Data analysis is challenging due to the large datasets and lack of standardization. Effective proteomics requires optimized multi-step workflows combining separation, labeling, mass spectrometry, and bioinformatics.
High throughput approaches to understanding gene function and mapping archite...
The document describes high-throughput approaches used to map gene and protein interactions in bacteria. It discusses reverse genetic techniques like loss-of-function and gain-of-function screens to understand gene phenotypes. Protein-protein interaction mapping methods like bacterial two-hybrid systems, bimolecular fluorescence complementation, and microarrays are described. As an example, a bacterial two-hybrid system was used to map 284 interactions between 155 proteins in Rickettsia sibirica by functionally shotgun sequencing its genome. Key virulence genes were identified through their interactions, providing insight into the pathogen's mechanism.
Cell signaling
- Cell signaling pathways regulate nearly all cellular functions through cascades of signaling events involving receptors, signal transducers, and effector proteins. Receptors include G-protein coupled receptors, receptor tyrosine kinases, cytokine receptors, and intracellular receptors.
- Signaling proteins that act as transducers include kinases, GTPases, adaptor proteins, and second messengers like cyclic nucleotides, calcium, lipids, and nitric oxide. These relay, integrate, and distribute signals within cells.
- Feedback loops allow cells to adapt their sensitivity to signaling and respond appropriately to their environment. Understanding cell signaling pathways is challenging due to their complexity, branching, and convergence.
Proteomics in VSC for crop improvement programme
Proteomics techniques such as gel electrophoresis and mass spectrometry are used to separate and identify proteins. Two-dimensional gel electrophoresis separates proteins by size and charge, while MALDI-TOF mass spectrometry relies on mass spectrometry to analyze proteins and peptides. Protein-protein interactions can be studied using techniques like yeast two-hybrid systems and co-immunoprecipitation. Databases such as UniProt, PDB, and KEGG provide information on protein sequences, structures, and pathways.
Protein protein interaction
Protein-protein interactions are important for many biological processes. There are various types of interactions depending on their composition and duration. Methods to study interactions include yeast two-hybrid, co-immunoprecipitation, affinity chromatography, and chromatin immunoprecipitation. Databases such as IntAct and MINT provide repositories for protein interaction data.
Systems Biology Approaches to Cancer
This document discusses systems biology approaches to studying cancer. It defines systems biology as studying organisms as interacting networks of genes, proteins, and reactions. Biological networks are constructed from different types of data and relationships. Integrating multiple data types into networks can provide a more complete understanding of cancer than single data types in isolation. Networks can be used to identify cancer driver genes, dysregulated pathways, and biomarkers for disease classification, understanding mechanisms, and drug development. While current biological networks are incomplete, systems approaches have already provided insights and are expected to be more powerful as networks become more comprehensive.
Proteomics
1. Proteomics is the study of proteomes, which are the entire set of proteins expressed by a genome.
2. Mass spectrometry combined with separation techniques like liquid chromatography are the main tools used in proteomics to identify and characterize proteins.
3. Modern proteomics utilizes multidimensional separation methods like multiple liquid chromatography columns or liquid chromatography coupled with capillary electrophoresis prior to mass spectrometry to better resolve complex protein mixtures.
Protein protein interaction
Proteins interact with other proteins to carry out most biological processes in cells. Understanding these protein-protein interactions (PPIs) is important for determining how proteins function. There are several methods for detecting and analyzing PPIs, including affinity chromatography, immunoprecipitation, cross-linking, phage display, and genetic techniques like using suppressor mutations or observing synthetic lethal effects. Each method has advantages and disadvantages for elucidating different types of PPIs that are integral to cellular processes.
Protein protein interactions
The document discusses protein-protein interactions (PPIs) and methods used to study them. It defines PPIs as physical contacts between two or more proteins through biochemical or electrostatic forces. It describes different types of PPIs including homo-oligomers, hetero-oligomers, covalent and non-covalent interactions. Common methods to study PPIs are also summarized, such as yeast two-hybrid systems, co-immunoprecipitation, and protein interaction databases. The applications and importance of PPI research are mentioned including roles in various cellular processes and diseases.
protein microarray.pptx
This document discusses protein microarrays, which are a high-throughput screening method. Protein microarrays allow researchers to study protein interactions and activities on a large scale by placing many proteins on a solid surface in defined locations. There are two main types of protein microarrays: analytical microarrays which use antibodies or other capture molecules to probe complex protein solutions, and functional microarrays which contain full-length functional proteins to study protein-protein and other biochemical interactions. Protein microarrays have applications in areas like biomarker identification, studying post-translational modifications, and host-pathogen interactions. However, several challenges remain in developing stable and sensitive protein microarrays.
Systems biology
Introduction
Overview
Reductionist approach
Holistic approach
What is systems biology?
○ Advantages of Systems Biology
Tools of holistic approach
○ Proteomics, Transcriptomics and Metabolomics
Conclusion
References
protein microarray-types and approaches.pptx
Protein microarrays are a high-throughput technology that allows thousands of proteins to be analyzed simultaneously. They consist of a solid support coated with thousands of different proteins in a defined array. Each protein spot represents a different protein. Protein microarrays can be used to study protein expression levels, interactions, activities, and functions on a large scale. There are different types including analytical, functional, and reverse-phase microarrays that each have their own strengths and applications.
“Proteomics” to study genes and genomes
By:
Nazish Nehal,
M. Tech (Biotechnology),
University School of Biotechnology (USBT),
Guru Gobind Singh Indraprastha University,
New Delhi (INDIA)
Techniques in proteomics
this presentation provides a detailed overview of most of the techniques used in proteomics for gene expression studies
KnetMiner - EBI Workshop 2017
Using KnetMiner to search and visualise the knowledge network of genes involved in neurodegenerative diseases such as Alzheimer, Parkinson and Huntington.
Proteomics and its applications in phytopathology
Dear friends, I Abhijeet kashyap presenting the basics of proteomics to you all . Proteomics is the large-scale study of proteins, particularly their structures and functions.Proteomics helps in understanding the structure and function of different proteins as well as protein-protein interactions of an organism.
intracellular receptors ,signaling,transcription factors , nucleic acids
This document provides an overview of intracellular receptors, enzymes, signals, transcription factors, structural proteins, and nucleic acids. It discusses in detail the structures and functions of DNA, RNA, nuclear receptors, IP3 receptors, intracellular enzymes, transcription factors, and structural proteins like collagen, keratin, myosin, and actin. The presentation was given by S. Dinakar from the Department of Pharmacology at PSG College of Pharmacy in Coimbatore, India.
Revealing disease-associated pathways by network integration of untargeted me...
Soyeon Kim presented on a metabolomics study analyzing metabolites and correlating changes with disease states. Metabolomics can identify and quantify metabolites through targeted or untargeted methods. PIUMet is a network-based algorithm that integrates untargeted metabolomics data to identify novel disease-associated metabolites and proteins by linking disease features to metabolites and inferring subnetworks. Experiments show PIUMet can detect many disease features and identify dysregulated pathways in Huntington's disease.
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Graph theory Application
- 1. Graph Theory application in Bioinformatics Sultan Mehmood MSCSF14M019 PUCIT
- 2. 2 Biological Terminology • Protein complex • Domain • Molecular Pathway • Homology – Orthology – Paralogy
- 3. 3 Graph Terminology Node Edge Directed/Undirected Degree Shortest Path/Geodesic distance Neighborhood Subgraph Complete Graph Clique Degree Distribution Hubs
- 4. 4 Examples of Biological Networks – Protein-Protein Interaction Networks – Metabolic Networks – Signaling Networks – Transcription Regulatory Networks We will discuss here only Protein-Protein Interaction Networks
- 5. 5 What is meant by a Protein-Protein Interaction (PPI) Network
- 6. 6 Example of a PPI Network • Yeast PPI network • Nodes – proteins • Edges – interactions The color of a node indicates the phenotypic effect of removing the corresponding protein (red = lethal, green = non- lethal, orange = slow growth, yellow = unknown).
- 7. 7 A Human PPI Network
- 8. 8 Why is it useful to study PPI networks? • Proteins are workhorses of cell • Carry out catalytic reactions, transport, Form viral caspids • Transmit information from DNA to RNA, traverse membranes, • Form regulated channel, make possible synthesis of new proteins • Responsible for degradation of unnecessary proteins, vehicles of immune response • A prime way to predict protein function is through identification of binding partners • If the function of at least one of the components with which the protein interacts is known, that should let us assign its function(s) and the pathway(s) • Hence, through the intricate network of these interactions we can map cellular pathways, their interconnectivities and their dynamic regulation
- 9. 9 Why is it useful to study structure of PPI networks? • Common properties of biological networks • Can help us relate network structure to biological function • Protein’s relative position in a network • Correlate conserved functional modules with protein complexes