C6: Protein-Protein Interaction Analysis - Prelab Reading

Welcome to the prelab reading for our session on Protein-Protein Interaction (PPI) Analysis! Proteins rarely act alone; they form intricate networks of interactions to carry out their biological functions. Understanding these interactions is key to deciphering cellular processes, disease mechanisms, and identifying potential drug targets.

1. What are Protein-Protein Interactions (PPIs)?

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What is a Protein-Protein Interaction (PPI)?

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A physical contact established between two or more protein molecules. These interactions are specific and result from biochemical events driven by forces like electrostatic interactions, hydrogen bonding, and hydrophobic effects. They are fundamental to virtually every cellular process.

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Proteins are the workhorses of the cell, and their functions are often modulated or executed through interactions with other proteins. These interactions can be:

• Stable (Obligate): Proteins that are always found together as part of a complex (e.g., subunits of ribosomes or hemoglobin).
• Transient: Proteins that associate and dissociate temporarily to perform a specific function (e.g., signaling pathway components, enzyme-substrate interactions).
• Homodimeric/Homooligomeric: Interactions between identical protein subunits.
• Heterodimeric/Heterooligomeric: Interactions between different protein subunits.

The sum of all PPIs in a cell is known as the interactome.

Significance of PPIs:

• Cellular Organization: Form structural scaffolds (e.g., cytoskeleton).
• Signal Transduction: Relay signals from the cell surface to the nucleus.
• Metabolic Pathways: Enzymes in a pathway often interact.
• Gene Regulation: Transcription factors interact with each other and with regulatory DNA.
• Immune Response: Antibody-antigen interactions, T-cell receptor signaling.

Figure 1: Conceptual illustration of different ways proteins can interact. (Source: Wikimedia Commons)

2. Why Study PPIs?

Understanding PPIs allows us to:

• Elucidate Protein Function: The interaction partners of a protein can provide clues about its role in the cell.
• Map Cellular Pathways: Connecting interacting proteins helps in reconstructing signaling and metabolic pathways.
• Understand Disease Mechanisms: Aberrant PPIs are often implicated in diseases like cancer, neurodegenerative disorders, and infectious diseases. For example, viral proteins interact with host proteins to facilitate infection.
• Identify Drug Targets: PPI interfaces can be targeted by drugs to modulate protein activity or disrupt disease-relevant interactions.

Which of the following is NOT a primary reason for studying Protein-Protein Interactions?

• To determine the primary amino acid sequence of a protein.
• To understand how proteins function in cellular pathways.
• To identify potential targets for drug development.
• To gain insights into disease mechanisms.

3. Methods for Detecting and Predicting PPIs

Detecting and predicting PPIs involves a wide array of experimental and computational techniques.

3.1 Experimental Methods (In vivo and In vitro)

These methods aim to directly identify physical interactions between proteins.

• Yeast Two-Hybrid (Y2H):

  • Principle: An in vivo technique that uses the transcriptional activation of a reporter gene to detect interactions. If two proteins interact, they bring together a DNA-binding domain (DBD) and an activation domain (AD) of a transcription factor, reconstituting its function and activating the reporter.
  • Use Case: Large-scale screening for binary interactions.
  • Watch a quick overview:

• Co-Immunoprecipitation (Co-IP) followed by Mass Spectrometry (MS):

  • Principle: An antibody targets a known protein (“bait”). If this protein interacts with others (“prey”), these partners will also be pulled down with the bait. The entire complex is then analyzed, often by MS, to identify the interacting proteins.
  • Use Case: Identifying members of a protein complex in their native cellular environment.
  • Watch a quick overview:

• Affinity Purification coupled with Mass Spectrometry (AP-MS):

  • Principle: Similar to Co-IP, but often involves tagging the bait protein (e.g., with GST, FLAG, HA tags) for which high-affinity antibodies or binding resins are available. This allows for efficient purification of the bait and its interacting partners.
  • Use Case: Systematic identification of protein complexes.

• Surface Plasmon Resonance (SPR):

  • Principle: An in vitro technique that measures the binding kinetics (association and dissociation rates) and affinity of two proteins in real-time without labeling. One protein is immobilized on a sensor chip, and the other flows over it. Binding changes the refractive index near the sensor surface.
  • Use Case: Quantifying binding affinity and kinetics of purified proteins.

3.2 Computational Prediction Methods

These methods predict PPIs based on various data sources and algorithms, especially useful when experimental data is scarce or for large-scale predictions.

• Sequence-based methods: Predict interactions based on sequence homology, co-evolutionary patterns (correlated mutations), or known interaction motifs in protein sequences.
• Structure-based methods:
  • Protein Docking: Predicts the 3D structure of a protein complex given the individual structures of interacting proteins.
  • Threading/Homology Modeling: If the structure of a homologous complex is known, it can be used as a template.
• Genome context-based methods:
  • Gene Fusion (Rosetta Stone): If two proteins are found as a single fused protein in another organism, they are likely to interact.
  • Gene Neighborhood/Operons: Genes whose products interact are often found close together on the chromosome, especially in prokaryotes (operons).
  • Phylogenetic Profiles: Proteins that function together in a pathway or complex tend to co-occur or be jointly absent across different genomes.
• Text Mining: Algorithms that scan scientific literature (e.g., PubMed abstracts) to find mentions of protein interactions.

Match Experimental Methods to their Principle

Method

• Yeast Two-Hybrid (Y2H)
• Co-Immunoprecipitation (Co-IP)
• Surface Plasmon Resonance (SPR)
• Affinity Purification-MS (AP-MS)

Brief Principle

• Uses an antibody to pull down a target protein and its bound partners from cell lysate.
• Measures binding kinetics by detecting changes in refractive index as one protein flows over an immobilized partner.
• Purifies a tagged "bait" protein and its interactors for identification by mass spectrometry.
• Detects interaction by reconstituting a functional transcription factor that activates a reporter gene in vivo.

Reset Check Pairs

4. Databases for Protein-Protein Interactions

Several public databases collect and curate PPI data from literature and high-throughput experiments. They are invaluable resources for researchers.

• STRING (Search Tool for the Retrieval of Interacting Genes/Proteins):

  • Content: Known and predicted interactions (direct physical and indirect functional). Includes data from experimental studies, computational predictions, text mining, and co-expression.
  • Features: Provides confidence scores for interactions, visualizes networks, links to other databases.
  • Website: https://string-db.org/

• BioGRID (Biological General Repository for Interaction Datasets):

  • Content: Curates physical and genetic interactions from published literature for multiple species.
  • Features: Provides detailed evidence for each interaction.
  • Website: https://thebiogrid.org/

• IntAct:

  • Content: Molecular interaction data, manually curated from literature and direct submissions. Follows PSI-MI standards.
  • Features: Focuses on detailed annotation of interaction evidence.
  • Website: https://www.ebi.ac.uk/intact/

• MINT (Molecular INTeraction database):

  • Content: Focuses on experimentally verified PPIs, curated from literature.
  • Website: https://mint.bio.uniroma2.it/

• DIP (Database of Interacting Proteins):

  • Content: Experimentally determined protein interactions, curated from literature.
  • Website: https://dip.doe-mbi.ucla.edu/ (Note: Check website status, as some older databases may have accessibility issues or merged with others).

4.1 Example: Querying the STRING Database

Let’s walk through a hypothetical query for interactions of human p53 (a tumor suppressor protein, official gene symbol TP53).

1. Go to the STRING website: https://string-db.org/
2. Enter your protein: In the “Protein by name” search box, type TP53.
3. Select organism: Choose Homo sapiens from the dropdown or suggestions.
4. Click “Search”.
5. Analyze Results:
   • You’ll see a network diagram where nodes are proteins and edges represent interactions.
   • Edge colors/thickness can indicate the type or strength of evidence.
   • The interface allows you to adjust confidence scores, select evidence types (e.g., experiments, databases, text mining), and explore interactors.
   • You can click on nodes (proteins) to get more information or on edges (interactions) to see the evidence.

Figure 2: Legend for STRING network. The actual network for a protein like TP53 will show many interactors with different evidence types.

5. Introduction to PPI Network Analysis

PPI data is often represented as a network (or graph), where:

• Nodes (Vertices): Represent individual proteins.
• Edges (Links): Represent interactions between proteins.

Analyzing these networks can reveal:

• Hubs: Highly connected proteins, often essential for cellular function. Disrupting hubs can have significant consequences.
• Bottlenecks: Proteins that bridge different parts of the network or pathways.
• Modules/Clusters: Densely connected groups of proteins that often correspond to protein complexes or functional units.
• Pathways: Sequences of interactions that mediate biological processes.

Basic Network Parameters:

• Degree (Connectivity): The number of interactions a protein has. Proteins with high degrees are hubs.
• Clustering Coefficient: Measures how well a protein’s interactors are connected to each other. High clustering suggests participation in a tight-knit complex.
• Centrality Measures (e.g., Betweenness Centrality): Identify proteins that are critical for communication flow within the network (bottlenecks often have high betweenness centrality).

Figure 3: A simple example of a protein interaction network. Nodes are proteins, edges are interactions. (Source: Wikimedia Commons)

6. Tools for PPI Network Visualization and Analysis

Specialized software is used to visualize and analyze these complex networks.

• Cytoscape:
  • What it is: An open-source software platform for visualizing complex networks and integrating them with any type of attribute data. It’s highly popular in bioinformatics for PPI network analysis.
  • Key Features:
    • Import network data from various file formats or databases.
    • Visualize networks with customizable layouts, colors, node shapes, etc.
    • Analyze network topology (degree, centrality, clustering).
    • Perform functional enrichment analysis of network modules (e.g., using Gene Ontology).
    • Extendable with numerous apps (plugins) for specialized analyses.
  • Website: https://cytoscape.org/
  • Introduction to Cytoscape:

During our lab session, we will likely use a tool like Cytoscape to explore and analyze PPI networks.

7. Applications of PPI Analysis

The study of PPIs has broad applications:

• Functional Annotation: Assigning functions to uncharacterized proteins by examining their interaction partners.
• Pathway Discovery: Identifying novel biological pathways or adding components to known ones.
• Disease Understanding: Pinpointing how disruptions in PPI networks contribute to disease pathology. For instance, identifying host-pathogen PPIs can reveal mechanisms of infection.
• Drug Discovery:
  • Identifying PPIs essential for pathogen survival as drug targets.
  • Designing drugs that specifically inhibit or stabilize disease-relevant PPIs.
  • Predicting off-target effects of drugs by analyzing their interactions with the broader interactome.

In a PPI network, 'hubs' are generally proteins with very few interactions and play minor roles in the cell.

True False

8. Conclusion

Protein-protein interactions are central to understanding life at a molecular level. This prelab reading has introduced the fundamental concepts, detection and prediction methods, key databases, and the basics of network analysis. In the upcoming lab session, we will delve deeper into analyzing these interactions, equipping you with skills to explore the fascinating world of cellular networks.

Make sure you are familiar with the terms and concepts discussed here, as they will form the basis for our practical exercises.