Protein-Protein Interactions
Tools and Approaches
Slides:
https://cytoscape.github.io/cytoscape-tutorials/presentations/ppi-tools1-2017-mpi.html
John "Scooter" Morris
Alex Pico
Max Planck Institute for Biology of Ageing
27 November 2017
Today's Agenda
| Time | Topic |
|---|---|
| 10:00 - 11:00 | Introductions - Why are protein-protein interactions important? |
| 11:00 - 12:30 | Protein-protein interaction networks - I |
| 13:30 - 15:00 | Protein-protein interaction networks - II |
| |
| |
| 15:00 - 15:30 | Break |
| 15:30 - 17:00 | Introduction to Cytoscape - II |
| |
| 17:00 - 18:00 | Workflows |
Introductions
- 2010 - Current
- Adjunct Assistant Professor, Pharmaceutical Chemistry, UCSF
- 2004 - Current
- Executive Director, NIH Resource for Biocomputing, VIsualization, and Informatics
- 1985 - 2004
- Principal Systems Architect: Genentech, Inc.
- 2006 - Current
- Cytoscape core team
- Author or co-author of several Cytoscape apps:
- structureViz2, clusterMaker2, chemViz2, stringApp, CyAnimator
Introductions
- Associate Director, Bioinformatics
- Executive Director NRNB
- Cytoscape team since 2006
- Author of half a dozen Cytoscape apps
- Founder of the WikiPathways repository
Introductions
- Computational Biologist
- Structural Biologist
- Biologist
- Chemist
- Bioinformatician
- Computer Scientist
- Data Scientist
- Other?
What is this course about?
- Some background
- Some techniques
- Cytoscape - network visualization and analysis
- UCSF Chimera - structural visualization and analysis
- It's what our lab works on ☺
Why?
- You tell me...
Why?
- Each person:
- Name
- Introduce your image
- Please keep it short
- Choose one image to present to everyone
Sources of PPI Data
- Experimental Techniques
- High Throughput
- Low Throughput
- Computational Techniques
- Public Repositories
Experimental Techniques
- Yeast 2 hybrid
Experimental Techniques
- Yeast 2 hybrid
- Pros:
- Relatively low-tech
- Scalable
- Cons:
- High false positive and false negative rate
- Pros:
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- Pros:
- Quantitative results in vivo
- Cons:
- Tag can obscure binding
- Protease can cleave protein
- Not good for transient interactions
- Pros:
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
- Pros:
- Can be used for membrane-bound complexes and insoluble proteins
- Can be used to capture a "snapshot" of dynamic processes
- Cons:
- Can capture random collisions (false positives)
- Size of BirA* (321 AA can interfere with binding (false negatives))
- NOTE: BioID2 is smaller (233 AA)
- 10 nm labeling radius. Need to consider location of fusion
- Pros:
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
- BioID2 - same procedure as BioID, but uses a smaller promiscuous biotin ligase
- split-BioID - like BioID, but BirA* is split into CBirA* and NBirA* parts, which are fused in the presence of Rapamycin, providing a conditional method
- APEX - a similar technique that uses an engineered ascorbate peroxidase, but is most often used to label as many cells as possible in an organelle rather than protein-protein interactions.
Experimental Techniques
- Yeast 2 hybrid
- Tandem Affinity Purification/Mass Spectrometry (AP/MS)
- BioID - Proximity-dependent Biotinylation
- Protein-fragment complementation assays
- Phage display
- Protein microarrays
- Chemical crosslinking
Jensen & Bork, Science, 2008
Experimental Techniques
- Genetic interactions
- Measures functional linkage between genes
- Orthogonal to physical interaction techniques
- Two approaches
- Synthetic Genetic Array (SGA)
- Epistatic MiniArray Profile (E-MAP)
Experimental Techniques
Synthetic Genetic Array (SGA)
Experimental Techniques
Synthetic Genetic Array (SGA)
Experimental Techniques
- Genetic Interactions: Epistatic MiniArray Profile (E-MAP)
- High throughput
- Quantitative
- Can provide corroboration to PPI data
Experimental Techniques
- Xray crystallography
- NMR
- SAXS
- ELISA Binding Assays
- Co-IP
Computational Techniques
- Text mining
- Search literature for protein-protein co-occurrences
- Orthology
- Predicts interaction based orthologous pairs in another species
- Domain pairs
- Predicts interaction based on domains interacting in other proteins
Public Repositories
- Pathway data
- WikiPathways
- Reactome
- KEGG
- Pathway Commons (collection)
- Published Pathway Figures
Public Repositories
Sources of PPI Data
- Experimental Techniques
- High Throughput
- Low Throughput
- Computational Techniques
- Public Repositories
Know what you're getting.
Introduction to Network Biology
Why Networks?
Networks are everywhere...
especially in biology!
especially in biology!
- Molecular networks
- Cell-cell communication
- Nervous systems
- Social networks
Networks are powerful tools...
especially in biology!
especially in biology!
- Reduce complexity
- More efficient than tables
- Great for data integration
- Intuitive visualization
Networks as Tools
Transcription factors targeting apoptosis genes that bind MKRN1 in protein-mRNA network in embryonic stem cells.
One network figure conveys results from multiple technologies. Cassar, EMBO Reports 2015, Fig.8
Networks as Tools
- Topological properties
- Hubs and subnetworks
- Classify, cluster and diffuse
- Data integration
- Data overlays
- Layouts and animation
- Exploratory analysis
- Context and interpretation
Cassar, EMBO Reports 2015, Fig.8
Applications in Research
Applications in Research
- In 2010, a Nature Methods paper introduced major themes of network visualization.
- (a) Mass spec analysis: 400 PPI in pneumonia microbe.
- (b) Subnetwork with function annotations.
- (c) Layout with knowledge of complexes.
- (d) Collapsed metanodes to reduce complexity.
Gehlenborg (2010) Visualization of omics data for systems biology. Nature Methods, 7, Fig. 1
Applications in Research
- Study of interactions with ASD-associated proteins.
- Y2H with human protein fragments of 26 ASD proteins: 848 interacitons with 539 proteins.
- Merged with known interactions from public datasets.
Sakai (2011) Protein Interactome Reveals Converging Molecular Pathways Among Autism Disorders.
Science Trans. Med., 3(86), Fig. 1
Applications in Research
- SARS-CoV-2-human interactions.
- AP-MS with 26 SARS-CoV-2 proteins: 332 interactions human proteins.
- Merged human-human PPI datasets to identify complexes.
- Used fill color to highlight known drug targets.
Gordon (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature, 583, Fig. 3
Applications in Research
- Genetic interactions for the entire yeast genome.
- Synthetic lethal screen of 1712 genes: 170,000 interactions.
- Visualization of clustering within and between functional groups annotated by GO.
- Predict functions and identify regulatory subnetworks.
Costanzo (2010) The genetic landscape of a cell. Science, 22, Fig. 1
Applications in Research
- Molecular profiling of two types of brain tumors.
- GSEA to identify distinguishing gene sets (nodes).
- Functional annotation of a network of gene sets.
Witt (2011) Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma. Cancer Cell, 20(2), Fig. 4
Applications in Research
- TCGA's seriers of molecular cancer profiles.
- Clinical, genomic, methylation, RNA and proteomic signatures.
- Multiple data types integrated onto signaling network.
- Includes patient sample-level data.
TCGA (2013) Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature, 499, Fig. 4
Applications in Research
The Challenge
- Making sense out of biological networks...
The Challenge
- Biological networks
- Seldom tell us anything by themselves
Analysis involves:- Understanding the characteristics of the network
- Modularity
- Comparison with other networks (i.e., random networks)
- Understanding the characteristics of the network
Visualization involves:- Placing nodes in a meaningful way (layouts)
- Mapping biologically relevant data to the network
- Node size, node color, edge weights, etc
- …which then allows for more analysis!
The Challenge
Biological Network Taxonomy
- Metabolic
- Signaling
- Regulatory
- Disease
- Key distinctions:
- Pruned and curated
- Mechanistic and contextual details
Biological Network Taxonomy
- Protein-Protein
- Protein-Ligand
- Domain-Domain
- Others
- Residue or atomic
- Cell-cell
- Epidemiology
- Social networks
Biological Network Taxonomy
- Protein/Sequence Similarity Networks (PSNs or SSNs)
- Chemical similarity
- Ligand similarity (SEA)
- Others
- Tag clouds
- Topic maps
Biological Network Taxonomy
- Metabolic
- Signaling
- Regulatory
- Disease
- Interaction
- Physical, Genetic, Social
- Similarity
- Structural, Sequence, Topical
Analytical Approaches
The levels of organization of complex networks:
- Node degree provides information about single nodes
- Three or more nodes represent a motif
- Larger groups of nodes are called modules or communities
- Hierarchy describes how the various structural elements are combined
Analytical Approaches
Degree is most commonly used, but there are other measures of the relative importance of a single node in a network.
Analytical Approaches
Network topology statistics such as node degree, degree distribution, centralitiy, clustering coefficient, shortest paths, and robustness of the network to the random removal of single nodes are important network characteristics.
Modularity refers to the identification of sub-networks of interconnected nodes that might represent molecules physically or functionally linked that work coordinately to achieve a specific function.
Motif analysis is the identification of small network patterns that are over-represented when compared with a randomized version of the same network. Regulatory elements are often composed of such motifs.
Network alignment and comparison tools can identify similarities between networks and have been used to study evolutionary relationships between protein networks of organisms.
Analytical Approaches
- Concepts
- Graph/Network correspondence
- Node/Vertex correspondence
- Edge directedness
- Usually a network property
- Multigraph
- Allow multiple edges between nodes
- Hypergraph
- Allow edges to connect more than 2 nodes
Analytical Approaches
- Network measures
- Shortest path length
- Shortest traversal distance between two nodes
- Can be weighted if edges have weights or hops if not
- Node degree
- Indegree: # of edges for which this node is a target
- Outdegree: # of edges for which this node is a source
- Degree (undirected): # of edges incident to this node
- Hub
- node with an exceptionally high degree
- Shortest path length
Analytical Approaches
- Network measures
- Diameter: the largest distance between two nodes
- Radius: the minimum of the maximum path between any two nodes
- Characteristic path length: average shortest path length
- Density: extent to which a network is fully connected
- Heterogeneity: tendency of the network to contain hub nodes
- Connected components: number of disconnected groups of nodes
Analytical Approaches
- Network measures
- Clustering coefficient
- Measures how close the neighbors of a node are to being a clique (fully connected)
- Clustering coefficient
Analytical Approaches
- Centrality - measures of node importance
- Degree centrality (find hubs)
- Betweenness centrality (bottleneck)
- Closeness centrality
- Degree centrality (find hubs)
Analytical Approaches
- Scale-free networks
- Degree distribution follows a power law:
P(k) ~ k-y, where y is a constant - Distinct "hubs" (essential proteins?)
- Resilient to perturbation
- Biological (and social) networks tend to be scale-free
- Degree distribution follows a power law:
Analytical Approaches
- Small-world networks
- any two arbitrary nodes are connected by a small number of intermediate edges
- the network has an average shortest path length much smaller than the number of nodes in the network (Watts, Nature, 1998).
- Interaction networks have been shown to be small-world networks (Barabási, Nature Reviews in Genetics, 2004)
Analytical Approaches
- Other techniques
- Guilt-by association
- Combine weak signals to get a stronger one
- Over-representation analysis
- Also called enrichment analysis
- Statistical technique to determine if a set of terms are over-represented (enriched) in a network (or subnetwork)
- Clustering
- Also called unsupervised classification
- Variety of techniques for group nodes based on attributes or connectivity
- Guilt-by association
Analytical Approaches
- Other techniques
- Motif finding
- Finding subnetworks of interest
Cytoscape Apps for Graph Analysis
Network Visualization
Depiction
- Various ways to depict biological networks:
- Node-Link (graph) representation
- Partitioned Node-Link representation
- Matrix representation
- Can be useful for very dense networks
- Can also map information into cells of matrix
- e.g. degree, color scale (heat map)
Data Mapping
- Mapping of data values associated with graph elements onto graph visuals
- Visual attributes
- Node fill color, border color, border width, size, shape, opacity, label
- Edge type, color, width, ending type, ending size, ending color
- Mapping types
- Continuous (numeric values)
- Discrete (categories)
- Passthrough (labels)
Data Mapping
- Avoid cluttering your visualization with too much data
- Highlight meaningful differences
- Avoid confusing the viewer
- Consider creating multiple network images
Layouts
- Layouts determine the location of nodes and (sometimes) the paths of edges
- Types:
- Simple
- Grid
- Hierarchical
- layout data as a tree or hierarchy
- Works best when there are no loops
- Circular (Radial)
- arrange nodes around a circle
- could use node attributes to govern position
- e.g. degree sorted
- Simple
- Types:
Layouts
- Types:
- Force-Directed
- simulate edges as springs
- may be weighted or unweighted
- Combining layouts
- Use a general layout (force directed) for the entire graph, but use hierarchical or radial to focus on a particular portion
- Multi-layer layouts
- Partition graph, layout each partition then layout partitions
- Many, many others
- Force-Directed
Layouts
- Manual tweaks with
Node Layout Tools - Scale
- Align
- Rotate
Layouts
- Use layouts to convey the relationships between the nodes.
- There is not one correct layout. Try different things.
- Layout algorithms may need to be “tuned” to fit your network.
Animation
- Animation is useful to show changes in a network:
- Over a time series
- Over different conditions
- Between species
Cytoscape
- Open source
- Cross platform
- Consortium
Core Concepts
Networks and Tables
Networks
e.g., PPIs or pathways
Tables
e.g., data or annotations
Visual Styles
Core Concepts
Cytoscape Apps!
apps.cytoscape.org
Loading Network Data
- Cytoscape can import network data from:
- Files (or URLs)
- Excel, TSV, CSV
- XGMML: eXtensible Graph Markup and Modelling Language
- SBML: Systems Biology Markup Language
- BioPAX
- PSI-MI
- SIF: Simple Interaction Format
- GML: Graph Markup Language
- ... and others depending on loaded Apps
- Files (or URLs)
Loading Network Data
- Cytoscape can import network data from:
- Public repositories:
- PSICQUIC
- STRING (via the stringApp)
- IntAct (via the IntActApp)
- Reactome (via the ReactomeFI app)
- WikiPathways (via the WikiPathways app)
- Pathway Commons (via the CyPath2 app)
- NDEx
- Automation:
- Command line scripts
- CyREST via R, Python, etc
- Public repositories:
Loading Table Data
- Cytoscape can load tables from:
- Files (or URLs)
- Excel, TSV, CSV
- Public repositories
- BioMart
- Automation:
- Command line scripts
- CyREST via R, Python, etc
- Files (or URLs)
Saving and Exporting
- Sessions save everything as
.cys files:
Networks, Tables, Styles, Screen sizes, etc. - Export networks in different formats:
SIF, GML, XGMML, BioPAX, JSON - Export tables as CSV files
- Publication quality graphics in several formats:
PDF, SVG, PNG, and JPEG
Hands-on Tutorials
Questions
- Specific workflows you are interested in?
- Any specific "How to" questions?
Thank You!
Protein-Protein Interactions
Tools and Approaches
Slides: https://cytoscape.github.io/cytoscape-tutorials/presentations/ppi-tools1-2017-mpi.html
John "Scooter" MorrisAlex Pico
Max Planck Institute for Biology of Ageing
27 November 2017
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