1. Chapter 4: Protein Interactions and Disease
Mileidy W. Gonzalez, Maricel G. Kann
Presented by Md Jamiul Jahid

2. What to learn in this chapter
Experimental and computational methods to detect protein interactions
Protein networks and disease
Studying the genetic and molecular basis of disease
Using protein interactions to understand disease

3. What is Protein interaction
Protein is the main agents of biological function
Protein determine the phenotype of all organisms
Protein don't function alone
interaction with other proteins
interaction with other molecules (e.g. DNA, RNA)

4. What is Protein interaction
Protein interaction generally means physical contact between proteins and their interacting partners.
Protein associate physically to create macromolecular structures of various complexities and heterogeneities
Protein pair can form dimers, multi-protein complexes or long chains

5. What is Protein interaction
But it always need not to be physical
Besides physical interactions protein interaction means metabolic or genetic correlation or co-localization
Metabolic -> in same pathway
Genetically correlated -> co-expressed
Co-localization -> protein in the same cellular compartment

6. PPI Network PPI network represents interaction among proteins
Each node represent a protein
Each link represents an interaction

7. PPI Network
A PPI network of the proteins encoded by radiation-sensitive genes in mouse, rat, and human, reproduced from [89].

8. PPI Network Some use of PPI network
To learn the evolution of different proteins
About different systems they are involved
Network can be used to learn interaction for other species
Helpful to identify functions of uncharacterized proteins

9. Experimental Identification of PPIs
Biophysical Methods
High-Throughput Methods
Direct high-throughput methods
Indirect high-throughput methods

10. Biophysical Methods Mainly biochemical, physical and genetic methods
X-ray
Crystallography
NMR spectroscopy
Fluorescence
Atomic force microscopy

11. Biophysical Methods Biophysical methods identify interacting partners
Chemical features of the interaction
Problem:
Time and resource consumption is high
Applicable for small scale

12. High Throughput Methods
Direct high-throughput methods
Indirect high-throughput methods

13. Direct high-throughput methods
Yeast two-hybrid (Y2H)
Most common
Fuse two protein in a transcription binding domain
If the protein interact->transcription complex activated

14. Direct high-throughput methods
Y2H overview
Image courtesy Wikipedia.org

15. Direct high-throughput methods
Problem (Yeast two-hybrid)
Cannot identify complex protein interaction means more than two interaction
Interaction of proteins initiating transcription

16. Indirect high-throughput methods
Looking at characteristics of the gene encode that produce that protein
Gene co-expression
Assumption: genes of interacting protein must co-expressed to provide the product of protein interaction

17. Computational Predictions of PPIs
Empirical predictions
Theoretical predictions
Coevolution at the residue level
Coevolution at the full sequence level

18. Empirical predictions
Based on
Relative frequency of interacting domains
Maximum likelihood estimation
Co-expression
Disadvantage
Rely on existing network
Propagate inaccuracies

19. Theoretical Predictions of PPIs Based on Coevolution
Coevolution at the residue level
Coevolution at the full sequence level
In biology, coevolution is "the change of a biological object triggered by the change of a related object."

20. Coevolution at the residue
Paris of residues of the same protein can co-evolve for three dimensional proximity or shared functions
A pair of protein is assumed to interact if they show enrichment of the same correlated mutations

21. Coevolution at the full sequence level
Basic idea: changes in one protein are compensated by correlated changes in its interacting partners to preserve interaction
->> interacting protein have phylogenetic trees with topologies more similar than by chance
Mirrortree is most accurate option to indentify interaction

22. Mirrortree Identify the orthologs of both proteins in common species
Creating multiple sequence alignment (MSA) with each orthologs
Create distance metric from MSA
Calculate correlation coefficient between distance metric

23. Mirrortree

24. Different methods for computing PPI

25. Protein Network and Disease
Studying the Genetic Basis of Disease
Studying the Molecular Basis of Disease

26. Studying the Genetic Basis of Disease
After Mendelian genetics in the 1900, a lot of effort to categorize disease genes
Positional cloning: the process to isolate a gene in the chromosome based on its position
Genes identified by this approach
cystic fibrosis, HD, breast cancer etc.
still mutation in gene not correlate with symptoms

27. Studying the Genetic Basis of Disease
Several reasons
pleiotropy
influence of other genes
environmental factors

28. Studying the Genetic Basis of Disease
Pleiotropy: when a single gene produce multiple phenotype
Problem: complicates disease elucidation process because mutation of such gene can have effect of some, all or none of its traits.
Means, mutation of a pleiotrophic gene may cause multiple syndrome or only cause disease in some of the biological process

29. Studying the Genetic Basis of Disease
Influence of other genes
Interact synergistically
Modify one another

30. Studying the Genetic Basis of Disease
Environmental factors
diet
infection etc.
Cancer are believed to be caused by several genes and are affected by several environment factors

31. Studying the Molecular Basis of Disease
Genes associated with disease is important
Molecular details is also important to identify the mechanism triggering, participating and controlled perturbed biological functions

32. The role of protein interaction in disease
Protein interaction provide a vast source of molecular information because their interaction involve in
metabolic
signaling
immune
gene regulatory networks
Protein interaction should be the key target to understand molecular based disease understanding

33. The role of protein interaction in disease
Protein-DNA interaction disruption
Protein misfolding
New undesired protein interaction

34. Protein-DNA interaction disruption
p53 tumor suppressor
Mutation on p53 DNA-binding domain destroy its ability to bind its target DNA sequence
Cause preventioning of several anticancer mechanism it mediates

35. Protein misfolding and undesired interaction
protein folding: A process by which a protein goes to its 3D functional shape
New undesired protein interaction
Main cause of several disease like Huntington disease, Cystic fibrosis, Alzheimer's disease etc.

36. Using PPI network to understand disease
PPI Network can help identify novel pathway
PPI network can be helpful to explore difference between healthy and disease states
Protein interaction studies play a major role in the prediction of genotype-phenotype association

37. Using PPI network to understand disease
New diagnostic tools can result from genotype-phenotype associations
Can identify disease sub networks
Drug design

38. PPI Network can help identify novel pathway
PPI network: Maps physical and functional interaction of protein pairs
Pathway: Represents genetic, metabolic, signaling or neural processes as a series of sequential biochemical reaction

39. PPI Network can help identify novel pathway
Pathway alone cannot uncover disease detail
When performing pathway analysis to study disease differential expression is the key
Majority of human genes haven't been assigned to pathway

40. PPI Network can help identify novel pathway
In this scenario PPI network can be helpful to identify novel pathway
Some key findings
Disease genes are generally occupy peripheral position in PPI network
Few cancer genes are hubs
Disease genes tend to cluster together
Protein involved in similar phenotype are highly connected

41. PPI network can be helpful to explore difference between healthy and disease states
Source: Dynamic modularity in protein interaction networks predicts breast cancer outcome, Nature Biotechnology 27, 2009

42. Genotype-phenotype association and new disease genes
Disease gene by interacting partners of already known disease genes
Topological features to predict disease genes
970/5000 genes are disease genes

43. Disease subnetwork identification

44. Disease subnetwork identification

45. Drug design Hub node in PPI are not good for drug target
Less connected nodes may be good target for drug

46. Exercise
Objective: investigate Epstein-Barr Virus pathogenesis using PPI
EBV is most common human virus
95% adult infected to this virus
EBV replicates in epithelial cells and establish latency in B lymphocytes
35-50% time mono-nucleosis
Sometimes cancer

47. Dataset Dataset S1: EBV interactome Dataset S2: EBV-Human interactome
Software requirement:
Cytoscape (DL link:

48. Questions How many nodes and edges are featured in this network?
How many self interactions does the network have?
How many pairs are not connected to the largest connected component?
Define the following topological parameters and explain how they might be used to characterize a protein-protein interaction network: node degree (or average number of neighbors), network heterogeneity, average clustering coefficient distribution, network centrality.

49. Questions
How many unique proteins were found to interact in each organism?
How many interactions are mapped?
How many human proteins are targeted by multiple (i.e. how many individual human proteins interact with >1) EBV proteins?
How does identifying the multi-targeted human proteins help you understand the pathogenicity of the virus? —Hint: Speculate about the role of the multi-targeted human proteins in the virus life cycle.

50. Questions
Based on the ‘degree’ property, what can you deduce about the connectedness of ET-HPs? What does this tell you about the kind of proteins (i.e. what type of network component) EBV targets?

51. Questions
What do the number and size of the largest components tell you about the inter-connectedness of the ET-HP subnetwork?

52. Questions
Why is distance relevant to network centrality? What is unusual about the distance of ET-HPs to other proteins and what can you deduce about the importance of these proteins in the Human-Human interactome?

53. Questions
Based on your conclusions from questions i-iii, explain why EBV targets the ET-HP set over the other human proteins and speculate on the advantages to virus survival the protein set might confer.

54. Thanks