Chapter 4: Protein Interactions and Disease

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 -gt in same pathway
• Genetically correlated -gt co-expressed
• Co-localization -gt 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-gttranscription 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
• -gtgt 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 misfolding
• 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 www.cytoscape.org)

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 gt1) 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