Chapter 4: Protein Interactions and Disease

Mileidy W. Gonzalez, Maricel G. Kann

Presented by Md Jamiul Jahid

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

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)

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

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

PPI Network

• PPI network represents interaction among proteins

• Each node represent a protein

• Each link represents an interaction

PPI Network

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

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

Experimental Identification of PPIs

• Biophysical Methods

• High-Throughput Methods– Direct high-throughput methods– Indirect high-throughput methods

Biophysical Methods

• Mainly biochemical, physical and genetic methods– X-ray– Crystallography– NMR spectroscopy– Fluorescence– Atomic force microscopy

Biophysical Methods

• Biophysical methods identify interacting partners

• Chemical features of the interaction

• Problem: – Time and resource consumption is high– Applicable for small scale

High Throughput Methods

• Direct high-throughput methods

• Indirect high-throughput methods

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

Direct high-throughput methods

Image courtesy Wikipedia.org

Y2H overview

Direct high-throughput methods

• Problem (Yeast two-hybrid)– Cannot identify complex protein interaction

means more than two interaction– Interaction of proteins initiating transcription

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

Computational Predictions of PPIs

• Empirical predictions

• Theoretical predictions– Coevolution at the residue level– Coevolution at the full sequence level

Empirical predictions

• Based on– Relative frequency of interacting domains– Maximum likelihood estimation– Co-expression

• Disadvantage– Rely on existing network– Propagate inaccuracies

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."

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

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

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

Mirrortree

Different methods for computing PPI

Protein Network and Disease

• Studying the Genetic Basis of Disease

• Studying the Molecular Basis of Disease

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

Studying the Genetic Basis of Disease

• Several reasons– pleiotropy– influence of other genes– environmental factors

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

Studying the Genetic Basis of Disease

• Influence of other genes– Interact synergistically– Modify one another

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

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

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

The role of protein interaction in disease

• Protein-DNA interaction disruption

• Protein misfolding

• New undesired protein interaction

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

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.

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

Using PPI network to understand disease

• New diagnostic tools can result from genotype-phenotype associations

• Can identify disease sub networks

• Drug design

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

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

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

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

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

Disease subnetwork identification

Disease subnetwork identification

Drug design

• Hub node in PPI are not good for drug target

• Less connected nodes may be good target for drug

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

Dataset

• Dataset S1: EBV interactome

• Dataset S2: EBV-Human interactome

• Software requirement:– Cytoscape (DL link: www.cytoscape.org)

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.

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.

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?

Questions

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

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?

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.

Thanks