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Protein Interactions and Disease
Audry Kang
7/15/2013
Central Dogma of Molecular Biology
Protein Review
• Primary Structure:
Chain of amino acids
• Secondary Structures:
Hydrogen bonds
resulting in alpha helix,
beta sheet and turns
• Tertiary Structure:
Overall Shape of a
single protein molecule
• Quaternary Structure:
structure formed by
several protein subunits
What is “Protein Interaction?”
• Physical contact between proteins and their
interacting partners (DNA, RNA)
– Dimers, multi-protein complexes, long chains
– Identical or heterogeneous
– Transient or permanent
• Functional Metabolic or Genetic Correlations
– Proteins in the same pathway or cycles or cellular
compartments
Protein-protein interactions
• Nodes represent proteins
• Lines connecting then
represent interactions
between them
• Allows us to visualize the
evolution of proteins and
the different functional
systems they are
involved in
• Allows us to compare
evolutionarily between
species
Figure 1. A PPI network of the proteins encoded by radiation-sensitive genes in mouse, rat, and human,
reproduced from [89].
Why Do We Care about PPI?
• Proteins play an central role in biological function
• Diseases are caused by mutations that change structure of
proteins
• Considering a protein’s network at all different functional
levels (pair-wise, complexes, pathways, whole genomes)
has advanced the way that we study human disease
An example: Huntington’s Disease
• AD, neurodegenerative disease identified by
Huntington in 1872 and patterns of
inheritance documented in 1908
• 100 years of genetic studies  identified
the culprit gene
• 1993 – CAG repeat in the Huntingtin gene
– Causes insoluble neuronal inclusion bodies
• 2004 - Mechanism Identified by mapping
out all the PPIs in HD
– Interaction between Htt and GIT1 (GTPaseactivating protein) results in Htt aggregation
– Potential target for therapy
Experimental Identification of PPIs:
Biophysical Methods
– Provides structural information
– Methods include: X-ray crystallography, NMR
spectroscopy, fluorescence, atomic force
microscopy
– Time and resource consuming
– Can only study a few complexes at a time
Experimental Identification of PPIs:
High-Throughput Methods
Direct high-throughput methods:
Yeast two-hybrid (Y2H)
-Tests the interaction of two
proteins by fusing a
transcription-binding domain
-If they interact, the transcription
complex is activated
-A reporter gene is transcribed and
the product can be detected
Drawbacks:
-Can only identify pair-wise
interactions
-Bias for unspecific interactions
http://www.specmetcrime.com/noncovalent_complexes_in_mass_s.htm
Experimental Identification of PPIs:
High-Throughput Methods
Indirect high-throughput methods:
• Looks at characteristics of genes encoding
interacting partners
• Gene co-expression – genes of interacting
proteins must be co-expressed
– Measures the correlation coefficient of relative
expression levels
• Synthetic lethality – introduces mutations on
two separate genes which are viable alone
but lethal when combined
Drawbacks of Experimental
Identification Methods
• High false positive
• Low agreement when studied with different
techniques
• Only generates pair-wise interaction
relationships and has incomplete coverage
Computational Predictions of PPIs
•
•
•
•
Fast, inexpensive
Used to validate experimental data and select targets
for screening
Allows us to study proteins in different levels (dimer,
complex, pathway, cells, etc)
Two categories:
– Methods predicting protein domain interactions from
existing empirical data about protein-protein interactions
•
•
•
Maximum likelihood estimation of domain interaction
probability
Co-expression
Network properties
– Methods relying on theoretical information to predict
interactions
•
•
•
•
Mirrortree
Phylogenetic profiling
Gene neighbors methods
The Rosetta Stone Method
Example: Theoretical Predictions of PPIs Based on
Coevolution at the Full-Sequence Level
The Principle:
• Changes in one protein result in changes in its
interacting partner to preserve the interaction
• Interacting proteins coevolve similarly
The Mirrortree Method
•
•
•
Measures coevolution for a pair of
proteins
Mirrortree correlation coefficient
is used to measure tree similarity
Each square is the tree distance
between two orthologs (darker
colors represent closeness)
Method:
1.
Identifies orthologs of proteins
in common species
2.
Creates a multiple sequence
alignment (MSA) of each protein
and its orthologs
3.
Builds distance matrices
4.
Calculated the correlation
coefficient between distance
matricies
Protein Networks and Disease
Studying the Genetic Basis of Disease
• The correlation between
mutations in a person’s genome
and symptoms is not clear…
• Pleiotrophy – single gene
produces multiple phenotypes 
mutations in a single gene may
cause multiple syndromes or only
affects certain processes
• Genes can influence one another
– Epistasis – interact synergistcally
– Modify each other’s expression
• Environmental factors
Studying the Molecular Basis of
Disease
• Crucial for understanding the pathogenesis and
disease progression of disease and identifying
therapeutic targets
Role of protein interactions in disease
• Protein-DNA Interaction disruptions (p53 TSP)
• Protein Misfolding
• New undesired protein interactions (HD, AD)
• Pathogen-host protein interactions (HPV)
Using PPI Networks to Understand
Disease
• PPI Networks can help identify novel pathways to
gain basic knowledge of disease
• Explore differences between healthy and disease
states
• Prediction of genotype-phenotype associations
• Development of new diagnostic tools for
identifying genotype-phenotype associations
• Identifying pathways that are activated in disease
states and markers for prognostic tools
• Development of drugs and therapeutic targets