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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

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protein review
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
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
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
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
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 (GTPase-activating protein) results in Htt aggregation
    • Potential target for therapy
experimental identification of ppis biophysical methods
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
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


-Can only identify pair-wise interactions

-Bias for unspecific interactions

experimental identification of ppis high throughput methods1
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 i dentification methods
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
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
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
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)


  • Identifies orthologs of proteins in common species
  • Creates a multiple sequence alignment (MSA) of each protein and its orthologs
  • Builds distance matrices
  • Calculated the correlation coefficient between distance matricies
studying the genetic basis of 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
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
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