Protein Interactions and Disease

1. Protein Interactions and Disease Audry Kang 7/15/2013

2. Central Dogma of Molecular Biology

3. 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

4. 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

5. 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].

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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: • 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

15. Protein Networks and Disease

16. 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

17. 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)

18. 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