Week 13 Functional analysis, clinical applications, drug discoveryv2.pptx

Functional Analysis, Clinical Applications, and Drug Discovery Dr. Jan E. Janecka [email protected] 236 Mellon Hall Different Approaches Used to Understand Genes that Effect Phenotypes Information Obtained • • • • Gene Sequences Location in genome Variation Genotype/Phenotype Associations • Diff...

Functional Analysis, Clinical Applications, and Drug Discovery Dr. Jan E. Janecka [email protected] 236 Mellon Hall Different Approaches Used to Understand Genes that Effect Phenotypes Information Obtained • • • • Gene Sequences Location in genome Variation Genotype/Phenotype Associations • Differential Expression Lesson 1 • Gene Ontology • Gene Enrichment Gene Ontology Gene Ontology is a framework for modeling of biological systems and pathways • GO defines concepts/classes used to describe gene function, and relationships between these concepts • Functions classified along three aspects of gene products: 1. Molecular function – activities they perform 2. Cellular component – where they are active 3. Biological process – pathways/processes made up of activities of multiple gene products http://www.geneontology.org . Functional assignment of enzymes: the EC (Enzyme Commission) system Oxidoreductases 1,003 Transferases 1,076 Hydrolases 1,125 Lyases 356 Isomerases 156 Ligases 126 GO terms are assigned to NCBI Gene entries Enrichment Analysis One of the main uses of Gene Ontology is Enrichment Analysis • Given a set of genes that are up-regulated under certain conditions, which GO terms are overrepresented (or under-represented)? Provides insight on the pathways/processes that are affecting the phenotype Once a specific gene is identified need to study its product to understand how it causes disease Proteins and human disease Disease Protein Cystic fibrosis CFTR Sickle-cell anemia hemoglobin beta “mad cow” disease prion protein Alzheimer disease amyloid precursor protein Protein structure and human disease • In some cases, a single amino acid substitution can induce a dramatic change in protein structure Example, the DF508 mutation of CFTR alters the ahelical content of the protein and disrupts intracellular trafficking (cystic fibrosis) • Other changes are subtle and only induce a small change to a part of the protein Example: the E6V mutation in hemoglobin beta introduces a hydrophobic patch on the protein surface leading to clumping of hemoglobin molecules (sickle cell disease) • Model structure, function, & interactions • Disease phenotypes • Risk/disease progression • Drug targets Once you identify pathway and gene need to model structure and interactions to find treatment Lesson 2 • Protein structure • Modeling protein interactions Physical properties of proteins Many websites are available for the analysis of individual proteins. ExPASy and ISREC are two excellent resources. The accuracy of these programs is variable. Predictions based on primary amino acid sequence (such as molecular weight prediction) are likely to be more trustworthy. For many other properties (such as posttranslational modification of proteins by specific sugars), experimental evidence may be required rather than prediction algorithms. Access a variety of protein analysis programs from ExPASy Input: RBP4 accession number P02753 Page 400 Page 400 Protein secondary structure Protein secondary structure is determined by the amino acid side chains. Myoglobin is an example of a protein having many a-helices. These are formed by amino acid stretches 4-40 residues in length. Thioredoxin from E. coli is an example of a protein with many b sheets, formed from b strands composed of 5-10 residues. They are arranged in parallel or antiparallel orientations. Myoglobin (John Kendrew, 1958) Thioredoxin Green: alpha helices Brown: beta sheets Blue: random coil Gene TXN and TXN2 • responds to reactive oxygen species Secondary structure prediction Chou and Fasman (1974) developed an algorithm based on the frequencies of amino acids found in a helices, b-sheets, and turns. Proline: occurs at turns, but not in a helices. GOR (Garnier, Osguthorpe, Robson): related algorithm Modern algorithms: use multiple sequence alignments and machine learning techniques. They achieve higher success rate (>80%) Secondary structure prediction Web servers: GOR4 Jpred NNPREDICT PHD Predator PredictProtein PSIPRED SAM-T99sec Go to npsa-pbil.ibcp.fr/, click “Secondary structure prediction” to access this prediction tool Tertiary protein structure: protein folding Main approaches: [1] Experimental determination (X-ray crystallography, NMR) [2] Prediction ► Comparative modeling (based on homology) ► Threading ► Ab initio (de novo) prediction (Ingo Ruczinski at JHSPH) The Protein Data Bank (PDB) • PDB is the principal repository for protein structures • Established in 1971 • Accessed at http://www.rcsb.org/pdb or simply http://www.pdb.org • In 2014, it contained 97,000 structure entities The CATH Hierarchy of Structure Viewing structures at PDB: JMol Access to structure data at NCBI: VAST Vector Alignment Search Tool (VAST) offers a variety of data on protein structures, including -- PDB identifiers -- root-mean-square deviation (RMSD) values to describe structural similarities -- NRES: the number of equivalent pairs of alpha carbon atoms superimposed -- percent identity VAST+: pre-computed similar structures VAST+ comparison of 1A2X_A and 2IX7_B (troponin C versus calmodulin) Modeling of molecular interactions More complex models have been developed to also model ProteinProtein and ProteinDNA interactions Auron Lab at Duquesne Tsukada, J. (Auron, P.E,), Cytokine 54:6-19 (2011) GST Protein-Protein Interaction Between C/EBPβ & Spi1 DNA Binding Domain Principle of Glutathione-S-Transferase (GST) Interaction Pulldown GST-Spi1 C/EBPβ GST-Spi1 C/EBPβ GST-Spi1 C/EBPβ A Single Amino Acid is Critical for Spi-1 Interaction with C/EBPβ Listman, JA, et al., (Auron, PE) J. Biol. Chem. 280:41421 (2005) Large Aminoterminal domain of Spi-1 is distal from C/EBPβ interaction site 180o R232A Auron Lab at Duquesne Yang, Z. in Pulugulla, S.H. et al. (Auron) J. Biol Chem 293:19942-56 (2018) GST Protein-Protein Interaction Between C/EBPβ & Spi1 Amino Acid Side Chains in the Spi1 Protein-Protein Interaction Region (aa202-254) form a Cuff Around the Factor at the DNA-Protein Interface that Affects C/EBPβ Binding to the Spi1 DNA Binding Domain. Amino Terminal TAD Attachment Site Two Possible ProteinProtein Interaction Sites 202-242 243-254 Auron Lab at Duquesne Yang, Z-Y Pulugulla, S.H. et al. (Auron) J. Biol Chem 293:19942-56 (2018) ZDOCK Protein-Protein Docking Pierce, B.G., et al., Bioinformatics 30:1771-1773 (2014) The Spi1 DNA Binding Domain Presents a Mixed Polar/Aliphatic Surface that is Complementary to that of the Carboxyl End of the C/EBPβ Leucine Zipper (Minus the Carboxyl-Terminal Extra Tail). Auron Lab at Duquesne Rutter, NW Pulugulla, S.H. et al. (Auron) J. Biol Chem 293:19942-56 (2018) Lesson 3 • Drug design • Identifying compounds that will alter the interactions • Examples • PCSK9 • IL1B Steps in Genetic Approach to Drug Discovery 1. Find genomic regions with variants associated with a disease phenotype 2. Identify genes and functional elements in that region 3. Determine causal mutation 4. Determine the effect this has on structure and function of proteins, functional RNAs, and/or complexes 5. Identify other genes/RNA molecules that are affected 6. Find drugs that will target the functional sites or mediate the downstream effect of that diseases factor Example: PCSK9 gene/protein • Binds to LDL cholesterol receptors (LDLR). When PCSK9 is blocked, more LDLR is presented thus removing more cholesterol -- drug example: Alirocumab (monoclonal antibody) Example: PCSK9 gene/protein • Bioinformatics software cam model the interactions between molecules to predict those that my bind active sites Alirocumab (monoclonal antibody) Other potential pharmaceuticals identified via modeling protein interactions Sun et al. 2021 Many Genes Affect Coronary Artery Disease • There are >30 additional genes that increase risk of coronary disease All these loci are potential targets for new pharmaceuticals Example: IL1B and Spi-1 • IL1B is a cytokine that causes inflammation. • Spi-1 is part of the transcription factor complex necessary for upregulation of IL1B • IL1B involved in numerous Auto-Inflamatory Diseases (AID) and inflammatory associated disorders (arthritis, septic shock) • There are three drugs (anakinra, rilonacept, and canakinumab) that target IL1B The Chain B C/EBPβ C-terminus tail interacts with Arg232 on Spi-1 2D Interaction Diagram of LowModeMD for C/EBPβ C-ter Tail Arg 235 His 344 Arg 232 Left Pocket: C/EBPβ C-ter (Green) associates with Spi-1 Arg232 Auron Lab at Duquesne Asn 236 Cys 345 https://www.biosolveit.de/SeeSAR 3D Diagram of Developed Pharmacophore Queries in the Left pocket Left Arg 232 Acceptor Right Donor & Acceptor Molecular Operating Environment (MOE), from Chemical Computing Group, Montreal, PQ, Canada. MOE is a leading drug discovery software platform that integrates visualization, modeling and simulations, as well as methodology development, in one package. Pulugulla, S.H. et al. (Auron) J. Biol Chem 293:19942-56 (2018) Auron Lab at Duquesne L-Arginine Can Theoretically Dock to the Spi-1 DBD Pocket, Blocking C/EBPβ Binding Asn236 L-Arginine Arg232 DNA DNA Pulugulla, et al. (Auron, PE) J. Biol. Chem. (2018) In L-Arginine Inhibits Both Transcription of the IL1B Gene and Recruitment of C/EBPb to Spi1 at the IL1B Promoter in Stimulated Macrophages Transcription Inhibition Titration Specific Transcription Inhibition Control IL6 Gene Enhancer IL1B Promoter Control IL6 Gene Enhancer IL1B Enhancer Specific Inhibition of C/EBP Recruitment to IL1B Promoter IL1B Promoter IL1B Enhancer Spi1 Binding Slightly Enhanced at IL1B Promoter, But Not Inhibited at Either IL1B Promoter or Enhancer Review – Functional Analysis, Clinical Applications, and Drug Discovery Main Concepts Lesson 1 • Gene Ontology • Gene Enrichment Lesson 2 • Protein structure • Modeling protein interactions Lesson 3 • Drug design • Identifying compounds that will alter the interactions • Examples • PCSK9 • IL1B Review – Functional Analysis, Clinical Applications, and Drug Discovery Main Terms Lesson 1 • Gene ontology, molecular function, cellular component. Biological process, gene enrichment Lesson 2 • Primary, secondary, tertiary structure, a-helices, b sheets • X-ray crystallography, NMR, Protein Databank, VAST • ILIB, C/EBPβ, Spi1 Lesson 3 • LDL, PCSK9, Alirocumab, cytokine, IL1B, Arginine, SeeSAR