Bioinformatics & Computational Biology Podcast for Frontiers in Biology - ISU 7/13/06

1. Bioinformatics& Computational BiologyPodcast for Frontiers in Biology - ISU 7/13/06 Thanks to Mark Gerstein (Yale) & Eric Green (NIH) for many borrowed & modified PPTs Drena Dobbs Genetics, Development and Cell Biology Bioinformatics & Computational Biology Iowa State University

2. What is Bioinformatics?(& What is Computational Biology?) Wikipedia: • Bioinformatics & computational biology involve the use of techniques from mathematics,informatics, statistics, and computer science(& engineering) to solve biological problems

3. What is Bioinformatics?(& What is Computational Biology?) Gerstein: • (Molecular) Bioinformatics is conceptualizing biology in terms of molecules & applying “informatics” techniques - derived from disciplines such as mathematics, computer science, and statistics - to organize and understand information associated with these molecules, on a large scale Modified from Mark Gerstein

4. Central Dogma of Molecular Biology DNA sequence -> RNA -> Protein -> Phenotype Molecules Sequence, Structure, Function Processes Mechanism, Specificity, Regulation Central Paradigm for Bioinformatics Genomic (DNA) Sequence -> mRNA& other RNA sequence -> Protein sequence -> RNA & Protein Structure -> RNA & Protein Function -> Phenotype Large Amounts of Information Standardized Statistical What is the Information?Biological Sequences, Structures, Processes Modified from Mark Gerstein idea from D Brutlag, Stanford, graphics from S Strobel)

5. Explosion of "Omes" & "Omics!"Genome, Transcriptome, Proteome * Note: the set of specific RNAs or proteins expressed varies greatly in different cells and tissues -- and critically depends on the age, developmental stage, disease state, etc. of the organism • Genome - the complete collection of DNA (genes and "non-genes") of an organism • Transcriptome- the complete collection of RNAs (mRNAs & others) expressed in an organism * • Proteome- the complete collection of proteins expressed in an organism *

6. Molecular Biology Information: DNA & RNA Sequences Functions: • Genetic material • Information transfer (mRNA) • Protein synthesis (tRNA/mRNA) • Catalytic & regulatory activities (some very new!) Information: • 4 letter alphabet • (DNA nucleotides: AGCT) • ~ 1,000 base pairs in a small gene • ~ 3 X 109 bp in a genome (human) DNA sequence: atggcaattaaaattggtatcaatggttttggtcgtat gcacaacaccgtgatgacattgaagttgtaggtattaa atggcttatatgttgaaatatgattcaactcacggtcg aaagatggtaacttagtggttaatggtaaaactatccg Gcaaacttaaactggggtgcaatcggtgttgatatcgctttaactgatgaaactgctcgtaaacatatcactgcaggcgcaaaaaaagtt RNA sequence has "U" instead of "T" • Where are the genes? • Which DNA sequences encode mRNA? • Which DNA sequences are "junk"? • Which RNA sequences encode protein? Modified from Mark Gerstein

7. Molecular Biology Information: Protein Sequences Functions:Most cellular functions are performed or facilitated by proteins • Biocatalysis • Cofactor transport/storage • Mechanical motion/support • Immune protection • Regulation of growth and differentiation Information: • 20 letter alphabet (amino acids) • ACDEFGHIKLMNPQRSTVWY but not BJOUXZ • ~ 300 aa in an average protein (in bacteria) • ~ 3 X 106 known protein sequences Protein sequences: d1dhfa_ LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTT d8dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTS d4dfra_ ISLIAALAVDRVIGMENAMPWN-LPADLAWFKRNTL d3dfr__ TAFLWAQDRDGLIGKDGHLPWH-LPDDLHYFRAQTV • What is this protein? • Which amino acids are most important -- for folding, activity, interaction with other proteins? • Which sequence variations are harmful (or, beneficial)? Modified from Mark Gerstein

8. Molecular Biology Information:Macromolecular Structures DNA/RNA/Protein Structures • How does a protein (or RNA) sequence fold into an active 3-dimensional structure? • Can we predict structure from sequence? • Can we predict function from structure (or perhaps, from sequence alone?) Modified from Mark Gerstein

9. We don't yet understand the protein folding code - but we try to engineer proteins anyway! Modified from Mark Gerstein

10. Molecular Biology Information:Biological Processes Functional Genomics • How do patterns of gene expression determine phenotype? • Which genes and proteins are required for differentiation during during development? • How do proteins interact in biological networks? • Which genes and pathways have been most highly conserved during evolution?

11. On a Large Scale?Whole GenomeSequencing Genome sequence now accumulate so quickly that, in less than a week, a single laboratory can produce more bits of data than Shakespeare managed in a lifetime, although the latter make better reading. -- G A Pekso, Nature401: 115-116 (1999) Modified from Mark Gerstein

12. Understanding Gene Function on a Genomic Scale Next Step after the Sequence? • Expression Analysis • Structural Genomics • Protein Interactions • Pathway Analysis • Systems Biology • Evolutionary Implications of: • Introns & Exons • Intergenic Regions as "Gene Graveyard" Modified from Mark Gerstein

13. Gene Expression Data: the Transcriptome MicroArray Data • Yeast Expression Data: • Levels for all 6,000 genes! • Experiments to investigate how genes respond to changes in environment or how patterns of expression change in normal vs cancerous tissue ISU's Biotechnology Facilities include state-of-the-art Microarray & Proteomics instrumentation Modified from Mark Gerstein (courtesy of J Hager)

14. Other Whole-Genome Experiments Systematic Knockouts: Make "knockout" (null) mutations in every gene - one at a time - and analyze the resulting phenotypes! For yeast: 6,000 KO mutants! 2-hybrid Experiments: For each (and every) protein, identify every other protein with which it interacts! For yeast: 6000 x 6000 / 2 ~ 18M interactions!! Modified from Mark Gerstein

15. Molecular Biology Information:Integrating Data • Understanding the function of genomes requires integration of many diverse and complex types of information: • Metabolic pathways • Regulatory networks • Whole organism physiology • Evolution, phylogeny • Environment, ecology • Literature (MEDLINE) Modified from Mark Gerstein

16. Storing & Analyzing Large-scale Information:Exponential Growth of Data Matched by Development of Computer Technology CPU vs Disk & Net • Both the increase in computer speed and the ability to store large amounts of information on computers have been crucial • Improved computing resources have been a driving force in Bioinformatics ISU's supercomputer "CyBlue" is among 100 most powerful in the world Modified from Mark Gerstein (Internet picture adaptedfrom D Brutlag, Stanford)

17. Bioinformatics is born!& more Bioinformaticists are needed! (Internet picture adaptedfrom D Brutlag, Stanford) Modified from Mark Gerstein (courtesy of Finn Drablos)

18. Weber Cartoon from Mark Gerstein

19. Databases Building, Querying Object-oriented DB String Comparison Text search Alignment Significance statistics Finding Patterns Machine Learning Data Mining Statistics Linguistics Geometry Robotics Graphics (Surfaces, Volumes) Comparison & 3D Matching Simulation & Modeling Newtonian Mechanics Electrostatics Numerical Algorithms Simulation Network modeling “Informatics” techniquesin Bioinformatics

20. Challenges in Organizing Information:Redundancy and Multiplicity • Different sequences can have the same structure • Organism has many similar genes • Single gene may have multiple functions • Genes and proteins function in genetic and regulatory pathways • How do we organize all this information so that we can make sense of it? Integrative Genomics: genes >< structures <> functions <> pathways <> expression levels <>regulatory systems <> …. Modified from Mark Gerstein

21. Molecular Parts = Conserved Domains Modified from Mark Gerstein

22. "Parts List" approach to bike maintenance: How many roles can these play? How flexible and adaptable are they mechanically? What are the shared parts (bolt, nut, washer, spring, bearing), unique parts (cogs, levers)? What are the common parts -- types of parts (nuts & washers)? Where are the parts located? Modified from Mark Gerstein

23. World of structures is also finite,providing a valuable simplification (human) ~30,000 genes ~2,000 folds (T. pallidum) ~2,000 genes Global Surveys of a Finite Set of Parts from Many Perspectives Same logic for pathways, functions, sequence families, blocks, motifs.... Modified from Mark Gerstein Functions picture from www.fruitfly.org/~suzi (Ashburner); Pathways picture from, ecocyc.pangeasystems.com/ecocyc (Karp, Riley). Related resources: COGS, ProDom, Pfam, Blocks, Domo, WIT, CATH, Scop....

24. So, this is Bioinformatics What is it good for?

25. Application I:Designing Drugs • Understanding how proteins bind other molecules • Docking & structure modeling • Designing inhibitors Figures adapted from Olsen Group Docking Page at Scripps, Dyson NMR Group Web page at Scripps, and from Computational Chemistry Page at Cornell Theory Center). Modified from Mark Gerstein

26. Application II: Finding homologs Modified from Mark Gerstein

27. Finding WHAT? Homologs - "same genes" in different organisms • Human vs. Mouse vs. Yeast • Much easier to do experiments on yeast! Best Sequence Similarity Matches to Date Between Positionally Cloned Human Genes and S. cerevisiae Proteins Human Disease MIM # Human GenBank BLASTX Yeast GenBank Yeast Gene Gene Acc# for P-value Gene Acc# for Description Human cDNA Yeast cDNA Hereditary Non-polyposis Colon Cancer 120436 MSH2 U03911 9.2e-261 MSH2 M84170 DNA repair protein Hereditary Non-polyposis Colon Cancer 120436 MLH1 U07418 6.3e-196 MLH1 U07187 DNA repair protein Cystic Fibrosis 219700 CFTR M28668 1.3e-167 YCF1 L35237 Metal resistance protein Wilson Disease 277900 WND U11700 5.9e-161 CCC2 L36317 Probable copper transporter Glycerol Kinase Deficiency 307030 GK L13943 1.8e-129 GUT1 X69049 Glycerol kinase Bloom Syndrome 210900 BLM U39817 2.6e-119 SGS1 U22341 Helicase Adrenoleukodystrophy, X-linked 300100 ALD Z21876 3.4e-107 PXA1 U17065 Peroxisomal ABC transporter Ataxia Telangiectasia 208900 ATM U26455 2.8e-90 TEL1 U31331 PI3 kinase Amyotrophic Lateral Sclerosis 105400 SOD1 K00065 2.0e-58 SOD1 J03279 Superoxide dismutase Myotonic Dystrophy 160900 DM L19268 5.4e-53 YPK1 M21307 Serine/threonine protein kinase Lowe Syndrome 309000 OCRL M88162 1.2e-47 YIL002C Z47047 Putative IPP-5-phosphatase Neurofibromatosis, Type 1 162200 NF1 M89914 2.0e-46 IRA2 M33779 Inhibitory regulator protein Choroideremia 303100 CHM X78121 2.1e-42 GDI1 S69371 GDP dissociation inhibitor Diastrophic Dysplasia 222600 DTD U14528 7.2e-38 SUL1 X82013 Sulfate permease Lissencephaly 247200 LIS1 L13385 1.7e-34 MET30 L26505 Methionine metabolism Thomsen Disease 160800 CLC1 Z25884 7.9e-31 GEF1 Z23117 Voltage-gated chloride channel Wilms Tumor 194070 WT1 X51630 1.1e-20 FZF1 X67787 Sulphite resistance protein Achondroplasia 100800 FGFR3 M58051 2.0e-18 IPL1 U07163 Serine/threoinine protein kinase Menkes Syndrome 309400 MNK X69208 2.1e-17 CCC2 L36317 Probable copper transporter Modified from Mark Gerstein

28. Application III:Genome/Transcriptome/ProteomeCharacterization & Comparison Databases, statistics • Occurrence of specific genes or features in a genome • How many kinases in yeast? • Compare Tissues • Which proteins are expressed in cancer vs normal tissues? • Diagnostic tools • Drug target discovery Modified from Mark Gerstein

29. Building “Designer” Zinc Finger DNA-binding Proteins J Sander, Fengli Fu, J Townsend, R Winfrey D Wright, K Joung, D Dobbs, D Voytas

30. Identifying "Missing" Components of Signal Transduction Pathways Phil Becraft, GDCBAntony ChettoorDrena Dobbs, GDCBJae-Hyung LeeKai-Ming Ho, Physics Zhong GaoYungok IhmHaibo CaoCai-zhuang Wang

31. Designing New HIV Therapies Susan Carpenter, VMPM Sijun Liu Wendy WoodDrena Dobbs, GDCB Jae-Hyung LeeKai-Ming Ho, Physics & Astronomy Yungok Ihm Haibo Cao Cai-zhuang WangAmy Andreotti,BBMBBruce Fulton, NMR FacilityVasant Honavar, Com S Changhui Yan

32. Predicting Protein-Protein Interactions from Amino Acid Sequence Vasant Honavar, Com S Changhui YanDrena Dobbs, GDCB Jae-Hyung LeeKai-Ming Ho, Physics Robert Jernigan, BBMB