Bioinformatics Computational Biology Podcast for Frontiers in Biology

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

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

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

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

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 (m. RNAs & others) expressed in an organism * • Proteome - the complete collection of proteins expressed in an organism *

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

Molecular Biology Information: Protein Sequences Functions: Most cellular functions are performed or facilitated by proteins • Biocatalysis • • Protein sequences: d 1 dhfa_ Cofactor transport/storage LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTT Mechanical motion/support d 8 dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTS Immune protection d 4 dfra_ ISLIAALAVDRVIGMENAMPWNRegulation of growth and differentiation Information: • 20 letter alphabet (amino acids) à ACDEFGHIKLMNPQRSTVWY but not BJOUXZ • ~ 300 aa in an average protein (in bacteria) • ~ 3 X 106 known protein sequences Modified from Mark Gerstein LPADLAWFKRNTL d 3 dfr__ TAFLWAQDRDGLIGKDGHLPWHLPDDLHYFRAQTV • What is this protein? • Which amino acids are most important -- for folding, activity, interaction with other proteins? • Which sequence variations are harmful (or, beneficial)?

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

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

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

On a Large Scale? Whole Genome Sequencing 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, Nature 401: 115 -116 (1999) Modified from Mark Gerstein

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

Gene Expression Data: the Transcriptome Micro. Array 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 Modified from Mark Gerstein (courtesy of J Hager) ISU's Biotechnology Facilities include state-of-the-art Microarray & Proteomics instrumentation

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! Modified from Mark Gerstein 2 -hybrid Experiments: For each (and every) protein, identify every other protein with which it interacts! For yeast: 6000 x 6000 / 2 ~ 18 M interactions!!

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

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 "Cy. Blue" is among 100 most powerful in the world Modified from Mark Gerstein (Internet picture adaptedfrom D Brutlag, Stanford)

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

from Mark Gerstein Weber Cartoon

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

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

Molecular Parts = Conserved Domains Modified from Mark Gerstein

"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)? Modified from Mark Gerstein Where are the parts located?

World of structures is also finite, providing a valuable simplification (human) ~30, 000 genes ~2, 000 folds (T. pallidum) 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, Pro. Dom, Pfam, Blocks, Domo, WIT, CATH, Scop. . ~2, 000 genes

So, this is Bioinformatics What is it good for?

Application I: Designing Drugs • Understanding how proteins bind other molecules • Docking & structure modeling • Designing inhibitors Modified from Mark Gerstein 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).

Application II: Finding homologs Modified from Mark Gerstein

Finding WHAT? Homologs - "same genes" in different organisms • Human vs. Mouse vs. Yeast à 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 # Hereditary Non-polyposis Colon Cancer Cystic Fibrosis Wilson Disease Glycerol Kinase Deficiency Bloom Syndrome Adrenoleukodystrophy, X-linked Ataxia Telangiectasia Amyotrophic Lateral Sclerosis Myotonic Dystrophy Lowe Syndrome Neurofibromatosis, Type 1 120436 MSH 2 120436 MLH 1 219700 CFTR 277900 WND 307030 GK 210900 BLM 300100 ALD 208900 ATM 105400 SOD 1 160900 DM 309000 OCRL 162200 NF 1 U 03911 U 07418 M 28668 U 11700 L 13943 U 39817 Z 21876 U 26455 K 00065 L 19268 M 88162 M 89914 9. 2 e-261 MSH 2 M 84170 6. 3 e-196 MLH 1 U 07187 1. 3 e-167 YCF 1 L 35237 5. 9 e-161 CCC 2 L 36317 1. 8 e-129 GUT 1 X 69049 2. 6 e-119 SGS 1 U 22341 3. 4 e-107 PXA 1 U 17065 2. 8 e-90 TEL 1 U 31331 2. 0 e-58 SOD 1 J 03279 5. 4 e-53 YPK 1 M 21307 1. 2 e-47 YIL 002 C Z 47047 2. 0 e-46 IRA 2 M 33779 DNA repair protein Metal resistance protein Probable copper transporter Glycerol kinase Helicase Peroxisomal ABC transporter PI 3 kinase Superoxide dismutase Serine/threonine protein kinase Putative IPP-5 -phosphatase Inhibitory regulator protein Choroideremia Diastrophic Dysplasia Lissencephaly Thomsen Disease Wilms Tumor Achondroplasia Menkes Syndrome 303100 222600 247200 160800 194070 100800 309400 X 78121 U 14528 L 13385 Z 25884 X 51630 M 58051 X 69208 2. 1 e-42 7. 2 e-38 1. 7 e-34 7. 9 e-31 1. 1 e-20 2. 0 e-18 2. 1 e-17 GDP dissociation inhibitor Sulfate permease Methionine metabolism Voltage-gated chloride channel Sulphite resistance protein Serine/threoinine protein kinase Probablecopper transporter Modified from Mark Gerstein Human Gen. Bank BLASTX Gene Acc# for P-value Human c. DNA CHM DTD LIS 1 CLC 1 WT 1 FGFR 3 MNK Yeast Gene GDI 1 SUL 1 MET 30 GEF 1 FZF 1 IPL 1 CCC 2 Gen. Bank Yeast Gene Acc# for Description Yeast c. DNA S 69371 X 82013 L 26505 Z 23117 X 67787 U 07163 L 36317

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

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

Identifying "Missing" Components of Signal Transduction Pathways Phil Becraft, GDCB Antony Chettoor Drena Dobbs, GDCB Jae-Hyung Lee Kai-Ming Ho, Physics Zhong Gao Yungok Ihm Haibo Cai-zhuang Wang

Designing New HIV Therapies Susan Carpenter, VMPM Sijun Liu Wendy Wood Drena Dobbs, GDCB Jae-Hyung Lee Kai-Ming Ho, Physics & Astronomy Yungok Ihm Haibo Cai-zhuang Wang Amy Andreotti, BBMB Bruce Fulton, NMR Facility Vasant Honavar, Com S Changhui Yan

Predicting Protein-Protein Interactions from Amino Acid Sequence Vasant Honavar, Com S Changhui Yan Drena Dobbs, GDCB Jae-Hyung Lee Kai-Ming Ho, Physics Robert Jernigan, BBMB