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 DobbsGenetics, Development and Cell BiologyBioinformatics & Computational Biology Iowa State University

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,

ProcessesCentral 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

idea from D Brutlag, Stanford, graphics from S Strobel)Modified from Mark Gerstein

Explosion of "Omes" & "Omics!"Genome, Transcriptome, Proteome

• 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 *

* 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

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:

atggcaattaaaattggtatcaatggttttggtcgtatgcacaacaccgtgatgacattgaagttgtaggtattaaatggcttatatgttgaaatatgattcaactcacggtcgaaagatggtaacttagtggttaatggtaaaactatccgGcaaacttaaactggggtgcaatcggtgttgatatcgctttaactgatgaaactgctcgtaaacatatcactgcaggcgcaaaaaaagtt

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

Molecular Biology Information: Protein

Sequences

• 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_ LNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTd8dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNEYKYFQRMTSd4dfra_ ISLIAALAVDRVIGMENAMPWN-LPADLAWFKRNTLd3dfr__ TAFLWAQDRDGLIGKDGHLPWH-LPDDLHYFRAQTV

Functions: Most cellular functions are performed or facilitated by proteins

• 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

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 ProcessesFunctional 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?

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, Nature 401: 115-116 (1999)

Modified from Mark Gerstein

Next Step after the Sequence?

• Expression Analysis• Structural Genomics• Protein Interactions• Pathway Analysis• Systems Biology

Understanding Gene Function on a Genomic

Scale

Evolutionary Implications of: • Introns & Exons• Intergenic Regions as "Gene Graveyard"

Modified from Mark Gerstein

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

(courtesy of J Hager)Modified from Mark Gerstein

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!

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

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

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

Modified from Mark Gerstein (Internet picture adaptedfrom D Brutlag, Stanford)

ISU's supercomputer "CyBlue" is among 100 most powerful in the world

Bioinformatics is born!& more Bioinformaticists are

needed!

(courtesy of Finn Drablos)

(Internet picture adaptedfrom D Brutlag, Stanford)

Modified from Mark Gerstein

Weber Cartoon

from Mark Gerstein

“Informatics” techniquesin Bioinformatics

•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

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:

What are the shared parts (bolt, nut, washer, spring, bearing), unique parts (cogs, levers)? What are the common parts -- types of parts (nuts & washers)?

How many roles can these play? How flexible and adaptable are they mechanically?

Where are the parts

located? Modified from Mark Gerstein

~2,000 folds

~30,000 genes

~2,000 genes1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 …

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 …

(human)

World of structures is also finite,providing a valuable simplification

Global Surveys of a Finite Set of Parts from Many Perspectives

Same logic for pathways, functions, sequence families, blocks, motifs....

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....

(T. pallidum)

Modified from Mark Gerstein

So, this is Bioinformatics

What is it good for?

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

Application II: Finding homologs

Modified from Mark Gerstein

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 ClonedHuman 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 proteinHereditary Non-polyposis Colon Cancer 120436 MLH1 U07418 6.3e-196 MLH1 U07187 DNA repair proteinCystic Fibrosis 219700 CFTR M28668 1.3e-167 YCF1 L35237 Metal resistance proteinWilson Disease 277900 WND U11700 5.9e-161 CCC2 L36317 Probable copper transporterGlycerol Kinase Deficiency 307030 GK L13943 1.8e-129 GUT1 X69049 Glycerol kinaseBloom Syndrome 210900 BLM U39817 2.6e-119 SGS1 U22341 HelicaseAdrenoleukodystrophy, X-linked 300100 ALD Z21876 3.4e-107 PXA1 U17065 Peroxisomal ABC transporterAtaxia Telangiectasia 208900 ATM U26455 2.8e-90 TEL1 U31331 PI3 kinaseAmyotrophic Lateral Sclerosis 105400 SOD1 K00065 2.0e-58 SOD1 J03279 Superoxide dismutaseMyotonic Dystrophy 160900 DM L19268 5.4e-53 YPK1 M21307 Serine/threonine protein kinaseLowe Syndrome 309000 OCRL M88162 1.2e-47 YIL002C Z47047 Putative IPP-5-phosphataseNeurofibromatosis, Type 1 162200 NF1 M89914 2.0e-46 IRA2 M33779 Inhibitory regulator protein

Choroideremia 303100 CHM X78121 2.1e-42 GDI1 S69371 GDP dissociation inhibitorDiastrophic Dysplasia 222600 DTD U14528 7.2e-38 SUL1 X82013 Sulfate permeaseLissencephaly 247200 LIS1 L13385 1.7e-34 MET30 L26505 Methionine metabolismThomsen Disease 160800 CLC1 Z25884 7.9e-31 GEF1 Z23117 Voltage-gated chloride channelWilms Tumor 194070 WT1 X51630 1.1e-20 FZF1 X67787 Sulphite resistance proteinAchondroplasia 100800 FGFR3 M58051 2.0e-18 IPL1 U07163 Serine/threoinine protein kinaseMenkes Syndrome 309400 MNK X69208 2.1e-17 CCC2 L36317 Probable copper transporter

Modified from Mark Gerstein

Application III:Genome/Transcriptome/Proteome

Characterization & ComparisonDatabases, 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

Building “Designer” Zinc Finger DNA-binding Proteins J Sander, Fengli Fu, J Townsend, R Winfrey

D Wright, K Joung, D Dobbs, D Voytas

Phil Becraft, GDCBAntony Chettoor

Drena Dobbs, GDCBJae-Hyung Lee

Kai-Ming Ho, Physics Zhong GaoYungok IhmHaibo CaoCai-zhuang Wang

Identifying "Missing" Components of Signal Transduction Pathways

Designing New HIV Therapies

Susan Carpenter, VMPMSijun LiuWendy Wood

Drena Dobbs, GDCBJae-Hyung Lee

Kai-Ming Ho, Physics & AstronomyYungok IhmHaibo CaoCai-zhuang Wang

Amy Andreotti,BBMBBruce Fulton, NMR FacilityVasant Honavar, Com S

Changhui Yan

Predicting Protein-Protein Interactions from Amino Acid Sequence

Vasant Honavar, Com SChanghui Yan

Drena Dobbs, GDCBJae-Hyung Lee

Kai-Ming Ho, Physics Robert Jernigan, BBMB