Introduction to bioinformatics Lecture 2 Genes and Genomes C E N T R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U E

Organisational Course website: or click on (>teaching >Introduction to Bioinformatics) Course book: Bioinformatics and Molecular Evolution by Paul G. Higgs and Teresa K. Attwood (Blackwell Publishing), 2005, ISBN (Pbk) Essential Bioinformatics by Jin Xiong, Cambridge University Press, 2006, ISBN Lots of information about Bioinformatics can be found on the web.

.....acctc ctgtgcaaga acatgaaaca nctgtggttc tcccagatgg gtcctgtccc aggtgcacct gcaggagtcg ggcccaggac tggggaagcc tccagagctc aaaaccccac ttggtgacac aactcacaca tgcccacggt gcccagagcc caaatcttgt gacacacctc ccccgtgccc acggtgccca gagcccaaat cttgtgacac acctccccca tgcccacggt gcccagagcc caaatcttgt gacacacctc ccccgtgccc ccggtgccca gcacctgaac tcttgggagg accgtcagtc ttcctcttcc ccccaaaacc caaggatacc cttatgattt cccggacccc tgaggtcacg tgcgtggtgg tggacgtgag ccacgaagac ccnnnngtcc agttcaagtg gtacgtggac ggcgtggagg tgcataatgc caagacaaag ctgcgggagg agcagtacaa cagcacgttc cgtgtggtca gcgtcctcac cgtcctgcac caggactggc tgaacggcaa ggagtacaag tgcaaggtct ccaacaaagc aaccaagtca gcctgacctg cctggtcaaa ggcttctacc ccagcgacat cgccgtggag tgggagagca atgggcagcc ggagaacaac tacaacacca cgcctcccat gctggactcc gacggctcct tcttcctcta cagcaagctc accgtggaca agagcaggtg gcagcagggg aacatcttct catgctccgt gatgcatgag gctctgcaca accgctacac gcagaagagc ctctc..... DNA sequence

Genome size OrganismNumber of base pairs  X-174 virus5,386 Epstein Bar Virus172,282 Mycoplasma genitalium580,000 Hemophilus Influenza1.8  10 6 Yeast (S. Cerevisiae)12.1  10 6 Human 3.2  10 9 Wheat16  10 9 Lilium longiflorum 90  10 9 Salamander100  10 9 Amoeba dubia670  10 9

Four DNA nucleotide building blocks G-C is more strongly hydrogen-bonded than A-T

A gene codes for a protein Protein mRNA DNA transcription translation CCTGAGCCAACTATTGATGAA PEPTIDEPEPTIDE CCUGAGCCAACUAUUGAUGAA

Central Dogma of Molecular Biology ReplicationDNA Transcription mRNA Translation Protein Transcription is carried out by RNA polymerase (II) Translation is performed on ribosomes Replication is carried out by DNA polymerase Reverse transcriptase copies RNA into DNA Transcription + Translation = Expression

But DNA can also be transcribed into non-coding RNA …  tRNA (transfer): transfer of amino acids to the ribosome during protein synthesis.  rRNA (ribosomal): essential component of the ribosomes (complex with rProteins).  snRNA (small nuclear): mainly involved in RNA-splicing (removal of introns). snRNPs.  snoRNA (small nucleolar): involved in chemical modifi-cations of ribosomal RNAs and other RNA genes. snoRNPs.  SRP RNA (signal recognition particle): form RNA-protein complex involved in mRNA secretion.  Further: microRNA, eRNA, gRNA, tmRNA etc.

Eukaryotes have spliced genes …  Promoter: involved in transcription initiation (TF/RNApol-binding sites)  TSS: transcription start site  UTRs: un-translated regions (important for translational control)  Exons will be spliced together by removal of the Introns  Poly-adenylation site important for transcription termination (but also: mRNA stability, export mRNA from nucleus etc.)

DNA makes mRNA makes Protein

DNA makes RNA makes Protein … yet another picture to appreciate the above statement

Transcription Factors mRNA transcription TF binding site (open) TF binding site (closed) TATA mRNA transcription TF binding site (TFBS) TATA TF Pol II Transcription Factor (TF): protein that binds to DNA and to a polymerase (Pol II) Polymerase: complex protein that transcribes DNA into mRNA Transcription factor – polymerase interaction sets off gene transcription… … many TFBSs are possible upstream of a gene Nucleosomes (chromatin structures composed of histones) are structures round which DNA coils. This blocks access of TFs

Some facts about human genes  There are about – genes in the human genome (~ 3% of the genome)  Average gene length is ~ bp  Average of 5-6 exons per gene  Average exon length is ~ 200 bp  Average intron length is ~ 2000 bp  8% of the genes have a single exon  Some exons can be as small as 1 or 3 bp

DMD: the largest known human gene  The largest known human gene is DMD, which stands for “Dystrophin (muscular dystrophy, Duchenne and Becker types)”  The gene encodes the protein dystrophin: the gene’s size is ~ 2.4 milion bp over 79 exons  X-linked recessive disease (affects boys)  Two variants: Duchenne-type (DMD) and becker-type (BMD)  Duchenne-type: more severe, frameshift-mutations Becker-type: milder phenotype, “in frame”- mutations Posture changes during progression of Duchenne muscular dystrophy

Nucleic acid basics  Nucleic acids are polymers  Each monomer consists of 3 moieties nucleoside nucleotide

Nucleic acid basics (2)  A base can be of 5 rings  Purines and Pyrimidines can base-pair (Watson- Crick pairs) Watson and Crick, 1953

Nucleic acid as hetero-polymers  Nucleosides, nucleotides (Ribose sugar, RNA precursor) (2’-deoxy ribose sugar, DNA precursor) (2’-deoxy thymidine tri- phosphate, nucleotide)  DNA and RNA strands REMEMBER: DNA =deoxyribonucleotides; RNA =ribonucleotides (OH-groups at the 2’ position) Note the directionality of DNA (5’-3’ & 3’-5’) or RNA (5’-3’) DNA = A, G, C, T ; RNA = A, G, C, U

So … DNARNA

Stability of base-pairing  C-G base pairing is more stable than A-T (A-U) base pairing (why?)  3 rd codon position has freedom to evolve (synonymous mutations)  Species can therefore optimise their G-C content (e.g. thermophiles are GC rich) (consequences for codon use?) Thermocrinis ruber, heat-loving bacteria

TAA, TAG, TGAStopStop codons CGT, CGC, CGA, CGG, AGA, AGGRArginine AAA, AAGKLysine GAT, GACDAspartic acid GAA, GAGEGlutamic acid CAT, CACHHistidine AAT, AACNAsparagine CAA, CAGQGlutamine TGGWTryptophan TAT, TACYTyrosine TCT, TCC, TCA, TCG, AGT, AGCSSerine ACT, ACC, ACA, ACGTThreonine CCT, CCC, CCA, CCGPProline GGT, GGC, GGA, GGGGGlycine GCT, GCC, GCA, GCGAAlanine TGT, TGC c Cysteine ATG M, Start Methionine TTT, TTCFPhenylalanine GTT, GTC, GTA, GTGVValine CTT, CTC, CTA, CTG, TTA, TTGLLeucine ATT, ATC, ATAIIsoleucine DNA codons Single Letter Code Amino Acid

DNA compositional biases  Base compositions of genomes: G+C (and therefore also A+T) content varies between different genomes  The GC-content is sometimes used to classify organism in taxonomy  High G+C content bacteria: Actinobacteria e.g. in Streptomyces coelicolor it is 72% Low G+C content: Plasmodium falciparum (~20%)  Other examples: Saccharomyces cerevisiae (yeast)38% Arabidopsis thaliana (plant)36% Escherichia coli (bacteria)50%

Genetic diseases: cystic fibrosis  Known since very early on (“Celtic gene”)  Autosomal, recessive, hereditary disease (Chr. 7)  Symptoms:  Exocrine glands (which produce sweat and mucus)  Abnormal secretions  Respiratory problems  Reduced fertility and (male) anatomical anomalies 30,000 3,000 20,000

cystic fibrosis (2)  Gene product: CFTR (cystic fibrosis transmembrane conductance regulator)  CFTR is an ABC (ATP-binding cassette) transporter or traffic ATPase.  These proteins transport molecules such as sugars, peptides, inorganic phosphate, chloride, and metal cations across the cellular membrane.  CFTR transports chloride ions (Cl - ) ions across the membranes of cells in the lungs, liver, pancreas, digestive tract, reproductive tract, and skin.

cystic fibrosis (3)  CF gene CFTR has 3-bp deletion leading to Del508 (Phe) in 1480 aa protein (epithelial Cl - channel)  Protein degraded in Endoplasmatic Reticulum (ER) instead of inserted into cell membrane The deltaF508 deletion is the most common cause of cystic fibrosis. The isoleucine (Ile) at amino acid position 507 remains unchanged because both ATC and ATT code for isoleucine Diagram depicting the five domains of the CFTR membrane protein (Sheppard 1999). Theoretical Model of NBD1. PDB identifier 1NBD as viewed in Protein Explorer

Let’s return to DNA and RNA structure …  Unlike three dimensional structures of proteins, DNA molecules assume simple double helical structures independent of their sequences.  There are three kinds of double helices that have been observed in DNA: type A, type B, and type Z, which differ in their geometries.  RNA on the other hand, can have as diverse structures as proteins, as well as simple double helix of type A.  The ability of being both informational and diverse in structure suggests that RNA was the prebiotic molecule that could function in both replication and catalysis (The RNA World Hypothesis).  In fact, some viruses encode their genetic materials by RNA (retrovirus)

Three dimensional structures of double helices Side view: A-DNA, B-DNA, Z-DNA Top view: A-DNA, B-DNA, Z-DNA Space-filling models of A, B and Z- DNA

Major and minor grooves

Forces that stabilize nucleic acid double helix  There are two major forces that contribute to stability of helix formation: Hydrogen bonding in base-pairing Hydrophobic interactions in base stacking 5’ 3’ Same strand stacking cross-strand stacking

Types of DNA double helix  Type A major conformation RNA minor conformation DNA Right-handed helix Short and broad  Type B major conformation DNA Right-handed helix Long and thin  Type Z minor conformation DNA Left-handed helix Longer and thinner

Secondary structures of Nucleic acids  DNA is primarily in duplex form  RNA is normally single stranded which can have a diverse form of secondary structures other than duplex.  RNA can form duplexes by folding back onto itself  DNA duplex is mostly in the B-form, RNA duplex regions in the A-form  DNA is more stable than RNA

Non B-DNA Secondary structures  Cruciform DNA  Triple helical DNA  Slipped DNA Hoogsteen basepairs Source: Van Dongen et al. (1999), Nature Structural Biology 6,

RNA Secondary structures  RNA pseudoknots  Cloverleaf rRNA structure Source: Cornelis W. A. Pleij in Gesteland, R. F. and Atkins, J. F. (1993) THE RNA WORLD. Cold Spring Harbor Laboratory Press. 16S rRNA Secondary Structure Based on Phylogenetic Data

3D structures of RNA : transfer-RNA structures  Secondary structure of tRNA (cloverleaf)  Tertiary structure of tRNA

3D structures of RNA : ribosomal-RNA structures  Secondary structure of large rRNA (16S)  Tertiary structure of large rRNA subunit Ban et al., Science 289 ( ), 2000

3D structures of RNA : Catalytic RNA  Secondary structure of self-splicing RNA  Tertiary structure of self-splicing RNA

Some structural rules …  Base-pairing is stabilizing  Un-paired sections (loops) destabilize  3D conformation with interactions makes up for this

Three main principles DNA makes RNA makes Protein Structure more conserved than sequence Sequence Structure Function

How to go from DNA to protein sequence A piece of double stranded DNA: 5’ attcgttggcaaatcgcccctatccggc 3’ 3’ taagcaaccgtttagcggggataggccg 5’ DNA direction is from 5’ to 3’

How to go from DNA to protein sequence 6-frame conceptual translation using the codon table: 5’ attcgttggcaaatcgcccctatccggc 3’ 3’ taagcaaccgtttagcggggataggccg 5’ So, there are six possibilities to make a protein from an unknown piece of DNA, only one of which might be a natural protein

Remark Identifying (annotating) human genes, i.e. finding what they are and what they do, is a difficult problem –First, the gene should be delineated on the genome Gene finding methods should be able to tell a gene region from a non- gene region Start, stop codons, further compositional differences –Then, a putative function should be found for the gene located

Dean, A. M. and G. B. Golding: Pacific Symposium on Bioinformatics 2000 Evolution and three-dimensional protein structure information Isocitrate dehydrogenase: The distance from the active site (in yellow) determines the rate of evolution (red = fast evolution, blue = slow evolution)

Genomic Data Sources DNA/protein sequence Expression (microarray) Proteome (xray, NMR, mass spectrometry) Metabolome Physiome (spatial, temporal) Integrative bioinformatics

Dinner discussion: Integrative Bioinformatics & Genomics VU metabolome proteome genome transcriptome physiome Genomic Data Sources Vertical Genomics

DNA makes RNA makes Protein (reminder)

DNA makes RNA makes Protein: Expression data More copies of mRNA for a gene leads to more protein mRNA can now be measured for all the genes in a cell at ones through microarray technology Can have 60,000 spots (genes) on a single gene chip Colour change gives intensity of gene expression (over- or under-expression)

Proteomics Elucidating all 3D structures of proteins in the cell This is also called Structural Genomics Finding out what these proteins do This is also called Functional Genomics

Protein-protein interaction networks

Metabolic networks Glycolysis and Gluconeogenesis Kegg database (Japan)

High-throughput Biological Data Enormous amounts of biological data are being generated by high-throughput capabilities; even more are coming –genomic sequences –arrayCGH (Comparative Genomic Hybridization) data, gene expression data –mass spectrometry data –protein-protein interaction data –protein structures –......

Protein structural data explosion Protein Data Bank (PDB): Structures (6 March 2001) x-ray crystallography, 1810 NMR, 278 theoretical models, others... Bioinformatics databases grow exponentially…

Dickerson’s formula: equivalent to Moore’s law Dickerson predicted that the Protein Data Bank (PDB) of protein three-dimensional structures would grow, starting with the first protein in 1960, as indicated by the above exponential growth function. On 27 March 2001 there were 12,123 3D protein structures in the PDB: Dickerson’s formula predicts 12,066 (within 0.5% -- not a bad prediction)! n = e 0.19(y-1960) with y the year.

Sequence versus structural data Structural genomics initiatives are now in full swing and growth is still exponential. However, growth of sequence data is even more rapidly. There are now more than 600 completely sequenced genomes publicly available. Increasing gap between structural and sequence data (“Mind the gap”)

Bioinformatics Offers an ever more essential input to –Molecular Biology –Pharmacology (drug design) –Agriculture –Biotechnology –Clinical medicine –Anthropology –Forensic science –Chemical industries (detergent industries, etc.) This list is from a molecular biology textbook – so not a self-absorbed bioinformatician is saying this…