1-Month Practical Master Course Genome Analysis Jaap Heringa Centre for Integrative Bioinformatics VU (IBIVU) Vrije Univ

1. C E N T E 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 1-Month Practical Master Course Genome AnalysisJaap Heringa Centre for Integrative Bioinformatics VU (IBIVU) Vrije Universiteit Amsterdam The Netherlands www.ibivu.cs.vu.nl heringa@cs.vu.nl

2. Chemistry Biology Molecular biology Mathematics Statistics Bioinformatics Computer Science Informatics Medicine Physics

3. C E N T E 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 Biological Sequence AnalysisPair-wise sequence alignment Residue exchange matrices Multiple sequence alignment Phylogeny

4. DNA sequence .....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.....

5. Genome size Organism Number of base pairs X-174 virus 5,386 Epstein Bar Virus 172,282 Mycoplasma genitalium 580,000 Hemophilus Influenza 1.8  106 Yeast (S. Cerevisiae) 12.1  106 Human 3.2  109 Wheat 16  109 Lilium longiflorum 90  109 Salamander 100  109 Amoeba dubia 670  109

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

7. Functional Genomics Genome Expressome Proteome TERTIARY STRUCTURE (fold) Metabolome TERTIARY STRUCTURE (fold) Regulation, signalling cascades, chaperonins, compartmentalisation

8. 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’

9. How to go from DNA to protein sequence 6-frame translation using the codon table (last lecture): 5’attcgttggcaaatcgcccctatccggc 3’ 3’ taagcaaccgtttagcggggataggccg 5’

10. 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) Dean, A. M. and G. B. Golding: Pacific Symposium on Bioinformatics2000

11. Protein Sequence-Structure-Function Ab initio prediction and folding Sequence Structure Function Threading Function prediction from structure Homology searching (BLAST)

12. Widely used tool for homology detection: PSI-BLAST • Heuristic tool to cut down computations required for database searching (~1M sequences in DB) • Sensitivity gained by iteratively finding hits (local alignments) and repeating search Q hits T DB PSSM

13. Threading Template sequence + Compatibility score Query sequence Template structure

14. Threading Template sequence + Compatibility score Query sequence Template structure

15. Fold recognition by threading Fold 1 Fold 2 Fold 3 Fold N Query sequence Compatibility scores

16. Bioinformatics • “Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975)) • “Nothing in bioinformatics makes sense except in the light of Biology”

17. Divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) ACCD or ACCD Pairwise Alignment AB─D A─BD mutation deletion

18. Divergent evolution Ancestral sequence: ABCD ACCD (B C) ABD (C ø) ACCD or ACCD Pairwise Alignment AB─D A─BD mutation deletion true alignment

19. Mutations under divergent evolution (a) G (b) G Ancestral sequence G C A C One substitution - one visible Two substitutions - one visible Sequence 1 Sequence 2 (c) G (d) G 1: ACCTGTAATC 2: ACGTGCGATC * ** D = 3/10 (fraction different sites (nucleotides)) G A A A Back mutation - not visible Two substitutions - none visible G

20. Convergent evolution • Often with shorter motifs (e.g. active sites) • Motif (function) has evolved more than once independently, e.g. starting with two very different sequences adopting different folds • Sequences and associated structures remain different, but (functional) motif can become identical • Classical example: serine proteinase and chymotrypsin

21. Serine proteinase (subtilisin) and chymotrypsin • Different evolutionary origins, no sequence similarity • Similarities in the reaction mechanisms. Chymotrypsin, subtilisin and carboxypeptidase C have a catalytic triad of serine, aspartate and histidine in common: serine acts as a nucleophile, aspartate as an electrophile, and histidine as a base. • The geometric orientations of the catalytic residues are similar between families, despite different protein folds. • The linear arrangements of the catalytic residues reflect different family relationships. For example the catalytic triad in the chymotrypsin clan (SA) is ordered HDS, but is ordered DHS in the subtilisin clan (SB) and SDH in the carboxypeptidase clan (SC).

22. A protein sequence alignment MSTGAVLIY--TSILIKECHAMPAGNE----- ---GGILLFHRTHELIKESHAMANDEGGSNNS * * * **** *** A DNA sequence alignment attcgttggcaaatcgcccctatccggccttaa att---tggcggatcg-cctctacgggcc---- *** **** **** ** ******

23. What can sequence tell us about structure (HSSP) Sander & Schneider, 1991

24. Searching for similarities What is the function of the new gene? The “lazy” investigation (i.e., no biologial experiments, just bioinformatics techniques): – Find a set of similar protein sequences to the unknown sequence – Identify similarities and differences – For long proteins: identify domains first

25. Evolutionary and functional relationships • Reconstruct evolutionary relation: • Based on sequence • -Identity (simplest method) • -Similarity • Homology (common ancestry: the ultimate goal) • Other (e.g., 3D structure) • Functional relation: • SequenceStructureFunction

26. Searching for similarities Common ancestry is moreinteresting: Makes it more likely that genes share the same function Homology: sharing a commonancestor – a binary property (yes/no) – it’s a nice tool: When (anunknown) gene X ishomologous to (a known) gene G itmeans that we gain a lot of informationon X: what we know about G can be transferred to X as a good suggestion.

27. Biological definitions for related sequences • Homologues are similar sequences in two different organisms that have been derived from a common ancestor sequence. Homologues can be described as either orthologues or paralogues. • Orthologues are similar sequences in two different organisms that have arisen due to a speciation event. Orthologs typically retain identical or similar functionality throughout evolution. • Paralogues are similar sequences within a single organism that have arisen due to a gene duplication event. • Xenologues are similar sequences that do not share the same evolutionary origin, but rather have arisen out of horizontal transfer events through symbiosis, viruses, etc.

28. How to evolve Important distinction: • Orthologues: homologous proteins in different species (all deriving from same ancestor) • Paralogues: homologous proteins in same species (internal gene duplication) • In practice: to recognise orthology, bi-directional best hit is used in conjunction with database search program (this is called an operational definition)

29. So this means … Source: http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/Orthology.html

30. Example today: Pairwise sequence alignment needs sense of evolution Global dynamic programming MDAGSTVILCFVG Evolution M D A A S T I L C G S Amino Acid Exchange Matrix Search matrix MDAGSTVILCFVG- Gap penalties (open,extension) MDAAST-ILC--GS

31. How to determine similarity Frequent evolutionary events at the DNA level: 1. Substitution 2. Insertion, deletion 3. Duplication 4. Inversion We will restrict ourselves to these events

32. nucleotide one-letter code A DNA sequence alignment attcgttggcaaatcgcccctatccggccttaa att---tggcggatcg-cctctacgggcc---- *** **** **** ** ****** A protein sequence alignment MSTGAVLIY--TSILIKECHAMPAGNE----- ---GGILLFHRTHELIKESHAMANDEGGSNNS * * * **** *** amino acid one-letter code

33. Dynamic programmingScoring alignments – Substitution (or match/mismatch) • DNA • proteins – Gap penalty • Linear: gp(k)=ak • Affine: gp(k)=b+ak • Concave, e.g.: gp(k)=log(k) The score for an alignment is the sum of the scores over all alignment columns

34. Dynamic programmingScoring alignments Sa,b= - gp(k) = gapinit+ kgapextensionaffine gap penalties

35. DNA: define a score for match/mismatch ofletters Simple: Used in genome alignments:

36. Dynamic programmingScoring alignments T D W V T A L K T D W L - - I K 2020 10 1 Affine gap penalties (open, extension) Amino Acid Exchange Matrix Score: s(T,T)+s(D,D)+s(W,W)+s(V,L)-Po-2Px + +s(L,I)+s(K,K)