1. Multiple sequence alignment Lesson 4

2. VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWYQQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDFYPGA--VTVAWKADS-- AALGCLVKDYFPEP--VTVSWNSG--- VSLTCLVKGFYPSD--IAVEWWSNG-- Like pairwise alignment BUT compare nsequences instead of 2 Each row represents an individual sequence Each column represents the ‘same’ position May be gaps in some sequences

3. MSA & Evolution MSA can give you a picture of the forces that shape evolution! • Important amino acids or nucleotides are not “allowed” to mutate • Less important positions change more easily

4. Conserved positions • Columns where all the sequences contain the same amino acids or nucleotides • Important for the function or structure VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGSSSNIGS--ITVNWYQQLPG LRLSCTGSGFIFSS--YAMYWYQQAPG LSLTCTGSGTSFDD-QYYSTWYQQPPG

5. Consensus Sequence • A consensus sequence holds the most frequent character of the alignment at each column

6. Profile Profile = PSSM – Position Specific Score (probability) Matrix

7. Alignment methods There is no available optimal solution for MSA – all methods are heuristics: • Progressive/hierarchical alignment (Clustal) • Iterative alignment (mafft, muscle)

8. Progressive alignment A B C D E First step: Compute the pairwise alignments for all against all (6 pairwise alignments) the similarities are stored in a table

9. A B C D E Second step: • Cluster the sequences to create a tree (guide tree): • represents the order in which pairs of sequences are to be aligned • similar sequences are neighbors in the tree • distant sequences are distant from each other in the tree The guide tree is imprecise and is NOT the tree which truly describes the relationship between the sequences!

10. A B C D E Third step: sequence sequence sequence sequence 1. Align the most similar (neighboring) pairs

11. A B C D E Third step: sequence profile 2. Align pairs of pairs

12. Third step: profile sequence A B 3. Align out group C D E • Main disadvantages: • sub-optimal tree topology • Misalignments resulting from globally aligning a • pair of sequences will only cause further deterioration

13. Iterative alignment A B C DE Pairwise distance table Iterate until the MSA doesn’t change (convergence) Guide tree MSA A B C D E

14. Searching for remote homologs • Sometimes BLAST isn’t enough. • Large protein family, and BLAST only gives close members. We want more distant members • PSI-BLAST • Profile HMMs

15. Profile HMM • Similar to PSI-BLAST: also uses a profile • Takes into account: • Dependence among sites (if site n is conserved, it is likely that site n+1 is conserved  part of a domain • The probability of a certain column in an alignment

16. PSI BLAST Vs. profile HMM PSI BLAST Profile HMM Less exact Faster More exact Slower

17. Case study: Using homology searching • The human kinome

18. Kinases and phosphatases

19. Multi-tasking enzymes • Signal transduction • Metabolism • Transcription • Cell-cycle • Differentiation • Function of nervous and immune system • … • And more

20. How many kinases in the human genome? • 1950’s, discovery of that reversible phosphorylation regulates the activity of glycogen phosphorylase • 1970’s, advent of cloning and sequencing produced a speculation that the vertebrate genome encodes as many as 1001 kinases

21. How many kinases in the human genome? • 2001 – human genome sequence … • As well – databases of Genbank, Swissprot, and dbEST • How can we find out how many kinases are out there?

22. The human kinome • In 2002, Manning, Whyte, Martinez, Hunter and Sudarsanam set out to: • Search and cross-reference all these databases for all kinases • Characterize all found kinases

23. ePKs and aPKs Eukaryotic protein kinase (majority) catalytic domain Atypical protein kinases Sequence homology of the catalytic domain; additional regulatory domains are non-homologous No sequence homology to ePKs; some aPK subfamilies have structural similarity to ePKs

24. The search • Several profiles were built:based on the catalytic domain of: (a) 70 known ePKs from yeast, worm, fly, and human with >50% identity in the ePK domain (b) each subfamily of known aPKs • HMM-profile searches and PSI-BLAST searches were performed

25. The results… • 478 apKs • 40 ePKs • Total of 518 kinases in the human genome (half of the prediction in the 1970’s)

26. Classifying the kinases • Classification based on the catalytic domain • Classification based on the regulatory domains 189 sub-families of kinases

27. Comparison to other species • 209 subfamilies of ePKs in human, worm, yeast and fly

28. The human genome has x2 kinases (in number) as fly or worm. Many are aPKs. • Most of them are receptor tyrosine kinases (RTKs) The human-expanded kinase families function predominantly in processes of the: • Nervous system • Immune system • Angiogenesis • Hemopoiesis

29. The discovery of new kinases: a new front for battling human diseases

30. Correlating with human diseases • 160 kinases mapped to amplicons seen in tumors • 80 kinases mapped to amplicons in other major illnesses • Usually kinases are over-expressed in cancer and other diseases

31. Correlating with human diseases • 6 kinase inhibitors have been approved till today for the use against cancer • >70 other inhibitors are in clinical trials