Simple Rearrangements

2. Simple Rearrangements

4. 1 2 3 9 10 8 4 7 5 6 Reversals • Blocks represent conserved genes. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

5. Reversals 1 2 3 9 10 8 4 7 5 6 1, 2, 3, -8, -7, -6, -5, -4, 9, 10 • Blocks represent conserved genes. • In the course of evolution or in a clinical context, blocks 1,…,10 could be misread as 1, 2, 3, -8, -7, -6, -5, -4, 9, 10.

6. Types of Rearrangements Reversal 1 2 3 4 5 6 1 2 -5 -4 -3 6 Translocation 1 2 3 45 6 1 2 6 4 5 3 Fusion 1 2 3 4 5 6 1 2 3 4 5 6 Fission

7. Sorting by reversals: 5 steps

8. Sorting by reversals: 4 steps

9. Sorting by reversals: 4 steps What is the reversal distance for this permutation? Can it be sorted in 3 steps?

10. From Signed to Unsigned Permutation (Continued) • Construct the breakpoint graph as usual • Notice the alternating cycles in the graph between every other vertex pair • Since these cycles came from the same signed vertex, we will not be performing any reversal on both pairs at the same time; therefore, these cycles can be removed from the graph 0 5 610 915 1612 117 814 1317 183 41 219 2022 21 23

11. Reversal Distance with Hurdles • Hurdles are obstacles in the genome rearrangement problem • They cause a higher number of required reversals for a permutation to transform into the identity permutation • Let h(π) be the number of hurdles in permutation π • Taking into account of hurdles, the following formula gives a tighter bound on reversal distance: d(π) ≥ n+1 – c(π) + h(π)

12. Median Problem Goal: find Mso that DAM+DBM+DCMis minimized NP hard for most metric distances

13. Genome Enumeration for Multichromosome Genomes Genome Enumeration For genomes on gene {1,2,3} 2 2 2 -2 -2 -2

14. Rearrangement Phylogeny

15. Compute A Given Tree (Start)

16. Compute A Given Tree (First Median)

17. Compute A Given Tree (Second Median)

18. Compute A Given Tree (Third Median)

19. Compute A Given Tree (After 1st Iteration)

20. Binary Encoding

21. MLBE Sequences

22. Experimental Results (Equal Content) 80% inversion, 20% transposition

23. An Example—New Genomes 1 2 3 4 5 6 7 8 9 10 1 -4 5 2 8 10 9 -7 -6 3 … 1 3 5 7 9 1 5 9 -7 3 …

24. Jackknifing Rate

25. Support Value Threshold - FP Up to 90% FP can be identified with 85% as the threshold

26. Jackknife Properties • Jackknifing is necessary and useful for gene order phylogeny, and a large number of errors can be identified • 40% jackknifing rate is reasonable • 85% is a conservative threshold, 75% can also be used • Low support branches should be examined in detail

27. Protein

28. In-silico Biochemistry • Online servers exist to determine many properties of your protein sequences • Molecular weight • Extinction coefficients • Half-life • It is also possible to simulate protease digestion • All these analysis programs are available on • www.expasy.ch

29. Analyzing Local Properties • Many local properties are important for the function of your protein • Hydrophobic regions are potential transmembrane domains • Coiled-coiled regions are potential protein-interaction domains • Hydrophilic stretches are potential loops • You can discover these regions • Using sliding-widow techniques (easy) • Using prediction methods such as hidden Markov Models (more sophisticated)

30. Sliding-window Techniques • Ideal for identifying strong signals • Very simple methods • Few artifacts • Not very sensitive • Use ProtScale on www.expasy.org • Make the window the same size as the feature you’re looking for

31. www.expasy.org/cgi-bin/protscale.pl

32. www.expasy.org/cgi-bin/protscale.pl

33. www.expasy.org/cgi-bin/protscale.pl

34. www.expasy.org/cgi-bin/protscale.pl Hphob. / Eisenberg

35. Transmembrane Domains • Discovering a transmembrane domain tells you a lot about your protein • Many important receptors have 7 transmembrane domains • Transmembrane segments can be found using ProtScale • The most accurate predictions come from using TMHMM

36. Using TMHMM • TMHMM is the best method for predicting transmembrane domains • TMHMM uses an HMM • Its principle is very different from that of ProtScale • TMHMM output is a prediction

37. TMHMM vs. ProtScale

38. >sp|P78588|FREL_CANAX Probable ferric reductase transmembrane component OS=Candida albicans GN=CFL1 PE=3 SV=1 MTESKFHAKYDKIQAEFKTNGTEYAKMTTKSSSGSKTSTSASKSSKSTGSSNASKSSTNA HGSNSSTSSTSSSSSKSGKGNSGTSTTETITTPLLIDYKKFTPYKDAYQMSNNNFNLSIN YGSGLLGYWAGILAIAIFANMIKKMFPSLTNNLSGSISNLFRKHLFLPATFRKKKAQEFS IGVYGFFDGLIPTRLETIIVVIFVVLTGLFSALHIHHVKDNPQYATKNAELGHLIADRTG ILGTFLIPLLILFGGRNNFLQWLTGWDFATFIMYHRWISRVDVLLIIVHAITFSVSDKAT GKYKNRMKRDFMIWGTVSTICGGFILFQAMLFFRRKCYEVFFLIHIVLVVFFVVGGYYHL ESQGYGDFMWAAIAVWAFDRVVRLGRIFFFGARKATVSIKGDDTLKIEVPKPKYWKSVAG GHAFIHFLKPTLFLQSHPFTFTTTESNDKIVLYAKIKNGITSNIAKYLSPLPGNTATIRV LVEGPYGEPSSAGRNCKNVVFVAGGNGIPGIYSECVDLAKKSKNQSIKLIWIIRHWKSLS WFTEELEYLKKTNVQSTIYVTQPQDCSGLECFEHDVSFEKKSDEKDSVESSQYSLISNIK QGLSHVEFIEGRPDISTQVEQEVKQADGAIGFVTCGHPAMVDELRFAVTQNLNVSKHRVE YHEQLQTWA Search with Accession number P78588 http://www.uniprot.org/uniprot/

39. www.cbs.dtu.dk/services/TMHMM-2.0

40. www.cbs.dtu.dk/services/TMHMM-2.0

41. Predicting Post-translational Modifications • Post-translational modifications often occur on similar motifs in different proteins • PROSITE is a database containing a list of known motifs, each associated with a function or a post-translational modification • You can search PROSITE by looking for each motif it contains in your protein (the server does that for you!) • PROSITE entries come with an extensive documentation on each function of the motif

42. Searching for PROSITE Patterns • Search your protein against PROSITE on ExPAsy • www.expasy.org/tools/scanprosite • PROSITE motifs are written as patterns • Short patterns are not very informative by themselves • They only indicate a possibility • Combine them with other information to draw a conclusion • Remember: Not everything is in PROSITE !

43. www.expasy.org/tools/scanprosite P12259

44. www.expasy.org/tools/scanprosite

45. Interpreting PROSITE Patterns • Check the pattern function: Is it compatible with the protein? • Sometimes patterns suggest nonexistent protein features • For instance : If you find a myristoylation pattern in a prokaryote, ignore it; prokaryotic proteins have no myristoylation ! • Short patterns are more informative if they are conserved across homologous sequences • In that case, you can build a multiple-sequence alignment • This slide shows an example

46. Patterns and Domains • Patterns are usually the most striking feature of the more general motifs (called domains) • Domains are less conserved than patterns but usually longer • In proteins, domain analysis is gradually replacing pattern analysis

47. Protein Domains • Proteins are usually made of domains • A domain is an autonomous folding unit • Domains are more than 50 amino acids long • It’s common to find these together: • A regulatory domain • A binding domain • A catalytic domain

48. Discovering Domains • Researchers discover domains by • Comparing proteins that have similar functions • Aligning those proteins • Identifying conserved segments • A domain is a multiple-sequence alignment formulated as a profile • For each column, a domain indicates which amino acid is more likely to occur

49. Domain Collections • Scientists have been discovering and characterizing protein domains for more than 20 years • 8 collections of domains have been established • Manual collections are very precise but small • Automatic collections are very extensive but less informative • These collections • Overlap • Have been assembled by different scientists • Have different strengths and weaknesses • We recommend using them all!

50. The Magnificent 8 • Pfam is the most extensive manual collection • Pfam is often used as a reference