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Jia-Ming Chang 2013-July-09

“Homology-enhanced probabilistic consistency” multiple sequence alignment : a case study on transmembrane protein. Jia-Ming Chang 2013-July-09.

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Jia-Ming Chang 2013-July-09

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  1. “Homology-enhanced probabilistic consistency” multiple sequence alignment : a case study on transmembrane protein Jia-Ming Chang2013-July-09 Chang, J-M, P Di Tommaso, J-Fß Taly, C Notredame. 2012. Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee. BMC Bioinformatics 13.

  2. Transmembrane protein Membrane proteins are likely to constitute 20-30% of all ORFs contained in genomes. Odorant receptors Richard Benton, “Eppendorf winner. Evolution and revolution in odor detection,” Science (New York, N.Y.) 326, no. 5951 (October 16, 2009): 382-383.

  3. Transmembrane protein multiple sequence alignment • 1994 first address alignment for transmembrane proteins • Cserzo M, Bernassau JM, Simon I, Maigret B: New alignment strategy for transmembrane proteins. J MolBiol1994, 243(3):388-396. • Few multiple sequence alignment software till now => 3 • Shafrir Y, Guy HR: STAM: simple transmembrane alignment method. Bioinformatics 2004, 20(5):758-769. • Forrest LR, Tang CL, Honig B: On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys J 2006, 91(2):508-517. • Pirovano W, Feenstra KA, Heringa J: PRALINETM: a strategy for improved multiple alignment of transmembrane proteins. Bioinformatics 2008, 24(4):492-497.

  4. BAliBASE 2.0 reference 7 • Pirovano W, Feenstra KA, Heringa J: PRALINETM: a strategy for improved multiple alignment of transmembrane proteins. Bioinformatics 2008, 24(4):492-497.

  5. We need an accurate Transmembrane MSA!

  6. Homology-extended Simossis VA, Kleinjung J, Heringa J: Homology-extended sequence alignment. Nucleic Acids Res 2005, 33(3):816-824.

  7. Homology-extended Simossis VA, Kleinjung J, Heringa J: Homology-extended sequence alignment. Nucleic Acids Res 2005, 33(3):816-824.

  8. Pair-hidden Markov Model Emission probabilities, which correspond to traditional substitution scores, are based on the BLOSUM62 matrix. Do CB, Mahabhashyam MS, Brudno M, Batzoglou S: ProbCons: Probabilistic consistency-based multiple sequence alignment. Genome Res 2005, 15(2):330-340.

  9. Probabilistic consistency transformation

  10. Homology-extended probabilistic consistency New emission probabilities are like the following. where αm is the frequency with which residue m appears at position i and βn is the frequency with which residue n appears at position j; p(A.A.m, A.A.n) is the original emission probabilities in ProbCons.

  11. Homology-extended probabilistic consistency where αi , βj, andrkare the profile frequency.

  12. Homology-extended Que1: how to build a profile? Que2: how to score profiles? Simossis VA, Kleinjung J, Heringa J: Homology-extended sequence alignment. Nucleic Acids Res 2005, 33(3):816-824.

  13. Que1: how to build a profile? • Database Size • Searching parameters • E-value : most used, anything else??? Matrix file : -M Filter the query sequence for low-complexity subsequence : -F Neighborhood word threshold : -f Truncates the report to number of alignments: -b

  14. Word hit & Neighborhood

  15. Searching parameters • Fast, Insensitive search • High percent identity • blastp –F “m S” –f 999 –M BLOSUM80 –G 9 –E 2 –e 1e-5 • Slow, Sensitive search • Increase sensitivity, decrease specificity • blastp –F “m S” –f 9 –M BLOSUM45 –e 100 –b 10000 –v 10000 • Book “BLAST”, page 146, 147

  16. Different database NCBI non-redundant (NR) UniProt(release 15.15 – 2010) UniRef50 UniRef90 UniRef100 UniRef50TM UniRef90TM UniRef100TM UniProtTM keyword:"Transmembrane [KW-0812]"

  17. Database Size NCBI non-redundant (NR) UniProt(release 15.15 – 2010) UniRef50 UniRef90 UniRef100 UniRef50TM UniRef90TM UniRef100TM UniProtTM keyword:"Transmembrane [KW-0812]"

  18. Performance comparison of different database sizes for the BAliBASE2-ref7. UniRef50-TM contains about 100 times fewer sequences than the full UniProt. The level accuracy is comparable and even superior to that achieved with the default PSI-Coffee while the CPU time requirements are dramatically decreased by a factor 10.

  19. 10% more columns are correctly aligned when compared with PRALINETM. The rows, Pairs and Cols, denote the sum of corrected aligned pairs and columns, respectively. The number of pairs and columns in the reference alignments are 3,294,102 and 1,781, respectively.

  20. BAliBASE 3.0 The performance of other methods are from Rausch et al. The SP and TC scores of full-length sequences are evaluated by core blocks (by xml).

  21. Que2: how to score profiles? Edgar RC, Sjolander K: A comparison of scoring functions for protein sequence profile alignment. Bioinformatics 2004, 20(8):1301-1308.

  22. Prediction mode : –template_file PSITM • Output : -output tm_html This output was obtained on Or94b of D. melanogaster and its orthologs of other Drosophlia species. Notably, the predicted topology of the Or94b set is consistent with the Benton et al.’s conclusion.

  23. http://tcoffee.crg.cat/tmcoffee Paolo Di Tommaso

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