1. “Homology-enhanced probabilistic consistency” multiple sequence alignment : a case study on transmembrane protein Jia-Ming Chang 2013-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 Mol Biol 1994, 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: PRALINE TM : 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: PRALINE TM : 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 Do CB, Mahabhashyam MS, Brudno M, Batzoglou S: ProbCons: Probabilistic consistency- based multiple sequence alignment. Genome Res 2005, 15(2):330-340. Emission probabilities, which correspond to traditional substitution scores, are based on the BLOSUM62 matrix.

9. Probabilistic consistency transformation

10. Homology-extended probabilistic consistency New emission probabilities are like the following. where α m is the frequency with which residue m appears at position i and β n 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 α i, β j, and r k are the profile frequency.

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

13. Que1: how to build a profile? Database Size Searching parameters – E-value : most used, anything else??? 1.Matrix file : -M 2.Filter the query sequence for low-complexity subsequence : -F 3.Neighborhood word threshold : -f 4.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. UniRef50 TM UniRef90 TM UniRef100 TM UniProt TM Different database UniProt (release 15.15 – 2010) NCBI non-redundant (NR) UniRef50 UniRef90 UniRef100 keyword:"Transmembrane [KW-0812]"

17. Database Size Data SetNo. UniRef50-TM87,989 UniRef90-TM263,306 UniRef100-TM613,015 UniProt-TM818,635 UniRef503,077,464 UniRef906,544,144 UniRef1009,865,668 UniProt11,009,767 NCBI NR10,565,004 UniRef50 TM UniRef90 TM UniRef100 TM UniProt TM UniProt (release 15.15 – 2010) NCBI non-redundant (NR) UniRef50 UniRef90 UniRef100 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.

20. 10% more columns are correctly aligned when compared with PRALINE TM. 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.

21. 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).

22. 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.

23. 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.

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