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Global Annotation of the Protein Kinase Family

Global Annotation of the Protein Kinase Family. Michael Gribskov University of California, San Diego. Signaling Cascades. Statistics. Arabidopsis 1028 putative kinase 58 Potentially alternatively spliced 82 % confirmed by full length cDNA Less than 100 experimentally investigated Rice

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Global Annotation of the Protein Kinase Family

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  1. Global Annotation of the Protein Kinase Family Michael Gribskov University of California, San Diego

  2. Signaling Cascades

  3. Statistics • Arabidopsis • 1028 putative kinase • 58 Potentially alternatively spliced • 82 % confirmed by full length cDNA • Less than 100 experimentally investigated • Rice • 1565 putative kinases • What are the functions of each protein kinase? • Functional groupings • Substrate prediction • Pathway analysis and modeling

  4. Targets • Protein kinase • Protein phosphatase • Membrane transporters • Proteasome complex

  5. Some Receptor Kinases Class I (EGF receptor) Class II (Insulin receptor) Class III (FGF receptor)

  6. Requirements for Functional Clustering • Must handle very large number of objects (over 1200 for plants, over 9000 for all species) • Must deal sensibly with paralogs from functional point of view • Must be based on entire sequence, not just kinase catalytic domain • Must be tolerant to sequence errors and omissions

  7. Species B Species A Orthology vs Paralogy • Relationships between genes in multigene families are complex • Multiple genes may exist before speciation • Genes may be lost and replaced along lineages • “Function space” must be filled

  8. Clustering

  9. Clustering

  10. Clustering/Classification Maximum linkage

  11. Clustering/Classification • Pairwise distances • All-against-all BLAST • Uses entire sequence • Alignments not required • Longer matches, i.e. more domains, give better score

  12. Basic Approach • Maximum linkage clustering up to “natural” limit • Recalculate average distances between groups • Repeat until tree is complete

  13. Complete Kinase Clustering

  14. Statistics • Class 1: RLKs (transmembrane) and RLCKs • Class 2: “Raf-like” • Class 3: Casein Kinase and CLK • Class 4: Non-TM, Non-Receptor

  15. BLASTDistance Entire Sequence

  16. BLASTDistance Non-KinaseDomain

  17. Yeast Signaling (MAPK)

  18. Validating Transgenomic Predictions

  19. GIN4/ERC47/CLA6/D9719.13/YDR507C KCC4/YCL024W HSL1/(SEL2)/NIK1/YKL453/YKL101W SNF1/CAT1/GLC2/CCR1/PAS14/HAF3/D8035.20/YDR477W At5g39440 At3g29160/AKIN11 At3g01090/AKIN10 At5g58380 At5g07070 At5g01810 At5g45820 At4g30960 At5g25110 At5g10930 At2g25090 At2g30360 At5g01820/AtSR1 At2g38490 At3g23000/AtSR2 At4g14580 At1g01140 At1g30270 At2g26980 At4g24400 At5g35410/SOS2 At1g48260 At3g17510 At5g57630 At1g60940 At1g10940 At5g08590 At5g63650 At2g23030 E=10-80 At1g78290 At3g50500 At5g66880 At4g33950 At4g40010 At1g29230 At2g34180 At4g18700 At5g45810 KIN1/YD9727.17/YDR122W KIN2/L8004.3/L2546/YLR096W KIN4/KIN31/(KIN3)/O5220/YOR233W YPL141C/LPI5 YPL150W/P2597 50 See Fig. 2 SnRK • At AKIN10 and AKin11 • Rescue yeast SNF1 deletion • Functional homolog

  20. MAPK

  21. MEME PSSM

  22. PPC4.2.6 MEME Motifs

  23. Summary • Functional groups by clustering • Functional assignment by transgenomic comparison • Directed search for functional motifs by motif comparison • Construction of public data resources

  24. Michael Gribskov Fariba Fana Degeng Wang Sheila Podell Tobey Tam * Jason Tchieu * Hannes Niedner Douglas Smith Guangfa Zhang * Jeff Harper Major Contributors Catherine Chan Alice Harmon Estelle Hrabak David Kerk Shinhan Shiu Bioinformatics Group

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