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Clustering and Motif Discovery in Kinases of Yeast, Worm and Arabidopsis thaliana

Clustering and Motif Discovery in Kinases of Yeast, Worm and Arabidopsis thaliana. Sihui Zhao. Background – Kinase. Protein kinases play a pivotal role in the control of all cellular processes Cell proliferation, differentiation, adhesion, migration, metabolism and signal transduction

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Clustering and Motif Discovery in Kinases of Yeast, Worm and Arabidopsis thaliana

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  1. Clustering and Motif Discovery in Kinases of Yeast, Worm and Arabidopsis thaliana Sihui Zhao

  2. Background – Kinase • Protein kinases play a pivotal role in the control of all cellular processes • Cell proliferation, differentiation, adhesion, migration, metabolism and signal transduction • A kinase superfamily in each genome, ~2% of all sequences

  3. Background Structure of Catalytic Domain • Also called C-subunit • Conserved among protein kinase superfamily • Contains 250-300 residues • 12 subdomains

  4. Background Subdomains of C-subunit • Two pivital subdomains (based on PKA): • Subdomain I: Sequester ATP Gly-X-Gly-X-X-Gly-X-Val • Subdomain VIB: ‘Catalytic loop’ His-Arg-Asp-X-Lys-X-X-Asn

  5. Background Conserved Residues

  6. Background Motif • Motif is a locally conserved region • Conserved due to higher selection pressure compared to non-conserved regions • Importance to the biological function or structure

  7. Background Problem & Strategy in Motif Discovery • Motif discovery relies on either statistical or combinatorial pattern search techqniues • Problem: High noise compared to signal when facing huge number of sequences • Strategy: Clustering/classification used to find sequence families first to decrease the noise ratio

  8. Objectives • Cluster kinase sequences into different families • Find conserved motifs from sequence families

  9. Tools • Blast – Sequence alignment tool • ClustalW – Multiple alignment tool • HMMER – HMM-based package • BAG package – Sequence clustering package • BlockerMaker – Block/Motif discovery tool • LAMA – Alignment tool for Blocks • Perl

  10. Computational Framework–Outline • Collecting and clustering kinase sequences based on similarity • The iterative HMM search – To collect more kinases, especially remotely homologous sequences • Motif discovery – To find blocks from each cluster and merge blocks across multiple clusters

  11. Computational Framework Collecting and Clustering Sequences • Cluster kinase sequences Extract annotated kinase sequences All to all pairwise comparison Estimate best score for clustering Cluster sequences using BAG

  12. Computational Framework HMM Iterative Search • Collect more sequences for each cluster Multiple alignment using CLUSTALW Build HMM/Profile Search all 3 genomes Add hits to each cluster if any

  13. Computational Framework Motif Discovery • Find blocks and merge across multiple clusters Block discovery by BlockMaker All to all block comparison by LAMA Clustering blocks using BAG package Conserved sites detection

  14. Result • 963 kinase from ~45,000 sequences (~2%) • 159 clusters of kinase sequences containing 2 to 32 sequences each • 0 to ~1000 sequences added to each cluster after HMM iterative search

  15. Result • 71 sequence clusters sent to BlockMaker ID c51.seq-1 BLOCK AC c51.seq-1; distance from previous block=(79,120) DE similar to eukaryotic protein kinase domains BL EGL motif=[5,0,17] motomat=[1,1,-10] width=31 seqs=5 gi|3329644|gb|AAC ( 792) SNFNFEFHKDSLEILEPIGSGHFGVVRRGIL 99 gi|3329650|gb|AAC ( 154) YNPKYEVDLEKLEILEQLGDGQFGLVNRGLL 92 gi|3877967|emb|CA ( 836) YNNDYEIDPVNLEILNPIGSGHFGVVKKGLL 79 gi|3877968|emb|CA ( 842) YNEDYEIDLENLEILETLGSGQFGIVKKGYL 77 gi|3878749|emb|CA ( 129) YKKQYEIASENLENKSILGSGNFGVVRKGIL 100

  16. Result • 45 clusters of Blocks after LAMA comparison and BAG clustering

  17. Result Some Found Conserved Sites • Cluster 11, size 29 Subdomain I: G-X-G-X-X-G-X-V • Cluster 16, size 97 Subdomain VIB: H-R-D-X-K-X-X-N

  18. Result Some New Sites • Cluster 20, size=8 Alignment and motif • Known: Arg280 - assembly of catalytic core • Unknown: Cys, Trp, Pro • Cluster 31, size=13 Alignment and motif • Known: Asp220 - assembly of catalytic loop • Unknown: Gly, Thr, Tyr • Cluster 40, size=7 Alignment and motif • Known: Glu91 - positioning triphosphate group • Unknown: His, Pro

  19. Conclusion • This computational framework is successful • Especially when no preliminary information on huge amount of sequences • It’s efficient • Not completely automatic

  20. Conclusion • Kinases are clustered based on similarity, which provides a way to deduce the functions from other family members • Some new conserved sites are found, which might indicate the specificity of kinase functions

  21. Acknowledgement • Prof. Sun Kim • Prof. Mehmet Dalkilic • Dr. Irfan Gunduz

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