Genomics in Drug Discovery Organon Oss 2005 08

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Genomics in Drug Discovery @ Organon, Oss 2005 -08 -22 Tim Hulsen

Genomics in Drug Discovery @ Organon, Oss 2005 -08 -22 Tim Hulsen

Introduction • Proteins are vital to life: involved in all kinds of life processes

Introduction • Proteins are vital to life: involved in all kinds of life processes • Understanding protein functions and relationships is very important for drug design • Currently, the molecular function of about 40% of the proteins is unknown

Introduction Availability of fully sequenced genomes gives us a wealth of information: currently more

Introduction Availability of fully sequenced genomes gives us a wealth of information: currently more than 15 eukaryotic genomes have nearly been completely sequenced, over 148 microbial genomes and over 1000 viruses. Determine protein function by using different in silico techniques: • sequence comparison to known protein sequences • sequence clustering with proteins which have the same or similar function

Genomics @ Organon: The Protein World project • All-against-all sequence comparison of complete proteomes

Genomics @ Organon: The Protein World project • All-against-all sequence comparison of complete proteomes from 145 species • Smith-Waterman algorithm + Z-value (Monte-Carlo statistics)

Protein World and its ambitions Build and maintain a sequence similarity repository of all

Protein World and its ambitions Build and maintain a sequence similarity repository of all complete proteomes and aligning it with “omics” research in the Netherlands Classification of all proteins into groups of related proteins • A phylogenetic repository • Annotation of new sequences • Mining protein families • Identification of genes common / specific to (groups of) species

Applications of Protein World Structural properties • Protein comparison coupled to structure related databases

Applications of Protein World Structural properties • Protein comparison coupled to structure related databases (PDB, SCOP, etc. ) Systems biology • Connecting PW to other databases (pathways, protein interactions, literature etc. ) Orthology • Annotation of new proteins • To predict discrepancies and similarities between species

Orthology • Describes “the evolutionary relationship between homologous genes whose independent evolution reflects a

Orthology • Describes “the evolutionary relationship between homologous genes whose independent evolution reflects a speciation event” (Fitch, 1970)

Protein World & Drug Discovery • Orthologies can be used to transfer function of

Protein World & Drug Discovery • Orthologies can be used to transfer function of proteins in model organisms (mice, rats, dogs, etc. ) to humans • Drugs tested on model organisms can have different effects in humans. Why? • Could be explained by looking at proteins in drug pathways and their orthologs • Example: trypsin inhibition pathway

Trypsin inhibition pathway (1) • Organon: thrombin inhibitors • Needed to stop thrombosis (blood

Trypsin inhibition pathway (1) • Organon: thrombin inhibitors • Needed to stop thrombosis (blood clotting) • Thrombin inhibitor on the market: (xi)melagatran, sold as Exanta (Astra. Zeneca) • Proven to be better than warfarin, but …

Trypsin inhibition pathway (2) • Side effect of thrombin inhibitors: inhibition of trypsin •

Trypsin inhibition pathway (2) • Side effect of thrombin inhibitors: inhibition of trypsin • Trypsin inhibition -> rise in cholecystokinin (CCK) levels -> stimulation of pancreas -> pancreatic tumors • Difficult to test in model organisms: – Rat: very strong CCK response – Mouse: weak CCK response – Human: almost no CCK response

Trypsin inhibition pathway (3)

Trypsin inhibition pathway (3)

Trypsin inhibition pathway (4) Ortholog identification methods: 1. Using functional annotation (SPTr. EMBL): 2.

Trypsin inhibition pathway (4) Ortholog identification methods: 1. Using functional annotation (SPTr. EMBL): 2. Best Bidirectional Hit (BBH) one-to-one relationships 3. Phylo. Genetic Trees (PGT) many-to-many relationships

Best Bidirectional Hit (BBH) • Very easy and quick • Human protein (1) SW

Best Bidirectional Hit (BBH) • Very easy and quick • Human protein (1) SW best hit in mouse/rat (2) • Mouse/rat protein (2) SW best hit in human (3) • If 3 equals 1, the human and mouse/rat protein are considered to be orthologs

Phylo. Genetic Tree (PGT) PROTEOME Human PROTEOMES All SELECTION OF HOMOLOGS eukaryotic LIST Hs-Mm

Phylo. Genetic Tree (PGT) PROTEOME Human PROTEOMES All SELECTION OF HOMOLOGS eukaryotic LIST Hs-Mm pairs Hs-Rn pairs proteomes ALIGNMENTS AND TREES TREE SCANNING PHYLOME Z>20 RH>0. 5*QL ~25, 000 groups

Trypsin inhibition pathway (5) Mm – Hs – Rn - by annotation - BBH

Trypsin inhibition pathway (5) Mm – Hs – Rn - by annotation - BBH - PGT

Trypsin inhibition pathway (6) • PGT method: in some cases too many orthologous relationships,

Trypsin inhibition pathway (6) • PGT method: in some cases too many orthologous relationships, especially for trypsin (73 in mouse and 62 in rat!) • BBH method seems to be more usable for this study, but still not gives an explanation for the differences in CCK levels • Our problem (different CCK responses in Human, Mouse and Rat) cannot be solved only by orthology identification • Combine ortholog analysis with other data • Focus on the molecules that are most likely to be responsible for these differences: CCK and trypsin

Trypsin inhibition pathway (7) • Current activities: – Take a better look at regulation:

Trypsin inhibition pathway (7) • Current activities: – Take a better look at regulation: promoter detection? – Use expression data? – Structural explanation? Modelling of interactions between the involved molecules

Possible student projects • Orthology case study: explain differences between humans and model organisms

Possible student projects • Orthology case study: explain differences between humans and model organisms (like example of trypsin inhibition pathway) • Chicken project (in collaboration with Wageningen University): comparison of immune system in chickens to i. s. in humans and other vertebrates • Cluster algorithms

People • • Peter Groenen Wilco Fleuren Tim Hulsen Others @ MDI • Students?

People • • Peter Groenen Wilco Fleuren Tim Hulsen Others @ MDI • Students?