Protein World SARA Amsterdam Tim Hulsen

Genome sequencing Since 1995: sequencing of complete ‘genomes’ (DNA): A/C/G/T order ACGTCATCGTAGCTAGCTAGTCGTACGTATG TGCAGTAGCATCGATCGATCAGCATGCATAC At this moment more than 80 genomes have been sequenced and published, of all kinds of organisms: –Animals –Plants –Fungi –Bacteria

Genomes  Proteins ‘Transcription’ and ‘translation’ of specific regions of the genome leads to proteins, consisting of twenty types of ‘amino acids’: ATG ACG CTG AGC TGC GGA CGT TGA -> TLSCGR Proteins are responsible for all kinds of life processes All the proteins that can be produced in an organism together are called the ‘proteome’ Sequence comparisons make possible the classification of proteins

Protein families e.g. The GPCR family: Sequence comparison helps in predicting the function of new proteins

Determining protein functions Function of 40-50% of the new proteins is unknown Understanding of protein functions and relationships is important for: –Study of fundamental biological processes –Drug design –Genetic engineering

Sequence comparison Smith-Waterman dynamic programming algorithm (1981): calculates similarity/distance between two sequences: Query ---PLIT-LETRESV- Subject NEQPKVTMLETRQTAD (bold=similar) Results in a SW-score that is a measure for how similar the two sequences are to each other Disadvantage: score is dependent of length After the alignments, the proteins are ‘clustered’ (divided into families) according to their similarity

Existent databases Domain-based clusterings: Prosite, Pfam, ProDom, Prints, Domo, Blocks Protein-based clusterings: ProtoMap, COGs, Systers, PIR, ClusTr Structural classifications: SCOP, CATH, FSSP Why should there be another database?

Another method Enhanced Smith-Waterman algorithm: Monte-Carlo evaluation (Lipman et al., 1984) How big is the chance that two sequences are similar but not related? One of the two sequences is randomized and recalculated (200 times). Randomization leads to sequences with the same length and the same composition, but different order Method leads to calculation of the Z-value: S(A,B) - µ Z(A,B) = σ

Advantages The obtained Z-value is a very reliable measure for sequence, compared to SW- score: –SW-score is dependent of length, Z-value is not –Amino acid bias does not affect the Z-value Independent of the database size Easier updating of the database, without a total recalculation

Disadvantage LOTS of calculation time needed, especially when all proteins in all proteomes are compared to each other (“all-against-all”)!  SARA

SARA calculation Proteomes of 82 organisms compared ‘all- against-all’ with the use of the Monte Carlo algorithm: more than 400,000 proteins! 21,600 CPU days (~520,000 CPU hours) = 21,600 PCs running parallel over 24 hours / 1 PC running for ~ 60 years Using supercomputer TERAS (1024-CPU SGI Origin 3800) at SARA: less than two months!

Parties involved Gene-IT (Paris, France) SARA (Amsterdam, the Netherlands) CMBI (Nijmegen, the Netherlands) Organon (Oss, the Netherlands) EBI (Hinxton, UK)

Supporting parties Financed by NCF, foundation in support of supercomputing Under the auspices of BioASP, the new Dutch knowledge and service center for Bioinformatics

Results available through BioASP Log in and click on links ‘Research’ and ‘Protein World’: 1 2

Results available through BioASP Organism selection screen:

Results available through BioASP Results screen:

Results available through BioASP Alignment screen:

Conclusions Currently the most comprehensive and most accurate data-set of protein comparisons A start for a maintainable and unique database of all proteins currently known A rich data-source for clustering, data- mining and orthology determination

Orthology determination Orthologs: genes/proteins in different species that derive from a common ancestor Orthologs often have the same function Interesting! Information from other species could help in annotating a protein

Thank you for your attention Any questions?