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The CMBI: Bioinformatics

The CMBI: Bioinformatics. Content Bioinformatics Bioinformatics @CMBI Bioinformatics tools & databases Hanka Venselaar CMBI UMC Radboud February 2009 h.venselaar @ cmbi.ru.nl. What is bioinformatics?.

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The CMBI: Bioinformatics

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  1. The CMBI: Bioinformatics Content Bioinformatics Bioinformatics@CMBI Bioinformatics tools & databases HankaVenselaar CMBI UMC Radboud February 2009 h.venselaar@cmbi.ru.nl

  2. What is bioinformatics? • Bioinformatics is the use of computers in solving information problems in the life sciences • You are "doing bioinformatics" when you use computers to store, retrieve, analyze or predict the sequence, function and/or structure of biomolecules. Bioinformatics

  3. Human genome, great expectations Data ≠ Knowledge, insight !!! Bioinformatics

  4. Why do we need Bioinformatics? Flood of biological data: • DNA-sequences (genomes) • protein sequences and structures • gene expression profiles (transcriptomics) • cellular protein profiles (proteomics) • cellular metabolite profiles (metabolomics) We want to : • collect and store the data • integrate, analyze, compare and mine the data • predict genes, protein function and protein structure • predict physiology (models, mechanisms, pathways) • understand how a whole cell works Bioinformatics

  5. A large fraction of the human genes has an unknown function (Science, 2001) Bioinformatics

  6. What is protein function? Genomic context Homology Bioinformatics

  7. How can we predict function of proteins? The importance of sequence similarity and sequence alignment Similar sequences have: • A similar evolutionary origin • A similar function • A similar 3D structure Compare with database of proteins BLAST “similar sequence with known function. E.g. proteine kinase” “new, unknown protein” Extrapolate the function Bioinformatics

  8. CMBI - Centre for Molecular and Biomolecular Informatics • Dutch national centre for computational molecular sciences research • Research groups • Comparative Genomics (Huynen) • Bacterial Genomics (Siezen) • Computational Drug Design (De Vlieg) • Bioinformatics of Macromolecular Structures (Vriend) • Training & Education • MSc, PhD and PostDoc programmes • International workshops • Hotel Bioinformatica • High school courses • Computational facilities, databases, and software packages via (inter-)national service platforms (NBIC, EBI, etc) • NBIC: National BioInformatics Centre. Bioinformatics @CMBI

  9. CDD Applications Exciting real life problems ‘wet’ validation Academic Research New scientific approaches Training & education Computational Drug Discovery (CDD) Group • Head: Prof. Jacob de Vlieg • Key goalDevelop molecular modeling and computer-based simulation techniques for structure-based drug design, translational medicine and protein family based approaches to design and identify drug-like compounds • Key Research Fields • Structural bioinformatics for drug design • Bioinformatics for genomics (microarray analysis, text mining, etc) • Translational medicine informatics Bridging academic research and applied genomics Bioinformatics @CMBI

  10. Examples of CDD Projects • Exploiting Structural Genomics Information To Incorporate Protein Flexibility In Drug Design • Protein knowledge building through comparative genomics and data integration • In silico studies on p63 as a new drug-target protein Bioinformatics @CMBI

  11. International Computational Drug Discovery Course • Course covers the entire research pipeline from genomics and proteomics in target discovery to Structure Based Drug Design and QSAR in drug optimization. • Lectures and practicals • 2 week course • June/July 2009 • www.cmbi.ru.nl/ICDD2008 Bioinformatics @CMBI

  12. lactococcus listeria Bacterial Genomics Group • Head: Prof Roland Siezen • Research interest: Biological questions in the interest of Dutch Food Industry • How can we improve: • fermentation • safety • health • Micro-organisms studied: Gram-positive food bacteria: • lactic acid bacteria (Lactococcus, Lactobacillus) • spoilage bacteria (Listeria, Clostridium, Bacillus cereus) Bioinformatics @CMBI

  13. Bacterial Genomics:from sequence to predicted function Key research fields: • Genome sequencing and interpretation • Network reconstruction and analysis • Systems biology, dynamic modelling A virtual cell:overview of predicted pathways Raw sequence data: 2 to 5 million nucleotides AAACACTTAGACAATCAATATAAAGATGAAGTGAACGCTCTTAAAGAGAAGTTGGAAAACTTGCAGGAACAAATCAAAGATCAAAAAAGGATAGAAGAACAAGAAAAACCACAAACACTTAGACAATCAATATAAAGATGAAGTGAACGCTCTTAAAGAGAAGTTGGAAAACTTGCAGGAACAAATCAAAGATCAAAAAAGGATAGAAGAACAAGAAAAACCACAAACACTTAGACAATCAATATAAAGATGAAGTGAACGCTCTTAAAGAGAAGTTGGAAAACTTGCAGGAACAAATCAAAGATCAAAAAAGGATAGAAGAACAAGAAAAACCACAAACACTTAGACAATCAATATAAAGATGAAGTGAACGCTCTTAAAGAGAAGTTGGAAAACTTGCAGGAACAAATCAAAGATCAAAAAAGGATAGAAGAACAAGAAAAACCACAAACACTTAGACAATCAATATAAAGATGAAGTGAACGCTCTTAAAGAGAAGTTGGAAAACTTGCAGGAA Bioinformatics @CMBI

  14. Bacterial Genomics:Example Differential NF-κB pathways induction by Lactobacillus plantarum in the duodenum of healthy humans correlating with immune tolerance Peter van Baarlen et al., PNAS, Febr 3, 2009 Bioinformatics @CMBI

  15. Comparative Genomics Group • Head: Prof. Martijn Huynen • Research Focus: • How do the proteins encoded in genomes interact with each other to produce cells and phenotypes ? • To predict such functional interactions between proteins as there exist e.g. in metabolic pathways, signalling pathways or protein complexes A genome is more than the sum of its genes -> Use “genomic context” for function prediction Types of genomic context: Gene fusion/fission Chromosomal location Gene order/neighbourhood Co-evolution Co-expression Bioinformatics @CMBI

  16. Turning data into knowledge Research topics: • Develop computational genomics techniques that exploit the information in sequenced genomes and functional genomics data • Make testable predictions about pathways and the functions of proteins therein. • Evolution of the eukaryotic cell and in the origin and evolution of organelles like the mitochondria and the peroxisomes Education: • Comparative Genomics Course, 3 EC, April 2009 Comparativegenomics Prediction of protein function, pathways Bioinformatics @CMBI

  17. FrataxinExample • Frataxin is a well-known disease gene (Friedreich's ataxia) whose function has remained elusive despite more than six years of intensive experimental research. • Using computational genomics we have shown that frataxin has co-evolved with hscA and hscB and is likely involved in iron-sulfur cluster assembly in conjunction with the co-chaperone HscB/JAC1. Prediction Confirmation Bioinformatics @CMBI

  18. Bioinformatics of macromolecular structures • Head: Prof. Gert Vriend • Research Focus: Understanding proteins (and their environment) • Proteins are the core of life, they do all the work, and they give you feelings, contact with the outside world, etc. • Proteins, therefore, are the most important molecules on earth. • We want to understand life; why are we what we are, why do we do what we do, how come you can think what you think? Bioinformatics @CMBI

  19. Bioinformatics of macromolecular structures Research topics Vriend group • Homology modeling technology and applications • Application of bioinformatics in medical research (Hanka Venselaar) • Structure validation and structure determination improvement • Molecular class specific information systems (e.g. GPCRDB & NucleaRDB) • Data mining • WHAT IF molecular modelling and visualization software Bioinformatics @CMBI

  20. Homology Modeling Hearing loss DFNB63: MGTPWRKRKGIAGPGLPDLSCALVLQPRAQVGTMSPAIALAFLPLVVTLLVRYRHYFRLLVRTVLLRSLRDCLSGLRIEERAFSYVLTHALPGDPGHILTTLDHWSSRCEYLSHMGPVKGQILMRLVEEKAPACVLELGTYCGYSTLLIARALPPGGRLLTVERDPRTAAVAEKLIRLAGFDEHMVELIVGSSEDVIPCLRTQYQLSRADLVLLAHRPRCYLRDLQLLEAHALLPAGATVLADHVLFPGAPRFLQYAKSCGRYRCRLHHTGLPDFPAIKDGIAQLTYAGPG Homology modeling: Prediction of 3D structure based upon a highly similar structure ? Unknown structure Bioinformatics @CMBI

  21. NSDSECPLSHDG || || | || NSYPGCPSSYDG NSDSECPLSHDG ? Alignment of model and template sequence Unknown structure Known structure Back bone copied Homology Modeling Prediction of 3D structure based upon a highly similar structure Model! Add sidechains, Molecular Dynamics simulation on model Copy backbone and conserved residues Known structure Bioinformatics @CMBI

  22. Structure! Homology Modeling DFNB63: MGTPWRKRKGIAGPGLPDLSCALVLQPRAQVGTMSPAIALAFLPLVVTLLVRYRHYFRLLVRTVLLRSLRDCLSGLRIEERAFSYVLTHALPGDPGHILTTLDHWSSRCEYLSHMGPVKGQILMRLVEEKAPACVLELGTYCGYSTLLIARALPPGGRLLTVERDPRTAAVAEKLIRLAGFDEHMVELIVGSSEDVIPCLRTQYQLSRADLVLLAHRPRCYLRDLQLLEAHALLPAGATVLADHVLFPGAPRFLQYAKSCGRYRCRLHHTGLPDFPAIKDGIAQLTYAGPG Hearing loss Bioinformatics @CMBI

  23. Homology Modeling Mutations: • Arginine 81 -> Glutamic acid • Glutamic acid 110 -> Lysine Saltbridge between Arginine and Glutamic acid is lost in both cases Bioinformatics @CMBI

  24. Homology Modeling Mutation: • Tryptophan 105 -> Arginine Hydrophobic contacts from the Tryptophan are lost, introduction of an hydrophilic and charged residue Bioinformatics @CMBI

  25. Homology Modeling The three mutated residues are all important for the correct positioning of Tyrosine 111 Tyrosine 111 is important for substrate binding Ahmed et al., Mutations of LRTOMT, a fusion gene with alternative reading frames, cause nonsyndromic deafness in humans. Nat Genet. 2008 Nov;40(11):1335-40. Interested? Contact Hanka Venselaar (h.venselaar@cmbi.ru.nl) Bioinformatics @CMBI

  26. Hotel functions Temporary housing, teaching and supervision of experimentalists for data analysis at the CMBI Centralization of UMC-wide bioinformaticians Shared (weekly) seminars of CMBI with ‘inhouse bioinformaticians’ Collaboration/advice in acquiring grants with a Bioinformatics aspect Hotel Bioinformatica Interested? Contact Martijn Huynen (m.huynen@cmbi.ru.nl) Bioinformatics @CMBI

  27. Bioinformatics data types mRNA expression profiles MS data Large amount of data Growing very very fast Heterogeneous data types Bioinformatics Tools & Databases

  28. Biological Databases • Information is the core of bioinformatics • Literally thousands of databases exist that are relevant for biology, medicine, and/or chemistry Bioinformatics Tools & Databases

  29. Important records in SwissProt/UniProt (1) Bioinformatics Tools & Databases

  30. Cross references Direct hyperlinks to: EMBL PDB OMIM, InterPro etc. etc. Features post-translational modifications signal peptides binding sites, enzyme active sites domains, disulfide bridges etc. etc. Important records in SwissProt/UniProt (2) Bioinformatics Tools & Databases

  31. Protein Databank & Structure Visualization • PDB structures have a unique identifier, the PDB Code:4 digits (often 1 digit & 3 letters, e.g. 1CRN). • Download PDB structures, give correct file extension: 1CRN.pdb • Structures from PDB can directly be visualized with: • Yasara (www.yasara.org) • SwissPDBViewer (http://spdbv.vital-it.ch/) • Protein Explorer (http://www.umass.edu/microbio/rasmol/) • Cn3D (http://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml) Bioinformatics Tools & Databases

  32. OMIM Database OMIM - Online Mendelian Inheritance in Man • a large, searchable, current database of human genes, genetic traits, and hereditary disorders • contains information on all known mendelian disorders and over 12,000 genes • focuses on the relationship between phenotype and genotype Bioinformatics Tools & Databases

  33. Browsing genomes NCBI UCSC http://genome.ucsc.edu/ Only eukaryotic genomes Ensembl http://www.ensembl.org/ Bioinformatics Tools & Databases

  34. Sequence Retrieval with MRS (1) Google = Thé best generic search and retrieval system MRS = Maarten’s Retrieval System (http://mrs.cmbi.ru.nl ) MRS is the Google of the biological database world Search engine (like Google) Input/Query = word(s) Output = entry/entries from database Searching is very intuitive: • Select database(s) of choice • Formulate your query • Hit “Search” • The result is a “query set” or “hitlist” • Analyze the results Bioinformatics Tools & Databases

  35. Sequence Retrieval with MRS (2) Select database Formulate query.But think about your query first!! MRS hitlist Bioinformatics Tools & Databases

  36. BLAST and CLUSTAL with MRS Blast brings you to the MRS-page from which you can do Blast searches. Blast results brings you to the page where MRS stores your Blast results of the current session. Clustal brings you to the MRS page from which you can do Clustal sequence alignments. Bioinformatics Tools & Databases

  37. Your Exercise Today • The practicum: FAMILIAL VISCERAL AMYLOIDOSIS • Today for PhD students • Friday (13:00) for MMD students •  CMBI, Course room, ground floor NCMLS • You will study Lysozyme: • Protein • Gene • Mutations causing familial visceral amyloidosis • 3D structure • HAVE FUN!! Bioinformatics Tools & Databases

  38. The Practicum You can find the practicum at http://swift.cmbi.ru.nl/teach/lyso/ Work with MRS Work with Yasara Read the text carefully User login = c(your pc number) f.e c07 User password = t0psp0rt (with zero’s) The program Yasara is on your desktop

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