1 / 56

CS177 Lecture 8 Bioinformatics Databases (and genetic diseases)

CS177 Lecture 8 Bioinformatics Databases (and genetic diseases). Tom Madej 11.01.04. Lecture overview. Very brief overview of on-line databases. Formulating queries in Entrez. Example: Molecular biology of diseases. Bioinformatics Resources.

nerina
Télécharger la présentation

CS177 Lecture 8 Bioinformatics Databases (and genetic diseases)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS177 Lecture 8Bioinformatics Databases (and genetic diseases) Tom Madej 11.01.04

  2. Lecture overview • Very brief overview of on-line databases. • Formulating queries in Entrez. • Example: Molecular biology of diseases.

  3. Bioinformatics Resources • Reference: Chapter 3 in Sequence – Evolution –Function, E.V. Koonin and M.Y. Galperin, Kluwer Academic 2003. • Available on the NCBI Bookshelf.

  4. Sequence Databases • GenBank, EMBL, DDBJ; archival (International Nucleotide Sequence Database Collaboration); sequences have a common accession • SWISS-PROT curated, non-redundant, entries hyperlinked e.g. to PubMed; TrEMBL entries not yet ready for SWISS-PROT • Motifs: PROSITE, BLOCKS, PRINTS • Domains: Pfam, SMART, ProDOM, COGs (NCBI) • Motifs/domains: InterPro, CDD (NCBI)

  5. More databases… • Structure: PDB/RCSB, MMDB (NCBI), SCOP, CATH, FSSP • Organism-specific: e.g. E. coli, B. subtilis, Synechocystis sp. (bacteria); yeast (unicellular eukaryote); Arabidopsis, C. Elegans (WormBase), Fruitfly, Human • COGs clusters of orthologous groups; KEGG biochemical pathways; BIND protein-protein interactions; ENZYME; LIGAND enzymes and their substrates • PubChem (NCBI) chemical substances

  6. PubChem (new)

  7. PubMed OMIM PubMed Central Journals 3D Domains Books Structure Sequence/Structure Protein Taxonomy CDD/CDART Entrez Genome Sequence/Structure Protein Nucleotide Sequence Genome UniSTS HomoloGene SNP UniGene Gene GEO/GDS Nucleotide PopSet The(ever expanding)Entrez System NLM Catalog PubChem Compounds BioAssays Substances Literature Organism Expression HomoloGene Gene

  8. PubMed abstracts Taxonomy Genomes Nucleotide sequences Links Between and Within Nodes Word weight Computational 3 -D Structures 3-D Structure VAST Phylogeny Computational Protein sequences BLAST BLAST Computational Computational

  9. Pubmed: Computation of Related Articles The neighbors of a document are those documents in the database that are the most similar to it. The similarity between documents is measured by the words they have in common, with some adjustment for document lengths. The value of a term is dependent on Global and Local types of information: G - the number of different documents in the database that contain the term; L - the number of times the term occurs in a particular document;

  10. Global and local weights • The global weight of a term is greater for the less frequent terms. The presence of a term that occurred in most of the documents would really tell one very little about a document. • The local weight of a term is the measure of its importance in a particular document. Generally, the more frequent a term is within a document, the more important it is in representing the content of that document.

  11. How we define similar documents • The similarity between two documents is computed by adding up the weights (local wt1 × local wt2 × global wt) of all of the terms the two documents have in common. All results are ranked and the most similar documents become Related Articles

  12. Entrez database queries • The databases are indexed by different sets of terms. • You can get to a particular DB by selecting it and then entering a “null” query. • The “Preview/Index” tab displays the index terms and can be used to formulate a query (if you can’t remember the syntax for the index). • “Limits” can be used e.g. to select publications in a specified time range. • “Details” shows the interpretation of the query.

  13. Exercises! • How many protein structures are there that include DNA and are from bacteria? • In PubMed, how many articles are there from the journal Science and have “Alzheimer” in the title or abstract, and “amyloid beta” anywhere? How many since the year 2000? • Notice that the results are not 100% accurate! • In 3D Domains, how many domains are there with no more than two helices and 8 to 10 strands and are from the mouse?

  14. Investigating genetic diseases • Now we will see examples of how bioinformatics databases can be used to investigate genetic diseases.

  15. Gene variants that can affect protein function • Mutation to a stop codon; truncates the protein product! • Insertion/deletion of multiple bases; changes the sequence of amino acid residues. • Single point change could alter folding properties of the protein. • Single point change could affect the active site of the protein. • Single point change could affect an interaction site with another molecule.

  16. Lodish et al. Molecular Cell Biology, W.H. Freeman 2000

  17. Sickle cell anemia • The first “molecular disease”, i.e. the first genetic disease with a known molecular basis. • The most common variant is caused by a Glu6Val mutation in the Hemoglobin β-chain (HbS). However, there are 100’s of other mutations that can cause this (OMIM lists 524 variants!). • This mutation causes the hemoglobin to polymerize, in turn the red blood cells form sickle shapes and clump together under low oxygen conditions or high hemoglobin concentrations. • Confers some resistance to malaria, by inhibiting parasite growth.

  18. NHLBI web site

  19. Exercise! • Find an appropriate Hemoglobin structure and view it in Cn3D. • Check the position of the Glu6Val mutation.

  20. P53 tumor suppressor protein • Li-Fraumeni syndrome; only one functional copy of p53 predisposes to cancer. • Mutations in p53 are found in most tumor types. • p53 binds to DNA and stimulates another gene to produce p21, which binds to another protein cdk2. This prevents the cell from progressing thru the cell cycle.

  21. G. Giglia-Mari, A. Sarasi, Hum. Mutat. (2003) 21 217-228.

  22. Exercise! • Use Cn3D to investigate the binding of p53 to DNA. • Formulate a query for Structure that will require the DNA molecules to be present (there are 2 structures like this).

  23. Important note! • Most diseases (e.g. cancer) are complex and involve multiple factors (not just a single malfunctioning protein!).

  24. Investigating a genetic disease… • The following EST comes from a hemochromatosis patient; your task is to identify the gene and specific mutation causing the illness, and why the protein is not functioning properly. • The sequence: TGCCTCCTTTGGTGAAGGTGACACATCATGTGACCTCTTCAG TGACCACTCTACGGTGTCGGGCCTTGAACTACTACCCCCAGA ACATCACCATGAAGTGGCTGAAGGATAAGCAGCCAATGGAT GCCAAGGAGTTCGAACCTAAAGACGTATTGCCCAATGGGGA TGGGACCTACCAGGGCTGGATAACCTTGGCTGTACCCCCTGG GGAAGAGCAGAGATATACGTACCAGGTGGAGCACCCAGGCC TGGATCAGCCCCTCATTGTGATCTGGG

  25. ESTs • Expressed Sequence Tags; useful for discovering genes, obtaining data on gene expression/regulation, and in genome mapping. • Short nucleotide sequences (200-500 bases or so) derived from mRNA expressed in cells. • The introns from the genes will already be spliced out. • mRNA is unstable, however, and so it is “reverse transcribed” into cDNA.

  26. Hemochromatosis 2 • BLAST the EST vs. the Human genome (could take a few minutes). - Which chromosome is hit? - What is the contig that is hit (reference assembly)? - Is the EST identical to the genomic sequence? - Take note of the coords of the difference. • Click on “Genome View”. • Select the map element at the bottom corresponding to the contig.

  27. Hemochromatosis 3 • What gene is hit? Zoom in on the BLAST hit a few times. • Display the entire gene sequence vi “dl” and “Display”. • Copy and save the genomic sequence. • Record the coords for the start of the genomic sequence.

  28. Hemochromatosis 4 • Click on a UniGene link Hs.233325. • Note: Expression profile presents data for the expression level of the gene in various tissues. • How many mRNAs and ESTs are there for the HFE gene? • Take note of the mRNA accession NM_000410.

  29. Hemochromatosis 5 • Go to “spidey”: http://www.ncbi.nlm.nih.gov/spidey/ • To determine the intron/exon structure, paste the HFE gene sequence into the upper box, and enter the HFE mRNA accession NM_000410 in the lower box. • Click “Align”.

  30. Hemochromatosis 6 • How many exons are there? • Which exon codes the residue that is changed in the original EST? (You have to do a little arithmetic!) • Record some of the protein sequence around the changed residue: EQRYTCQVEHPG

  31. Hemochromatosis 7 • From the Map Viewer page click on the HFE gene link. • How many HFE transcripts are there? Which is the longest isoform? • Follow “Links” to “Protein” and then to the report for NP_000410. • Determine the residue number that corresponds to the mutation.

  32. RNA splicing and isoforms

  33. Hemochromatosis 8 • What effect does the mutation in the original EST have on the protein? (Look at the table for the Genetic Code.) • Go back to the Gene Report; read the summary and take note of the GeneRIF bibliography. • Now go to “Links” and then to “GeneView in dbSNP” to a list of known SNPs.

  34. Hemochromatosis 9 • In the SNP list note that the one you want is currently shown. • Select “view rs in gene region” and then click on “view rs”. • How many nonsynonomous substitutions do you see? • Do you see the one we are particularly interested in?

  35. Digression: SNPs • Single Nucleotide Polymorphisms. • A single base change that can occur in a person’s DNA. • On average SNPs occur about 1% of the time, most are outside of protein coding regions. • Some SNPs may cause a disease; some may be associated with a disease; others may affect disposition to a disease; others may be simple genetic variation. • dbSNP archives SNPs and other variations such as small-scale deletion/insertion polymorphisms (DIPs), etc.

  36. Hemochromatosis 10 • Back to the Gene Report, click on “Links” and go to “OMIM” (can also get there via the Map Viewer). • In the OMIM entry you can read a bit; also click on “View List” for Allelic Variants, where you can see the mutation again.

  37. Hemochromatosis 11 • From the Gene Report again follow “Links” to “Protein” and scroll down to NP_000401. • Click on “Domains” and then “Show Details”. • What is the Conserved Domain in the region of interest? • Follow the link to the CD. • Click on “View 3D Structure”.

  38. Hemochromatosis 12 • Look for residue position 282 in the query sequence. • Highlight that column. • Is the Cys282 conserved in the family? • The C282Y mutation therefore likely has the effect of …

  39. Aligning a sequence on a structure with Cn3D (example) • Example: Use structure 1ne3A, align sequence for 1m5xA. • In Sequence/Alignment Viewer window select the menu item “Imports/Show Imports”. • In the Import Viewer window select the menu item “Edit/Import Sequences”. • In the Select Chain dialogue box select 1N3E A and click OK. • In the Select Import Source dialogue box select “Network via GI/Accession” and click OK. • In the Import Identifier dialogue box enter the accession 31615545 and click OK. The new sequence will appear. • Select “Algorithms/BLAST single” and use the cursor to click anywhere on the 1m5xA sequence to align it using BLAST.

  40. Aligning a sequence on a structure with Cn3D (example cont.) • Select the menu item “Alignments/Merge All” to make the new alignment appear in the Sequence/Alignment Viewer window. • The alignment should now appear in the Sequence/Alignment Viewer window, aligned residues will be red. • Close the Import Viewer window, pick another color style for the alignment, if desired (e.g. identity). • You can do this with multiple sequences; especially useful if there is no CD for the structure.

  41. PDB

  42. PDB File: Header HEADER ISOMERASE/DNA 01-MAR-00 1EJ9 TITLE CRYSTAL STRUCTURE OF HUMAN TOPOISOMERASE I DNA COMPLEX COMPND MOL_ID: 1; COMPND 2 MOLECULE: DNA TOPOISOMERASE I; COMPND 3 CHAIN: A; COMPND 4 FRAGMENT: C-TERMINAL DOMAIN, RESIDUES 203-765; COMPND 5 EC: 5.99.1.2; COMPND 6 ENGINEERED: YES; COMPND 7 MUTATION: YES; COMPND 8 MOL_ID: 2; COMPND 9 MOLECULE: DNA (5'- COMPND 10 D(*C*AP*AP*AP*AP*AP*GP*AP*CP*TP*CP*AP*GP*AP*AP*AP*AP*AP*TP* COMPND 11 TP*TP*TP*T)-3'); COMPND 12 CHAIN: C; COMPND 13 ENGINEERED: YES; COMPND 14 MOL_ID: 3; COMPND 15 MOLECULE: DNA (5'- COMPND 16 D(*C*AP*AP*AP*AP*AP*TP*TP*TP*TP*TP*CP*TP*GP*AP*GP*TP*CP*TP* COMPND 17 TP*TP*TP*T)-3'); COMPND 18 CHAIN: D; COMPND 19 ENGINEERED: YES SOURCE MOL_ID: 1; SOURCE 2 ORGANISM_SCIENTIFIC: HOMO SAPIENS; SOURCE 3 EXPRESSION_SYSTEM_COMMON: BACULOVIRUS EXPRESSION SYSTEM; SOURCE 4 EXPRESSION_SYSTEM_CELL: SF9 INSECT CELLS; SOURCE 5 MOL_ID: 2; SOURCE 6 SYNTHETIC: YES; SOURCE 7 MOL_ID: 3; SOURCE 8 SYNTHETIC: YES KEYWDS PROTEIN-DNA COMPLEX, TYPE I TOPOISOMERASE, HUMAN REMARK 1 REMARK 2 REMARK 2 RESOLUTION. 2.60 ANGSTROMS. REMARK 3 REMARK 3 REFINEMENT. REMARK 3 PROGRAM : X-PLOR 3.1 REMARK 3 AUTHORS : BRUNGER … REMARK 280 REMARK 280 CRYSTALLIZATION CONDITIONS: 27% PEG 400, 145 MM MGCL2, 20 REMARK 280 MM MES PH 6.8, 5 MM TRIS PH 8.0, 30 MM DTT REMARK 290 ...

  43. PDB File: Data ATOM 1 N TRP A 203 30.156 -4.908 37.767 1.00 50.81 N ATOM 2 CA TRP A 203 30.797 -4.667 36.431 1.00 49.96 C ATOM 3 C TRP A 203 30.369 -3.337 35.766 1.00 49.18 C ATOM 4 O TRP A 203 29.315 -3.238 35.147 1.00 49.27 O ATOM 5 CB TRP A 203 30.518 -5.863 35.513 1.00 46.77 C ATOM 6 CG TRP A 203 30.847 -5.651 34.081 1.00 44.60 C ATOM 7 CD1 TRP A 203 32.028 -5.234 33.553 1.00 49.72 C ATOM 8 CD2 TRP A 203 29.980 -5.876 32.984 1.00 43.73 C ATOM 9 NE1 TRP A 203 31.956 -5.191 32.177 1.00 45.45 N ATOM 10 CE2 TRP A 203 30.704 -5.582 31.805 1.00 45.23 C ATOM 11 CE3 TRP A 203 28.657 -6.305 32.877 1.00 46.48 C ATOM 12 CZ2 TRP A 203 30.149 -5.705 30.539 1.00 46.06 C ATOM 13 CZ3 TRP A 203 28.101 -6.431 31.622 1.00 43.08 C ATOM 14 CH2 TRP A 203 28.849 -6.131 30.463 1.00 45.77 C … ATOM 1 N TRP A 203 30.156 -4.908 37.767 1.00 50.81 X Y Z Name Atom Number Occupancy Residue Number Temperature Factor Atom Name Chain ID Issues: Justification Nomenclature Residue Name

  44. From Coordinates to Models 1EJ9: Human topoisomerase I

More Related