Biomedical genomics lecture
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This document discusses investigating protein structure using online tools. It provides an overview of protein structure, describing how amino acid sequences fold into specific three-dimensional structures, including alpha helices, beta strands, and membrane proteins. It also mentions several online tools that can be used to analyze protein sequences and structures, such as SCOP, CATH, and tools to analyze domains, folds, interactions, and structural variations.
Biomedical genomics lecture
- 1. Biomedical Genomics 1 - Investigating protein structure using online tools Dr L. Therese Bergendahl Research Fellow Joseph Marsh Lab MRC Human Genetics Unit Medical Research Council Human Genetics Unit MRC Institute of Genetics and Molecular Medicine www.igmm.ac.uk
- 4. Protein Structure – Ultrafast recap A Linear sequence of amino acids forms a stable 3D structure fold All α - helices All β - strands Combination of the two, either alternating (α/β) or mixed (α+β) Membrane proteins Disordered Proteins Classified by SCOP or CATH Classic examples are the globin fold, immunoglobulin fold, SH2 domain and TIM barrel
- 5. Laurents et al., Protein Sci, 1994 3 % RMS difference (Cα) is 1.6 Å Sequence similarity is: Ovomucoid (green) and the C-terminal of the L7/L12 ribosomal protein (red)
- 6. Protein Structure Domains are analogous to folds, from the point of view of the full protein – essentially the units of folds that can function independently Proteins interact! With ligands crucial for function As members of transient protein signaling networks As subunits in stable protein complexes
- 7. Protein Structure Disordered Proteins are also important, and are overrepresented in signalling networks Lack any recognisable 3D structure either entirely or in parts of the structure. Data. What is it good for? Sequence Folds/ Motifs/ Domains Interaction with other proteins or macromolecules Stability Function Phenotype
- 9. My protein: inositol-1,4,5-triphosphate receptor 1 (IP3R1) IGMM crew used exome sequencing and found a set of interesting de-novo mutations in the ITPR1 gene in individuals with Gillespie Syndrome Iris hypoplasia and cerebral volume loss common phenotypes in GS patients
- 10. 1, Uniprot My protein: IP3R1
- 19. Domains Homologues and other annotations
- 22. www.predictprotein.org If you don’t want to sign up, use the open version, here: https://ppopen.informatik.tu-muenchen.de
- 23. This can take a frustratingly long time for a large protein..
- 24. No joke.. Its STILL going…
- 30. 206
Editor's Notes
- #10: Inositol-1,4,5-trisphosphate receptors (InsP3Rs) are ubiquitous ion channels responsible for cytosolic Ca(2+) signalling and essential for a broad array of cellular processes ranging from contraction to secretion, and from proliferation to cell death. Despite decades of research on InsP3Rs, a mechanistic understanding of their structure-function relationship is lacking.
- #11: Go to UniProt.org and type in the name of your protein in the search bar on top of the page. Make sure you are searching the protein knowledgebase (UniProtKB). How many hits do you get?
- #13: Take a note of interesting aspects such as disease variants and interaction sites.
- #17: BLAST (Basic Local Alignment Search Tool) is a sequence similarity search method, in which a query protein or nucleotide sequence is compared to nucleotide or protein sequences in a target database to identify regions of local alignment and report those alignments that score above a given score threshold. To determine whether matches to the databases are "significant", we use a threshold E-value. The E-value describes the number of hits one can expect to see by chance when searching a database of a particular size. The lower the E-value, the more "significant" a match to a database sequence is (i.e. there is a smaller probability of finding a match just by chance)
- #26: STRING uses a spring model to generate the network images. Nodes are modeled as masses and edges as springs; the final position of the nodes in the image is computed by minimizing the 'energy' of the system. We give high confidence edges a higher 'spring strength' so that they will reach an optimal position before lower confidence edges.