1 / 20

IBGP/BMI 705 Lab 4: Protein structure and alignment

IBGP/BMI 705 Lab 4: Protein structure and alignment. TA: L. Cooper. Why Align Structures For homologous proteins (similar ancestry), this provides the “gold standard” for sequence alignment – elucidates the common ancestry of the proteins.

rod
Download Presentation

IBGP/BMI 705 Lab 4: Protein structure and alignment

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. IBGP/BMI 705 Lab 4:Protein structure and alignment TA: L. Cooper

  2. Why Align Structures • For homologous proteins (similar ancestry), this provides the “gold standard” for sequence alignment – elucidates the common ancestry of the proteins. • For nonhomologous proteins, allows us to identify common substructures of interest. • Allows us to classify proteins into clusters, based on structural similarity.

  3. Example of Structural Homologs 4DFR: Dihydrofolate reductase 1YAC: Octameric Hydrolase of Unknown Specificity 5.9% sequence identity (best alignment) 1YAC structure solved without knowing function. Alignment to 4DFR and others implies it is a hydrolase of some sort. Sheets only Helices only DHFR: yellow & orange YAC: green & purple

  4. Example of Structural Homologs Sequence alignment SLSAAEADLAGKSWAPVFANKNANGLDFLVALFEKFPDSANFFADFK-GKSVADIKA-S VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG PKLRDVSSRIFTRLNEFVNNAANAGKMSAMLSQFAKEHVGFGVGSAQFENVRSMFPGFVA KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP Structural alignment XSLSAAEADLAGKSW-APVFANKN-ANGLDFLVALFEKFPDSANFF-ADFKGKSVA—-DIK V-LSPADKTNVKAAWGK-VGAHA-GEYGAEALERMFLSFPTTKTYFPHF-------DLS-H ASPKLRDVSSRIFTRLNEFVNNAANAGKMSA-MLSQ-FAKEHV-GFGVGSAQFENVRSM-F GSAQVKGHGKKVADALTNAVAHV-D—-DMPNAL—-SALSDLHAHKLRVDPVNFKLLS-HCL PGFVA LVTLAAHLPAEFTP

  5. How to Align Structures • Visual inspection (by eye) • Computational approach • Point-based methods using point distances and other properties to establish correspondences • Secondary structure-based methods use vectors representing secondary structures to establish correspondences. Global motif local

  6. Structural Alignment Algorithms Alignment algorithms create a one-to-one mapping of subset(s) of one sequence to subset(s) of another sequence. Structure-based alignment algorithms do this by minimizing the structure difference score or root-mean-square difference(rmsd) in alpha-carbon positions. • Find correspondence set • Find alignment transform • (protein superposition problem) • Chicken-and-egg

  7. Parameter Space Problem: find the rotation matrix, R and a vector, v, that minimize the following quantity: Torsion angles (f,y) are: - local by nature (error propagation) - invariant upon rotation and translation of the molecule - compact (O(n) angles for a protein of n residues) Add 1 degree To all f, y

  8. Structural Alignments Methods • STRUCTAL [Levitt, Subbiah, Gerstein] • Using dynamic programming with a distance metric • DALI [Holm, Sander] • Analysis of distance maps • LOCK [Singh, Brutlag] • Analysis of secondary structure vectors, followed by refinement with distances • SSAP [Orengo and Taylor, 1989] • VAST [Gibrat et al., 1996] • CE [Shindyalov and Bourne, 1998] • SSM [Krissinel and Henrik, 2004] • …

  9. VAST (Vector Alignment Search Tool) • It places great emphasis on the definition of the threshold of significant structural similarity. By focusing on similarities that are surprising in the statistical sense, one does not waste time examining many similarities of small substructures that occur by chance in protein structure comparison. Very many of the remaining similarities are examples of remote homology, often undetectable by sequence comparison. As such they may provide a broader view of the structure, function and evolution of a protein family. • At the heart of VAST's significance calculation is definition of the "unit" of tertiary structure similarity as pairs of secondary structure elements (SSE's) that have similar type, relative orientation, and connectivity. In comparing two protein domains the most surprising substructure similarity is that where the sum of superposition scores across these "units" is greatest. The likelihood that this similarity would be seen by chance is then given as a simple product: the probability that one would obtain this score in drawing so many "units" at random, times the number of alternative SSE-pair combinations possible in the domain comparison, from which one has chosen the best. • http://www.ncbi.nlm.nih.gov/Structure/RESEARCH/iucrabs.html#Ref_6

  10. Today’s lab • Answer questions bolded on handout (There are five)

  11. PDB: Protein structure viewing

  12. PDB- Protein structure viewing

  13. PDB- Protein structure viewing

  14. PDB- Protein structure viewing

  15. SCOP: Protein Classification

  16. SCOP: Protein Classification

  17. SCOP: Protein Classification

  18. VAST: Alignment

  19. VAST: Alignment

  20. VAST: Alignment

More Related