1 / 29

Macromolecular Structures: A User’s Perspective

Macromolecular Structures: A User’s Perspective. Mike Word, Ph.D. GlaxoSmithKline & Duke University Biochemistry November, 2003. Rational drug design what we want to be doing. 4cox. Illustration by David Goodsell. Structure  Function. 1aos (urea cycle). 94% sequence identity. 1dcn

kato
Télécharger la présentation

Macromolecular Structures: A User’s Perspective

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. Macromolecular Structures:A User’s Perspective Mike Word, Ph.D. GlaxoSmithKline & Duke University Biochemistry November, 2003

  2. Rational drug designwhat we want to be doing 4cox Illustration by David Goodsell

  3. Structure  Function 1aos (urea cycle) 94% sequence identity 1dcn (eye lens)

  4. Structure not always unique Prion protein

  5. SCOP classes http://scop.berkeley.edu/ • All alpha proteins (138) • All beta proteins (93) • Alpha and beta proteins (a/b) (97) • Mainly parallel beta sheets (beta-alpha-beta units) • Alpha plus beta proteins (a+b) (184) • Mainly antiparallel beta sheets (segregated alpha and beta regions) • Multi-domain proteins (28) • Membrane and cell surface proteins and peptides (11) • Small proteins (54) • Coiled coil proteins (5) • Peptides(77)

  6. ~30% ?

  7. Alternative:ab initio (or de novo ) modeling Sequence + theory  model A range of techniques; mostly energy based Very difficult to apply Comparative Protein Modeling • Aim - To gain structural insights for a new protein sequence before experimental elucidation takes place • Method - Extrapolation of the new structure from that of related family members

  8. Fold Assignment Template Selection Alignment Model Building Evaluation Folds, families and motifs Evolutionary patterns are critical for successful prediction of function

  9. Fold Assignment Template Selection Alignment Model Building Evaluation Templates • Atomic coordinates from X-ray or NMR • Highest sequence homology • Relevant domain fragment • SWISS-MODEL “first approach”: • Can the structure be modeled?

  10. Fold Assignment Template Selection Alignment Model Building Evaluation Target to template alignment • Should consider (2º) structure: domain boundaries, motifs, location of loops, active site residues, SS bonds... • Can’t recover from incorrect alignment!

  11. Fold Assignment Template Selection Alignment Model Building Evaluation Comparative modeling methods • Manual model building • Satisfaction of spatial restraints • Template based fragment assembly

  12. Fold Assignment Template Selection Alignment Model Building Evaluation Model Evaluation • Does the model match the template(s)? • Is the stereochemistry good? Energy ok? • Are amino acids in reasonable environments? • What parts are conserved in the sequence alignment? • What information can the model provide?

  13. All-Atom Small-ProbeContact Surface Analysis

  14. Contact score: score = e–(gap/err)2 [van der Waals contacts] dots + 4 Vol(Hbonds) [hydrogen bonds] - 10 Vol(Overlaps) [atomic clashes] Clash score: clsc = number(clashes > 0.4Å)/1000 atoms

  15. MolProbity • Structure validation server • Add H’s, analyze contacts http://kinemage. biochem.duke.edu/

  16. CASP • Critical Assessment of Techniques for Protein Structure Prediction • Biannual contest to model proteins of unknown structure • While experimental structure determination is still in progress • Evaluates manual to completely automated structure prediction • http://predictioncenter.llnl.gov

  17. Richardson Lab:Dave & Jane, Laura Weston, Ian Davis, Bryan Arendall, Shuren Wang, Jeremy Block, Michael Prisant, Simon Lovell, Thomas LaBean, Mike Zalis GlaxoSmithKline Protein Bioinformatics: Nicolas Guex, Kristin Koretke NIH GM15000 GlaxoSmithKline Acknowledgements

More Related