1. Molecular modelling / structure prediction (A computational approach to protein structure) Today: Why bother about proteins/prediction Concepts of molecular modelling –The physicist’s approach –The biologist’s approach Get a feel for usefulness/uselessness Where is the future going? Thomas Huber Department of Mathematics Room 724, Priestley building huber@maths.uq.edu.au

2. Why do we care about Protein Structures/ Prediction? Academic curiosity? –Understanding how nature works Drug & Ligand design –Need protein structure to design molecules which inhibit/excite cure all sorts of diseases Protein design –making better proteins sensor proteins industrial catalysts (washing powder, synthetic reactions, …) Urgency of prediction –  10 4 structures are determined insignificant compared to all proteins –sequencing = fast & cheap –structure determination = hard & expensive

4. Three basic choices in molecular modelling Representation –Which degrees of freedom are treated explicitly Scoring –Which scoring function (force field) Searching –Which method to search or sample conformational space

5. The physicist’s approach: Folding by 1 st principles Representation: atomic level Scoring: physical force field Searching: Newton’s equations of motion Concept: Doing what nature does

6. Naïve idea? Levinthal’s paradox (1968) –3 possible rotamers per dihedral angle  astronomical number of conformations Golf course scenario

7. Levinthal’s paradox is irrelevant Folding is not a random process  Bumpy bowl scenario Why are folding simulations still unsuccessful? Simulations computational expensive Force fields are not good Gross approximations in simulations Nature uses tricks Posttranslational processing Chaperones Environment change

8. Is a physical approach useless? No! Very useful aid to structure determination / refinement –Experimentally observed structural data very incomplete NMR: only distances <  6Å Xtallography: only 50% of data can be measured (phase information missing) –Physico-chemical information and complement experimental data Give dynamical picture of structure

9. Biologist’s approach: Prediction by induction Representation: amino acid sequence Scoring: sequence similarity (identity) Searching: optimal string matching (with gaps and insertions) Concept: Homologous sequences fold into similar structures

10. Validation of concept (Rost, 1999) >10 6 sequence alignments between protein pairs Optimal discrimination between similar and dis-similar structure

11. Is it useful? PDB statistics: –  10 4 protein structures determined –<10 3 protein folds

12. 8 Modelling steps Template recognition Alignment Alignment correction Backbone generation Loop building Side chain generation Overall model refinement Model verification –Comparison with Experimental results –Steric overlap –Ramachandran plot Sequence score Force field

13. Limiting factors

14. How good are homology models? G.V. Vried 1998: 34 homologous protein pairs

15. What about side chains? Biology happens in side chains Packing side chains in protein core is not a trivial problem –Many alternative arrangements –High energy barriers

16. Accuracy of modelled side chains Dunbrack SCWRL results –299 monomeric proteins –40263 side chains

17. The Next Step: Computational Proteomics Mass scale homology modelling of entire genomes –Lots of sequence data –First pick the easy cases –Computers are cheap and work 7-24

20. Prediction of Protein Structure

21. How to detect remote homologues Fold recognition using threading –Combine concepts of physicist and biologists Predicting secondary structure More about that in BIOL3004 –Structural biology elective Tue 8/5 10am Thu 10/5 10am –Database mining elective L10

22. Take home messages Computational approaches are –Not perfect –Yet indispensable Molecular modelling has huge potential in structural biology –Currently 10 4 structures in PDB –For every sequence in the Swissprot database with homology to a structure in the PDB models are available!! –Vast amount of data still to come Levinthal paradox –Is true –BUT not relevant Different aims need different approaches (3 choices of MM!) –modelling enzyme reactions –modelling protein folding –weather forecast

23. Clever approaches more important than bigger computers