1. Applied Bioinformatics Week 12

2. Bioinformatics & Functional Proteomics How to classify proteins into functional classes? How to compare one proteome with another? How to include functional/activity/pathway information in databases? How to extract functional motifs from sequence data? How to predict phenotype from proteotype?

3. Bioinformatics & Expressional Proteomics How to correlate changes in protein expression with disease? How to distinguish important from unimportant changes in expression? How to compare, archive, retrieve gel data? How to rapidly, accurately identify proteins from MS and 2D gel data? How to include expression info in databases?

4. Bioinformatics & Structural Proteomics How to predict 3D structure from 1D sequence? How to determine function from structure? How to classify proteins on basis of structure? How to recognize 3D motifs and patterns? How to use bioinformatics databases to help in 3D structure determination? How to predict which proteins will express well or produce stable, folded molecules?

5. Protein Folding Problem “ Predict a three-dimensional structure of a protein from its amino acid sequence. ” “ How does a protein fold into the structure? ” This question has not been solved for more than half a century.

6. Proteins Can Fold into 3D Structures Spontaneously The three-dimensional structure of a protein is self-organized in solution. If we can calculate the energy of the system precisely, it is possible to predict the structure of the protein! The structure corresponds to the state with the lowest free energy of the protein-solvent system. (Anfinsen’s dogma)

7. Levinthal Paradox We assume that there are three conformations for each amino acid (ex. α-helix, β-sheet and random coil). If a protein is made up of 100 amino acid residues, a total number of conformations is 3 100 = 515377520732011331036461129765621272702107522001 ≒ 5 x 10 47. If 100 psec (10 -10 sec) were required to convert from a conformation to another one, a random search of all conformations would require 5 x 10 47 x 10 -10 sec ≒ 1.6 x 10 30 years. However, folding of proteins takes place in msec to sec order. Therefore, proteins fold not via a random search but a more sophisticated search process. We want to watch the folding process of a protein using molecular simulation techniques.

8. Why is the “ Protein Folding ” so Important? Proteins play important roles in living organisms. Some proteins are deeply related with diseases. And structural information of a protein is necessary to explain and predict its gene function as well as to design molecules that bind to the protein in drug design. Today, whole genome sequences (the complete set of genes) of various organisms have been deciphered and we realize that functions of many genes are unknown and some are related with diseases. Therefore, understanding of protein folding helps us to investigate the functions of these genes and to design useful drugs against the diseases efficiently. In addition to that, the understanding opens the door to designing of proteins having novel functions as new nano machines.

9. Forces Involved in the Protein Folding Electrostatic interactions van der Waals interactions Hydrogen bonds Hydrophobic interactions

10. The Energy Function Calculate energies for each particle Since long range interactions important for each pair of particles the pair-wise interactions should be calculated

11. System for Folding Simulations Without water molecules With water molecules # of atoms: 304 # of atoms: 304 + 7,377 = 7,681

12. Much Faster, Much Larger! Special-purpose computer –Calculation of non-bonded interactions is performed using the special chip that is developed only for this purpose. –For example; MDM (Molecular Dynamics Machine) or MD-Grape: RIKEN MD Engine: Taisho Pharmaceutical Co., and Fuji Xerox Co. Parallelization –A single job is divided into several smaller ones and they are calculated on multi CPUs simultaneously. –Today, almost all MD programs for biomolecular simulations (ex. AMBER, CHARMm, GROMOS, NAMD, MARBLE, etc) can run on parallel computers. Fold@Home

13. Homology Modeling Template Selection and Fold Assignment Target – Template Alignment Model Building –Loop Modeling –Sidechain Modeling Model Evaluation

14. Fold Assignment and Template Selection Identify all protein structures with sequences related to the target, then select templates 3 main classes of comparison methods –Compare the target sequence with each database sequence independently, pair-wise sequence – sequence comparison, BLAST and FASTA –Multiple sequence comparisons to improve sensitivity, PSI-BLAST –Threading or 3-D template matching methods

15. Target – Template Alignment Most important step in Homology Modeling A specialized method should be used for alignment –Over 40% identity the alignment is likely to be correct. –Regions of low local sequence similarity become common when overall sequence identity is under 40%. (Saqi et al., Protein Eng. 1999) –The alignment becomes difficult below 30% sequence identity. (Rost, Protein Eng. 1999)

16. Model Building Construct a 3-D model of the target sequence based on its alignment on template structures Three different model building approaches –Modeling by rigid body assembly –Modeling by segment matching –Modeling by satisfaction of spatial restraints Accuracies of these models are similar Template selection and alignment have larger impact on the model

17. Swiss-MOD Web Server Screenshots from the Homology Modeling Server Swiss-Model Construct a framework using known protein structures Generate the location of the target amino acids on the framework If loop regions not determined, additional database search or short simulations

18. Procedure of the MODELLER program After obtaining restraints run a geometry optimization or real- space optimization to satisfy them

19. Errors in Homology Models a.Errors in side chain packing b.Distortions and shifts in correctly aligned regions c.Errors in regions without a template

20. d. Errors due to misalignment e. Incorrect templates

21. Model Building Programs COMPOSERPwww-cryst.bioc.cam.ac.uk CONGENPwww.congenomics.com/congen/congen.html CPH modelsSwww.cbs.dtu.dk/services/CPHmodels/ DRAGONPwww.nimr.mrc.ac.uk/~mathbio/a-aszodi/dragon.html ICMPwww.molsoft.com InsightIIPwww.msi.com MODELLERPguitar.rockefeller.edu/modeller/modeller.html LOOKPwww.mag.com QUANTAPwww.msi.com SYBYLPwww.tripos.com SCWRLPwww.cmpharm.ucsf.edu/~bower/scrwl/scrwl.html SWISS-MODSwww.expasy.ch/swissmod WHAT IFPwww.sander.embl-heidelberg.de/whatif/

23. Applications

24. End Theory Mind mapping 10 min break

25. Practice

26. 3D Structure Prediction? Get a protein sequence Go to: http://bioinf.cs.ucl.ac.uk/psipred –Use threading Got to: http://www.rcsb.org/pdb –Find known structure Folding@home –Ab inito prediction FoldIt (http://fold.it/portal/) Crystal structure of a monomeric retroviral protease solved by protein folding game players. Increased Diels-Alderase activity through backbone remodeling guided by Foldit players.