1. Protein Tertiary Structure Prediction Structural Bioinformatics

2. Primary: amino acid linear sequence. Secondary:  -helices, β-sheets and loops. Tertiary: the 3D shape of the fully folded polypeptide chain The Different levels of Protein Structure

3. The 3D structure of a protein is stored in a coordinate file Each atom is represented by a coordinate in 3D (X, Y, Z)

4. The coordinate file can be viewed graphically RBP Description is given in slides 35-36

5. Predicting 3D Structure –Comparative modeling (homology) Based on structural homology –Fold recognition (threading) Outstanding difficult problem Based on sequence homology

6. Comparative Modeling Similar sequences suggests similar structure

7. Sequence and Structure alignments of two Retinol Binding Protein

8. Structure Alignments The outputs of a structural alignment are a superposition of the atomic coordinates and a minimal Root Mean Square Distance (RMSD) between the structures. The RMSD of two aligned structures indicates their divergence from one another. Low values of RMSD mean similar structures There are many different algorithms for structural Alignment.

9. Comparative Modeling Builds a protein structure model based on its alignment (sequence) to one or more related protein structures in the database Similar sequence suggests similar structure

10. Comparative Modeling Accuracy of the comparative model is usually related to the sequence identity on which it is based >50% sequence identity = high accuracy 30%-50% sequence identity= 90% can be modeled <30% sequence identity =low accuracy (many errors) However other parameters (such as identify length) can influence the results

11. Comparative Modeling Modeling of a sequence based on known structures Consist of four major steps : 1.Finding a known structure(s) related to the sequence to be modeled (template), using sequence comparison methods such as PSI-BLAST 2. Aligning sequence with the templates 3. Building a model 4. Assessing the model

12. What is a good model?

15. Fold Recognition

16. GlobinTIM Protein Folds: sequential and spatial arrangement of secondary structures

17. Similar folds usually mean similar function Homeodomain Transcription factors

18. The same fold can have multiple functions Rossmann TIM barrel 12 different functions 31 different functions

19. Fold Recognition Fold recognition attempt to detect similarities between protein 3D structure that have no significant sequence similarity. Search for folds that are compatible with a particular sequence. "the turn the protein folding problem on it's head” rather than predicting how a sequence will fold, they predict how well a fold will fit a sequence

20. Basic steps in Fold Recognition : Compare sequence against a Library of all known Protein Folds (finite number) Query sequence MTYGFRIPLNCERWGHKLSTVILKRP... Goal: find to what folding template the sequence fits best There are different ways to evaluate sequence-structure fit

21. MAHFPGFGQSLLFGYPVYVFGD... Potential fold... 1)... 56)... n)... -10... -123... 20.5 There are different ways to evaluate sequence-structure fit

22. Ab Initio Modeling Compute molecular structure from laws of physics and chemistry alone Theoretically Ideal solution Practically nearly impossible WHY ? –Exceptionally complex calculations –Biophysics understanding incomplete

23. CASP - Critical Assessment of Structure Prediction Competition among different groups for resolving the 3D structure of proteins that are about to be solved experimentally. Current state - –ab-initio - the worst, but greatly improved in the last years. –Modeling - performs very well when homologous sequences with known structures exist. –Fold recognition - performs well. How do we know what is a good prediction ???

24. What can you do? FOLDIT Solve Puzzles for Science A computer game to fold proteins http://fold.it/portal/puzzles

25. What’s Next Predicting function from structure

26. Structural Genomics : a large scale structure determination project designed to cover all representative protein structures Zarembinski, et al., Proc.Nat.Acad.Sci.USA, 99:15189 (1998) ATP binding domain of protein MJ0577

27. As a result of the Structure Genomic initiative many structures of proteins with unknown function are solved Wanted ! Automated methods to predict function from the protein structures resulting from the structural genomic project.

28. An “out of the box” approach for predicting function from structure DNA binding interfaceRNA binding interface

29. DNA binding interfaceRNA binding interface RNA and DNA binding interfaces tend to have different geometric features

30. H=(k1+k2)/2 Mean Curvature K=k1*k2 Gaussian Curvature Applying Differential Geometry to characterize DNA and RNA binding proteins K1 - MINIMAL CURVATURE K2- MAXIMAL CURVATURE

31. Peak Pit Ridge Valley Flat Minimal Surface Saddle ridge Saddle valley Applying Differential Geometry to characterize DNA and RNA proteins

32. Applying Differential Geometry for DNA and RNA function prediction Frequency of points

33. RNA binding surfaces are distinguished from DNA binding surfaces based on Differential Geometric features 78% DNA binding 76% RNA-binding

34. Differential Geometry can correctly determine whether a given binding domain binds RNA or DNA RNA patternDNA pattern Frequency of points Shazman et al, NAR 2011

35. How can we view the protein structure ? Download the coordinates of the structure from the PDB http://www.rcsb.org/pdb/ Launch a 3D viewer program For example we will use the program Pymol The program can be downloaded freely from the Pymol homepage http://pymol.orghttp://pymol.org Upload the coordinates to the viewer

36. Pymol example Launch Pymol Open file “1aqb” (PDB coordinate file) Display sequence Hide everything Show main chain / hide main chain Show cartoon Color by ss Color red Color green, resi 1:40 Help : http://pymol.orghttp://pymol.org