1. Tema 14. Bases of protein structure and structural prediction. Structural data bank. Protein Data Bank. Molecular Visualization Tools for 3D. Prediction based on sequence. Folding prediction. 3D structural prediction by homology. Quality criteria.

2. Structural Bioinformatics: analysisis of protein structures and their functions by informatic tools

3. Tools and techniques for: Analize Save Visualize Predict Compare Evaluate –ESTRUCTURE OF PROTEINS

4. 1-GLSDGEWQLV LNVWGKVEAD IPGHGQEVLI RLFKGHPETL EKFDKFKHLK SEDEMKASED LKKHGATVLT ALGGILKKKG HHEAEIKPLA QSHATKHKIP VKYLEFISEC IIQVLQSKHP GDFGADAQGA MNKALELFRK DM ASNYKELG FQG-153

5. -Ala-Ser-Ile-Met-Arg- Función Aminoacid sequence determines one significative form. 3D form of the protein determines its function

6. Complexity levels: Hierarchics Primary: so far Secundary: α-helix 35% of residues ß - sheet, 25% of residues ß turns, Ω turns, 3/10 helix Total: 65-75% Rest: inclasificable subestructures, hazard forms (ramdom coils)

7. Tertiary Structure Simple Clasification: –All alfa (>50% helix; <10% ß) –All ß (>30% beta; <5% heix) –Mixture Refined Clasification –Topologies, motifs, domains –Foldings. Most of the proteins will be classified in one or other way from about 1000 distinct basic foldings Quaternary structure

8. X ray difraction

10. NMR

11. 3D structural Data Bank

12. Protein data Bank Tour Statistics Look for the active form (closed Conformation from human glucokinase) Take a look to the file Save archive

14. PDB archives

16. Molecular Visualization JMOL web JMOL molecular visualization program FIRST GLANCE JMOL Example of a tutorial on glucokinase

18. Molecular Visualization Programs Rasmol (1995) Chime Protein Explorer ( Chime interface, requieres Chime, problems with Chime) Jmol (java) Deep View Others: “professionals” Pymol

23. Tools for 3D structures analysis and comparison Check structures Looking for similars in structures. VASTVAST (1 mbn, whale myoglobin) Structure alignment: servers and deepview conserved surfaces (glucokinase)conserved surfaces

24. Structural alignment

25. Goal: Obtain best superposition from several structures –Dinamic program scoring from geometric characteristics –Matrices of intramolecular distances –Clustering in 3D It is possible to classify proteins based on structural homology Servidor

29. Derived Data bases and classification of proteins based on 3D structures PDBsum Clasification: SCOP, CATH

30. PDBSUM

31. CATHCATH Hierarchy C: Class (secondary structure content) A: Architecture (disposition of the secondary structure elements) T: Topology (disposition of the connexions between elements) H: Homology (Structural homology) S: Sequence (Sequence homology)

36. SCOPSCOP. Structural Classification of Proteins 1.Family. Clear evolutive relationship 2.Superfamily. Probably common evolutive origin 3.Folding. Strong structural homology