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.
Structural Bioinformatics: analysisis of protein structures and their functions by
informatic tools
Tools and techniques for:
• Analize
• Save
• Visualize
• Predict
• Compare
• Evaluate– ESTRUCTURE OF PROTEINS
1-GLSDGEWQLV LNVWGKVEAD IPGHGQEVLI RLFKGHPETL EKFDKFKHLK SEDEMKASED LKKHGATVLT ALGGILKKKG HHEAEIKPLA QSHATKHKIP VKYLEFISEC IIQVLQSKHP GDFGADAQGA MNKALELFRK DMASNYKELG FQG-153
-Ala-Ser-Ile-Met-Arg-
Función
Aminoacid sequence determines one significative form.3D form of the protein determines its function
Complexity levels: Hierarchics
Primary: so far
Secundary: α-helix 35% of residuesß - sheet, 25% of residuesß turns, Ω turns, 3/10 helix Total: 65-75%Rest: inclasificable subestructures, hazard forms (ramdom coils)
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
X ray difraction
NMR
•3D structural Data Bank
Protein data Bank Tour
•Statistics•Look for the active form (closed Conformation from human glucokinase)•Take a look to the file•Save archive
PDB archives
Molecular Visualization
JMOL web
JMOL molecular visualization program
FIRST GLANCE JMOL
Example of a tutorial on glucokinase
Molecular Visualization Programs
• Rasmol (1995)• Chime• Protein Explorer ( Chime interface, requieres
Chime, problems with Chime)• Jmol (java)
• Deep View• Others: “professionals” Pymol
Tools for 3D structures analysis and comparison
• Check structures
• Looking for similars in structures. VAST
• (1 mbn, whale myoglobin)
• Structure alignment: servers and deepview
• conserved surfaces (glucokinase)
Structural alignment
Structural alignment
• 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
Derived Data bases and classification of proteins based on
3D structures
• PDBsum
• Clasification: SCOP, CATH
CATH 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)
SCOP. Structural Classification of Proteins
1. Family. Clear evolutive relationship
2. Superfamily. Probably common evolutive origin
3. Folding. Strong structural homology
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