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Protein Structure Prediction PowerPoint Presentation
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Protein Structure Prediction

Protein Structure Prediction

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Protein Structure Prediction

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  1. Protein Structure Prediction Protein Sequence + Dr. G.P.S. Raghava Structure

  2. Protein Structure Prediction • Experimental Techniques • X-ray Crystallography • NMR • Limitations of Current Experimental Techniques • Protein DataBank (PDB) -> 23000 protein structures • SwissProt -> 100,000 proteins • Non-Redudant (NR) -> 10,00,000 proteins • Importance of Structure Prediction • Fill gap between known sequence and structures • Protein Engg. To alter function of a protein • Rational Drug Design

  3. Different Levels of Protein Structure

  4. Protein Architecture • Proteins consist of amino acids linked by peptide bonds • Each amino acid consists of: • a central carbon atom • an amino group • a carboxyl group and • a side chain • Differences in side chains distinguish the various amino acids

  5. Amino Acid Side Chains Vary in: • Size • Shape • Polarity

  6. Peptide Bond

  7. Peptide Bonds

  8. Dihedral Angles

  9. Conformation Flexibility • Backbone (main chain of atoms in peptide bonds, minus side chains) conformation: • Torsion or rotation angles around: • C-N bond () • C-C bond () • Sterical hinderance: • Most – Pro • Least - Gly

  10. Ramachandran Plot

  11. Protein Secondary Structure Secondary Structure Regular Secondary Structure (-helices, -sheets) Irregular Secondary Structure (Tight turns, Random coils, bulges)

  12. Secondary Structure:Helices ALPHA HELIX : a result of H-bonding between every fourth peptide bond (via amino and carbonyl groups) along the length of the polypeptide chain Individual Amino acid H-bond

  13. Helix formation is local THYROID hormone receptor (2nll)

  14. Secondary Structure:Beta Sheets BETA PLEATED SHEET: a result of H-bonding between polypeptide chains

  15. b-sheet formation is NOT local

  16. Definition of -turn A -turn is defined by four consecutive residues i, i+1, i+2 and i+3 that do not form a helix and have a C(i)-C(i+3) distance less than 7Å and the turn lead to reversal in the protein chain. (Richardson, 1981). The conformation of -turn is defined in terms of  and  of two central residues, i+1 and i+2 and can be classified into different types on the basis of  and . i+1 i+2 i H-bond i+3 D <7Å

  17. Tight turns

  18. Secondary Structureshortcuts

  19. Tertiary Structure: Hexokinase (6000 atoms, 48 kD, 457 amino acids) polypeptides with a tertiary level of structure are usually referred to as globular proteins, since their shape is irregular and globular in form

  20. Quarternary Structure:Haemoglobin

  21. What determines fold? • Anfinsen’s experiments in 1957 demonstrated that proteins can fold spontaneously into their native conformations under physiological conditions. This implies that primary structure does indeed determine folding or 3-D stucture. • Some exceptions exist • Chaperone proteins assist folding • Abnormally folded Prion proteins can catalyze misfolding of normal prion proteins that then aggregate

  22. Levels of Description of Structural Complexity • Primary Structure (AA sequence) • Secondary Structure • Spatial arrangement of a polypeptide’s backbone atoms without regard to side-chain conformations • , , coil, turns (Venkatachalam, 1968) • Super-Secondary Structure • , , /, + (Rao and Rassman, 1973) • Tertiary Structure • 3-D structure of an entire polypeptide • Quarternary Structure • Spatial arrangement of subunits (2 or more polypeptide chains)

  23. Techniques of Structure Prediction • Computer simulation based on energy calculation • Based on physio-chemical principles • Thermodynamic equilibrium with a minimum free energy • Global minimum free energy of protein surface • Knowledge Based approaches • Homology Based Approach • Threading Protein Sequence • Hierarchical Methods

  24. Energy Minimization Techniques Energy Minimization based methods in their pure form, make no priori assumptions and attempt to locate global minma. • Static Minimization Methods • Classical many potential-potential can be construted • Assume that atoms in protein is in static form • Problems(large number of variables & minima and validity of potentials) • Dynamical Minimization Methods • Motions of atoms also considered • Monte Carlo simulation (stochastics in nature, time is not cosider) • Molecular Dynamics (time, quantum mechanical, classical equ.) • Limitations • large number of degree of freedom,CPU power not adequate • Interaction potential is not good enough to model

  25. Molecular Dynamics • Provides a way to observe the motion of large molecules such as proteins at the atomic level – dynamic simulation • Newton’s second law applied to molecules • Potential energy function • Molecular coordinates • Force on all atoms can be calculated, given this function • Trajectory of motion of molecule can be determined

  26. Knowledge Based Approaches • Homology Modelling • Need homologues of known protein structure • Backbone modelling • Side chain modelling • Fail in absence of homology • Threading Based Methods • New way of fold recognition • Sequence is tried to fit in known structures • Motif recognition • Loop & Side chain modelling • Fail in absence of known example

  27. Homology Modeling • Simplest, reliable approach • Basis: proteins with similar sequences tend to fold into similar structures • Has been observed that even proteins with 25% sequence identity fold into similar structures • Does not work for remote homologs (< 25% pairwise identity)

  28. Homology Modeling • Given: • A query sequence Q • A database of known protein structures • Find protein P such that P has high sequence similarity to Q • Return P’s structure as an approximation to Q’s structure

  29. Threading • Given: • sequence of protein P with unknown structure • Database of known folds • Find: • Most plausible fold for P • Evaluate quality of such arrangement • Places the residues of unknown P along the backbone of a known structure and determines stability of side chains in that arrangement

  30. Hierarcial Methods Intermidiate structures are predicted, instead of predicting tertiary structure of protein from amino acids sequence • Prediction of backbone structure • Secondary structure (helix, sheet,coil) • Beta Turn Prediction • Super-secondary structure • Tertiary structure prediction • Limitation Accuracy is only 75-80 % Only three state prediction

  31. Thanks