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Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding I Prof. Corey O’Hern Department of Mechanical Engineering & Materials.

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Presentation on theme: "Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding I Prof. Corey O’Hern Department of Mechanical Engineering & Materials."— Presentation transcript:

1 Bioinformatics: Practical Application of Simulation and Data Mining Protein Folding I Prof. Corey O’Hern Department of Mechanical Engineering & Materials Science Department of Physics Department of Applied Physics Program in Computation Biology & Bioinformatics Integrated Graduate Program in Physical & Engineering Biology Yale University 1

2 Statistical Mechanics Biochemistry Protein Folding 2

3 What are proteins? Proteins are important; e.g. for catalyzing and regulating biochemical reactions, transporting molecules, … Linear polymer chain composed of tens (peptides) to thousands (proteins) of monomers Monomers are 20 naturally occurring amino acids Different proteins have different amino acid sequences Structureless, extended unfolded state Compact, ‘unique’ native folded state (with secondary and tertiary structure) required for biological function Sequence determines protein structure (or lack thereof) Proteins unfold or denature with increasing temperature or chemical denaturants Linear polymerFolded state 3

4 Amino Acids I N-terminalC-terminal CC variable side chain R peptide bonds Side chains differentiate amino acid repeat units Peptide bonds link residues into polypeptides 4

5 Amino Acids II 5

6 The Protein Folding Problem: What is ‘ unique ’ folded 3D structure of a protein based on its amino acid sequence? Sequence  Structure Lys-Asn-Val-Arg-Ser-Lys-Val-Gly-Ser-Thr-Glu-Asn-Ile-Lys- His-Gln-Pro- Gly-Gly-Gly-… 6

7 Folding: hydrophobicity, hydrogen bonding, van der Waals interactions, … Unfolding: increase in conformational entropy, electric charge… H (hydrophobic) P (polar) inside outside Hydrophobicity index Driving Forces 7

8 Higher-order Structure 8

9 Secondary Structure: Loops,  -helices,  -strands/sheets  -sheet  -strand Right-handed; three turns Vertical hydrogen bonds between NH 2 (teal/white) backbone group and C=O (grey/red) backbone group four residues earlier in sequence Side chains (R) on outside; point upwards toward NH 2 Each amino acid corresponds to 100 , 1.5Å, 3.6 amino acids per turn ( ,  )=(-60 ,-45  )  -helix propensities: Met, Ala, Leu, Glu  -helix 5-10 residues; peptide backbones fully extended NH (blue/white) of one strand hydrogen-bonded to C=O (black/red) of another strand C ,side chains (yellow) on adjacent strands aligned; side chains along single strand alternate up and down ( ,  )=(-135 ,135  )  -strand propensities: Val, Thr, Tyr, Trp, Phe, Ile 5Å 9

10 Backbonde Dihedral Angles 10

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14 Ramachandran Plot: Determining Steric Clashes CC  NC C  N CC  N CC  N Backbone dihedral angles    =0,180  Non-Gly Gly theory PDB   vdW radii < vdW radii backbone flexibility 4 atoms define dihedral angle: 14

15 Backbone dihedral angles from PDB

16 16

17 Prof. Fred Richards, Yale MB&B

18 Side-Chain Dihedral Angles Side chain: C  -CH 2 -CH 2 -CH 2 -CH 2 -NH 3  4 : Lys, Arg  5 : Arg Use NC  C  C  C  C  N  to define  1,  2,  3,  4 18

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20 Sidechain Dihedral Angle Distributions for Leu PDB Hard-sphere model

21 Molecular biologistBiological Physicist Your model is oversimplified and has nothing to do with biology! Your model is too complicated and has no predictive power! 21

22 Folding Transition T>T m T<T m Oxygen: Red Carbon: Cyan Nitrogen: Dark Blue Sulfur: Yellow

23 For all possible conformations, compute free energy from atomic interactions within protein and protein- solvent interactions; find conformation with lowest free energy…e.g using all-atom molecular dynamics simulations Possible Strategies for Understanding Protein Folding Not possible?, limited time resolution Use coarse-grained models with effective interactions between residues and residues and solvent General, but qualitative 23

24 Why do proteins fold (correctly & rapidly)??  = # allowed dihedral angles How does a protein find the global optimum w/o global search? Proteins fold much faster. For a protein with N amino acids, number of backbone conformations/minima Levinthal ’ s paradox: N c ~ 3 200 ~10 95  fold ~ N c  sample ~10 83 s  universe ~ 10 17 s vs  fold ~ 10 -6 -10 -3 s 24

25 U, F =U-TS Energy Landscape all atomic coordinates; dihedral angles M1M1 M3M3 M2M2 S 12 S 23 S -1 minimum saddle point maximum 25

26 Roughness of Energy Landscape smooth, funneled rough (Wolynes et. al. 1997) 26

27 Folding Pathways similarity to native state dead end 27

28 Folding Phase Diagram smoothrough 28

29 What differentiates the native state from other low-lying energy minima? How many low-lying energy minima are there? Can we calculate landscape roughness from sequence? What determines whether protein will fold to the native state or become trapped in another minimum? What are the pathways in the energy landscape that a given protein follows to its native state? Open Questions NP Hard Problem! 29

30 Digression---Number of Energy Minima for Sticky Spheres NmNm NsNs NpNp 4 1 1 5 6 7 1 2 5 6 50 486 8 9 10 13 52 - 5500 49029 - sphere packings polymer packings N s ~exp(aN m ); N p ~exp(bN m ) with b>a 30


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