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The folding network of villin headpiece subdomain

The folding network of villin headpiece subdomain. Hongxing Lei Beijing Institute of Genomics Chinese Academy of Sciences. The Protein Folding Problem. ?. The importance of protein folding. Amyloid diseases Alzheimer ’ s disease (AD) Parkinson ’ s disease (PD) Huntington ’ s disease

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The folding network of villin headpiece subdomain

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  1. The folding network of villin headpiece subdomain Hongxing Lei Beijing Institute of Genomics Chinese Academy of Sciences

  2. The Protein Folding Problem ?

  3. The importance of protein folding • Amyloid diseases • Alzheimer’s disease (AD) • Parkinson’s disease (PD) • Huntington’s disease • Prion diseases • Amyotrophic lateral sclerosis (ALS) • Protein structure prediction • Protein design

  4. formation of unfolded state microdomains formation of diffusion and collision of collapse a nucleus microdomains native state

  5. Folding funnel Onuchic & Wolynes, COSB 2004, 14:70-75

  6. The challenges in all-atom protein folding Time scale • Protein folding: seconds • Simulation: microsecond • Gap: 106 • Solution: Ultrafast-folding proteins / Supercomputers Energetic accuracy • ΔGfold (a few kcal/mol, hydrogen bond) • High accuracy of force field

  7. Ab initio all-atom protein folding • 1998: villin headpiece, 36 amino acids, 3+Å • 2002/2003: • trpcage, 20 amino acids, 1Å • Villin headpiece by Folding@Home (3.8Å) • Villin headpiece by Shen et al (3.0Å) • BBA5 by Folding@Home (2.2-2.5Å) • Recently (Scheraga and others) • A few small proteins 2.0-4.0Å

  8. Villin headpiece subdomain (HP35)

  9. Review of previous work

  10. Best folded structure from simulation Cα RMSD 0.39 Å

  11. Four states from simulation

  12. Thermodynamic properties from simulation

  13. The folding pathway of HP35

  14. Results from 10μs simulations

  15. Folding trajectory #1

  16. Segment folding

  17. Population of native hydrogen bonds

  18. Folding landscape

  19. Free energy landscape 0-2.5 us 2.5-5.0 us 7.5-10.0 us 5.0-7.5 us

  20. Top ten clusters

  21. Folding network (RMSD)

  22. Folding network (Epot)

  23. Scale free property R2 = 0.786

  24. Hubs Degree: 45 RMSD-ALL: 7.26 Å RMSD-CA : 5.90 Å RMSD-segment A : 5.17 Å RMSD-segment B : 1.63 Å RGYR : 9.75 Å Population: 124243 Degree: 17 RMSD-ALL: 5.98 Å RMSD-CA: 4.27 Å RMSD-segment A: 3.96 Å RMSD-segment B: 1.18 Å RGYR: 10.80 Å Population: 1735 Degree: 24 RMSD-ALL: 3.75 Å RMSD-CA : 1.50 Å RMSD-segment A : 0.36Å RMSD-segment B : 0.59 Å RGYR : 10.17 Å Population: 32090

  25. Bottlenecks Betweenness: 2.78 RMSD-ALL: 6.24 Å RMSD-CA : 5.02 Å RMSD-segment A: 4.40 Å RMSD-segment B : 1.53 Å RGYR : 10.86 Å Population : 550 Betweenness: 2.95 RMSD-ALL: 5.70 Å RMSD-CA : 4.34 Å RMSD-segment A: 3.38 Å RMSD-segment B : 1.34 Å RGYR : 10.42 Å Population : 237 Betweenness: 4.11 RMSD-ALL: 6.63 Å RMSD-CA : 4.03 Å RMSD-segment A: 4.64 Å RMSD-segment B : 1.07 Å RGYR : 11.02 Å Population : 873

  26. Folding trajectory #2

  27. Segment folding

  28. Population of native hydrogen bonds

  29. Folding landscapes

  30. Free energy landscape 0-2.5 us 2.5-5.0 us 5.0-7.5 us 7.5-10.0 us

  31. Top ten clusters

  32. Folding network (RMSD)

  33. Folding network (Epot)

  34. Scale free property R2 = 0.723

  35. Hubs Degree : 36 RMSD-ALL: 3.73 Å RMSD-CA : 1.71 Å RMSD-segment A: 0.63 Å RMSD-segment B : 0.69 Å RGYR : 10.05 Å Population : 61485 Degree : 31 RMSD-ALL: 5.99 Å RMSD-CA : 3.92 Å RMSD-segment A: 4.13 Å RMSD-segment B : 0.97 Å RGYR : 11.50 Å Population : 2689 Degree : 30 RMSD-ALL: 6.83 Å RMSD-CA : 5.83 Å RMSD-segment A: 4.88 Å RMSD-segment B : 1.65 Å RGYR : 9.93 Å Population : 5991 Degree : 22 RMSD-ALL: 6.75 Å RMSD-CA : 5.13 Å RMSD-segment A: 5.04 Å RMSD-segment B : 0.61 Å RGYR : 12.30 Å Population : 2854

  36. Bottlenecks Betweenness: 2.27 RMSD-ALL: 6.22 Å RMSD-CA : 4.50 Å RMSD-segment A: 4.84 Å RMSD-segment B : 1.82 Å RGYR : 10.97 Å Population : 890 Betweenness: 2.46 RMSD-ALL: 7.23 Å RMSD-CA : 5.80 Å RMSD-segment A: 5.17 Å RMSD-segment B : 0.82 Å RGYR : 10.63 Å Population : 392 Betweenness: 2.48 RMSD-ALL: 6.62 Å RMSD-CA : 4.93 Å RMSD-segment A: 4.50 Å RMSD-segment B : 1.13 Å RGYR : 11.43 Å Population : 260

  37. A SCORING FUNCTION FOR STRUCTURE PREDICTION

  38. SCORING FUNCTIONS Knowledge-based functions (well compacted; surface area; contact order) Physics-based functions (free energy; potential energy; hydrogen bond energy; VDW energy)

  39. OUR SCORING FUNCTION F(E)=ESE + a*EFF + b*EHB ESE= the statistical energy EFF= the force field physical energy with GB solvation model EHB= the main chain hydrogen bonding energy a= the coefficient of the force field physical energy term b= the coefficient of the main chain hydrogen bonding energy term

  40. DECOY SETS http://depts.washington.edu/bakerpg/decoys/ a wide variety of different proteins; close to the native structure; produced by a relatively unbiased procedure

  41. Decoy sets RMSD <5Å  acceptable decoys Total : 534, 38.14% Training sets ( 14 × 100 ) Testing sets ( 13 × 100 ) Group a: contain 3-11 acceptable decoys Group b: contain at least 93 acceptable decoys

  42. Decoy sets

  43. F(E)=ESE+ A*EFF + B*EHB

  44. F(E)=ESE+ A*EFF + B*EHB

  45. F(E)=ESE+ A*EFF + B*EHB EFF = the force field physical energy with GB solvation model Two protocols: only a minimization; after minimization, a 40 ps molecule dynamic run followed by another minimization. (The results from both protocols are very similar, and therefore, the use of the less time consuming protocol was adopted. )

  46. F(E)=ESE+ A*EFF + B*EHB

  47. Various force fields in Tinker

  48. F(E)=ESE+ A*EFF + B*EHB

  49. AMBER force fields

  50. F(E)=ESE+ A*EFF + B*EHB

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