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Forces and Prediction of Protein Structure

Forces and Prediction of Protein Structure. Ming-Jing Hwang ( 黃明經 ) Institute of Biomedical Sciences Academia Sinica. http://gln.ibms.sinica.edu.tw/. Sequence - Structure - Function.

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Forces and Prediction of Protein Structure

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  1. Forces and Prediction of Protein Structure Ming-Jing Hwang (黃明經) Institute of Biomedical Sciences Academia Sinica http://gln.ibms.sinica.edu.tw/

  2. Sequence - Structure - Function MADWVTGKVTKVQNWTDALFSLTVHAPVLPFTAGQFTKLGLEIDGERVQRAYSYVNSPDNPDLEFYLVTVPDGKLSPRLAALKPGDEVQVVSEAAGFFVLDEVPHCETLWMLATGTAIGPYLSILR

  3. Sequence/Structure Gap Current (May 26, 2005) entries in protein sequence and structure database: • SWISS-PROT/TREMBL : 181,821/1,748,002 • PDB : 31,059 Sequence Structure

  4. Structure Prediction Methods Homology modeling Fold recognition ab initio 0 10 20 30 40 50 60 70 80 90 100 % sequence identity

  5. Levinthal’s paradox (1969) • If we assume three possible states for every flexible dihedral angle in the backbone of a 100-residue protein, the number of possible backbone configurations is 3200. Even an incredibly fast computational or physical sampling in 10-15 s would mean that a complete sampling would take 1080 s, which exceeds the age of the universe by more than 60 orders of magnitude. • Yet proteins fold in seconds or less! Berendsen

  6. Energy landscapes of protein folding Borman, C&E News, 1998

  7. Levitt’s lecture for S*

  8. Levitt

  9. Levitt

  10. Other factors • Formation of 2nd elements • Packing of 2nd elements • Topologies of fold • Metal/co-factor binding • Disulfide bond • …

  11. Ab initio/new fold prediction • Physics-based (laws of physics) • Knowledge-based (rules of evolution)

  12. Levitt

  13. Levitt

  14. Levitt

  15. Levitt

  16. Levitt

  17. Levitt

  18. Levitt

  19. Levitt

  20. Levitt

  21. Levitt

  22. Levitt

  23. Levitt

  24. Levitt

  25. Molecular Mechanics (Force Field)

  26. Levitt

  27. 1-microsecond MD simulation 980ns • villin headpiece • 36 a.a. • 3000 H2O • 12,000 atoms • 256 CPUs (CRAY) • ~4 months • single trajectory Duan & Kollman, 1998

  28. Protein folding by MD PROTEIN FOLDING:A Glimpse of the Holy Grail? Herman J. C. Berendsen* "The Grail had many different manifestations throughout its long history, and many have claimed to possess it or its like". We might have seen a glimpse of it, but the brave knights must prepare for a long pursuit.

  29. Massively distributed computing • SETI@home: • Folding@home • Distributed folding • Sengent’s drug design • FightAIDS@home • …

  30. Massively distributed computing Letters to nature (2002) • engineered protein (BBA5) • zinc finger fold (w/o metal) • 23 a.a. • solvation model • thousands of trajectories each of 5-20 ns, totaling 700 ms • Folding@home • 30,000 internet volunteers • several months, or ~a million CPU days of simulation

  31. Energy landscapes of protein folding Borman, C&E News, 1998

  32. Protein-folding prediction technique CGU: Convex Global Underestimation - K. Dill’s group

  33. Challenges of physics-based methods • Simulation time scale • Computing power • Sampling • Accuracy of energy functions

  34. Structure Prediction Methods Homology modeling Fold recognition ab initio 0 10 20 30 40 50 60 70 80 90 100 % sequence identity

  35. Flowchart of homology (comparative) modeling From Marti-Renom et al.

  36. Fold recognition Find, from a library of folds, the 3D template that accommodates the target sequence best. Also known as “threading” or “inverse folding” Useful for twilight-zone sequences

  37. Fold recognition (aligning sequence to structure) (David Shortle, 2000)

  38. 3D->1D score

  39. On X-ray, NMR, and computed models

  40. (Rost, 1996)

  41. Reliability and uses of comparative models Marti-Renom et al. (2000)

  42. Pitfalls of comparative modeling • Cannot correct alignment errors • More similar to template than to true structure • Cannot predict novel folds

  43. Ab initio/new fold prediction • Physics-based (laws of physics) • Knowledge-based (rules of evolution)

  44. From 1D  2D  3D Primary LGINCRGSSQCGLSGGNLMVRIRDQACGNQGQTWCPGERRAKVCGTGNSISAYVQSTNNCISGTEACRHLTNLVNHGCRVCGSDPLYAGNDVSRGQLTVNYVNSC seq. to str. mapping Secondary(fragment) Tertiary fragment assembly

  45. CASP Experiments

  46. One group dominates the ab initio (knowledge-based) prediction One lab dominated in CASP4

  47. Some CASP4 successes Baker’s group

  48. Ab initio structure prediction server

  49. Science 2003

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