Presentation is loading. Please wait.

Presentation is loading. Please wait.

Forces and Prediction of Protein Structure Ming-Jing Hwang ( 黃明經 ) Institute of Biomedical Sciences Academia Sinica

Similar presentations


Presentation on theme: "Forces and Prediction of Protein Structure Ming-Jing Hwang ( 黃明經 ) Institute of Biomedical Sciences Academia Sinica"— Presentation transcript:

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 MADWVTGKVTKVQ NWTDALFSLTVHAP VLPFTAGQFTKLGLE IDGERVQRAYSYVN SPDNPDLEFYLVTVP DGKLSPRLAALKPG DEVQVVSEAAGFFV LDEVPHCETLWMLA TGTAIGPYLSILR

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

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 3 200. Even an incredibly fast computational or physical sampling in 10 -15 s would mean that a complete sampling would take 10 80 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*S*

8 Levitt

9

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

14

15

16

17

18

19

20

21

22

23

24

25 Molecular Mechanics (Force Field)

26 Levitt

27

28 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

29 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.

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

31 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  s - Folding@home - 30,000 internet volunteers - several months, or ~a million CPU days of simulation Massively distributed computing

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

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

34 Challenges of physics-based methods Simulation time scale Computing power Sampling Accuracy of energy functions

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

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

37 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

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

39 3D->1D score

40 On X-ray, NMR, and computed models

41 (Rost, 1996)

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

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

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

45 From 1D  2D  3D SISAY VQGTEACRHLTNLVNH LGINCRGSSQCGLSGGNLMVRIRDQACGNQGQTWCPGERRAKVCGTGNSISAY VQSTNNCISGTEACRHLTNLVNHGCRVCGSDPLYAGNDVSRGQLTVNYVNSC Tertiary Primary Secondary (fragment) fragment assembly seq. to str. mapping

46 CASP Experiments

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

48 Some CASP4 successes Baker ’ s group

49 Ab initio structure prediction server

50 Science 2003

51 A computer-designed protein (93 aa) with 1.2 A resolution

52 Structure prediction servers http://bioinfo.pl/cafasp/list.html

53 Thank You!


Download ppt "Forces and Prediction of Protein Structure Ming-Jing Hwang ( 黃明經 ) Institute of Biomedical Sciences Academia Sinica"

Similar presentations


Ads by Google