1. Protein Structure Prediction
Ming-Jing Hwang (黃明經)
N121, Institute of Biomedical Sciences
Academia Sinica

2. Objective
To understand the what, why, and how aspects of protein structure prediction, as well as its current status and use.

3. Science 2005

4. Why structure?
Most proteins fold to function. Structure allows us to understand how a protein functions, often with mechanistic details, more than sequence can.
With the knowledge we can design experiments to further probe the protein’s function, or, in the case of a disease protein, devise ways to counter the disease process (e.g. drug design).

5. Ex: Structure & function of potassium channel
MacKinnon, 1998 (2003 Nobelist)

6. Some structure biology Nobel winners
F. H. C. Crick, J. D. Watson, M. H. C. Wilkins (Physiology or Medicine, 1962)for their discoveries concerning the molecular structure of nuclear acids and its significance for information transfer in living material
M. F. Perutz, Sir J. C. Kendrew (Chemistry, 1962)for their studies of the structures of globular proteins
D. Crowfoot Hodgkin (Chemistry, 1964)for her determinations by X-ray techniques of the structures of important biochemical substances
Sir A. Klug (Chemistry, 1982) for his development of crystallographic electron microscopy and his structural elucidation of biologically important nuclei acid-protein complexes
J. Deisenhofer, R. Huber, H. Michel (Chemistry, 1988) for the determination of the three-dimensional structure of a photosynthetic reaction centre
P. D. Boyer, J. E. Walker, J. C. Skou (Chemistry, 1997)for their elucidation of the enzymatic mechanism underlying the synthesis of adenosine triphosphate (ATP) [Boyer, Walker] for the first discovery of an ion-transporting enzyme, Na+, K+ -ATPase [Skou]
J. B. Fenn, K. Tanaka, K. Wüthrich (Chemistry, 2002) for the development of methods for identification and structure analyses of biological macromolecules for their development of soft desorption ionisation methods for mass spectrometric analyses of biological macromolecules [Fenn, Tanaka] for his development of nuclear magnetic resonance spectroscopy for determining the three-dimensional structure of biological macromolecules in solution[Wüthrich]
R. D. Kornberg (Chemistry, 2006)for his studies of the molecular basis of eukaryotic transcription
V. Ramakrishnan, T.A. Steitz, A.E. Yonath (Chemistry 2009)for studies of the structure and function of the ribosome

7. Why prediction?
Structure determination by experimental methods (X-ray, NMR, etc.) is still hard, especially with obstacles at early steps (e.g. expression and crystallization)
To bridge the widening gap between sequence and structure

8. Sequence/Structure Gap
As of June 02, 2009, the number of entries in protein sequence and structure database:
SWISS-PROT/TREMBL : 468,851/7,916,844
PDB : 57,835
Sequence
Structure

9. Structural genomics and drug design
Structural Genomics: HM as work horse
Structural genomics and drug design
Baker & Sali, 2001

10. Structure prediction: 1D->3D, then Function
                                                                                    
MADWVTGKVTKVQNWTDALFSLTVHAPVLPFTAGQFTKLGLEIDGERVQRAYSYVNSPDNPDLEFYLVTVPDGKLSPRLAALKPGDEVQVVSEAAGFFVLDEVPHCETLWMLATGTAIGPYLSILR
UNKNOWN
KNOWN

11. Prediction is very hard, especially if you are predicting unknowns.

12. Why do we believe in prediction at all?
Christian Anfinson, in an elegant experiment in 1957, showed that ribonuclease A (124 aa’s), after having been completely denatured using 8M urea and 2-mercapto-ethanol, regained full enzymatic activity when the urea and 2-ME were slowly removed by dialysis.
All the information needed to fold is contained within the primary sequence.
(1957)

13. Theory of Structure Prediction
Energy Landscape
Theory of Structure Prediction
Nature makes the landscapes of real proteins funneled. You have to work to make the energy landscapes of structure prediction schemes funneled. Let me show you some of the things you have to consider.
Zaida (Zan) Luthey-Schulten

14. How to do 1D3D? (I) Physics-based approach: computing energy as a function of structure (surfing the energy surface)

15. Molecular Mechanics (Force Field)

16. Levitt

18. A POP study: 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

19. Science 2010
(1 millisecond; previous longest 10 microsecond;
Amber FF)
Fig. 1 Folding proteins at x-ray resolution, showing comparison of x-ray structures (blue) (15, 24) and last frame of MD simulation (red): (A) simulation of villin at 300 K, (B) simulation of FiP35 at 337 K. Simulations were initiated from completely extended structures. Villin and FiP35 folded to their native states after 68 µs and 38 µs, respectively, and simulations were continued for an additional 20 µs after the folding event to verify the stability of the native fold.

20. 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
30,000 internet volunteers
several months, or ~a million CPU days of simulation

21. Worldwide distributed computing
Pande group

22. Massively distributed computing …

23. The problem: timescales
Bond
vibration
Isomeris-
ation
Water
dynamics
Helix
forms
Fastest
folders
typical
folders
slow
folders
10-15
femto
10-12
pico
10-9
nano
10-6
micro
10-3
milli
100
seconds MD
step
long
MD run
where we
need to be
where we’d
love to be
16 order of magnitude range
Femtosecond timesteps
Need to simulate micro to milliseconds
Pande group

24. Biology Can’t Wait! (Evolution to rescue)
One Big Family.

25. How to do 1D->3D ab initio
How to do 1D->3D ab initio? (II) Biology-based approach: data (knowledge)-mining
Ignore the actual folding process in cell, instead focus on the end point!

26. The 123 (1D fragment3D) approach
Primary
LGINCRGSSQCGLSGGNLMVRIRDQACGNQGQTWCPGERRAKVCGTGNSISAYVQSTNNCISGTEACRHLTNLVNHGCRVCGSDPLYAGNDVSRGQLTVNYVNSC
seq. to str. mapping
fragment
(structural motifs)
Tertiary
fragment assembly

27. The I-sites library (Baker’s group)

28. Fragment insertion Monte Carlo
Rosetta: a folding simulation program (a try and error process)
Fragment insertion Monte Carlo
backbone torsion angles
accept or reject
fragments
Energy
function
Choose a fragment
change backbone angles
evaluate
Convert to 3D
course/ 2002/ cbio/ handouts/ Class8

29. Does it work? The CASP experiments

30. One lab dominated in CASP4
Baker’s group dominates the ab initio (knowledge-based) prediction in CASP4
One lab dominated in CASP4

31. Some CASP4 successes
Baker’s group

32. # of residues with cRMS below 4Å/6Å
ROSETTA results at CASP5
# of residues with cRMS below 4Å/6Å
Name
Length
human
Automatic
Best decoy
T135
106
83/98
54/64
94/105
T149
116
52/71
44/62
76/92
T161
154
45/83
57/79
55/95
Rosetta predictions in CASP5:
Successes, failures, and prospect
for complete automation. Baker et
all, Proteins, 53: (2003)

33. Toward High-Resolution de Novo Structure Prediction for Small Proteins
--Philip Bradley, Kira M. S. Misura, David Baker
(Science 2005)
The prediction of protein structure from amino acid sequence is a grand challenge of computational molecular biology. By using a combination of improved low- and high-resolution conformational sampling methods, improved atomically detailed potential functions that capture the jigsaw puzzle–like packing of protein cores, and high-performance computing, high-resolution structure prediction (<1.5 angstroms) can be achieved for small protein domains (<85 residues). The primary bottleneck to consistent high-resolution prediction appears to be conformational sampling.

34. Still, not practical for most …
Small proteins
Expensive (computationally): sampling
Not for everyday biologists …

35. HM: the poor man’s solution

36. Similar sequences

37. Similar structures with low sequence similarity
9% sequence identity
Shapiro & Harris, 2000

38. Another example
FtsZ and tubulin would not be recognized as homologous by sequence comparison
Burns, R., Nature 391: (1998)

39. Fold recognition Query sequence Library of known folds Best-fit fold
Mark Gerstein Lab

40. FR by threading Query sequence:
Thread the sequence onto the fold template
Use structural properties to evaluate the fit
Environment
Pairwise interactions
Mark Gerstein Lab

41. Pitfalls of comparative (homology) modeling
Difficult to detect and correct alignment errors
More similar to template than to true structure
Cannot predict novel folds (template may be wrong!)

42. Structure Prediction Methods
Twilight zone
Homology modeling
Fold recognition
ab initio
% sequence identity

43. Protein Structure Prediction
clickable map

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

45. Success and limitations of structure prediction
Models of large and remotely related proteins are not very accurate
Domain boundaries are difficult to define
Models often do not provide details for functional annotation
Success:
Accuracy scores almost doubled from CASP1 to CASP6, might be because of database size
Models of small targets are very accurate
Kryshtafovych et al 2005
Manager/ Files/ Panchenko/ shaitan_kurs_lab2.ppt

46. Structural Bioinformatics: Sequence/Structure Relationship
Percent Identity
100 90 80 70 60 50 40 30 20 10
All possible sequences of amino acids
Protein structures observed in nature
Twilight zone
Midnight zone
Protein sequences observed in nature

47. Final exam assignment
Find a protein sequence of any organism sharing no greater than 40% sequence identify with any accessible entry in PDB.
Predict the 3D structure of your protein using whatever method/tool/server/database.
Write a ~5 page report to document how you find the sequence, how you do (or get) the prediction, and how you visualize/describe the predicted model, along with thoughts/comments on your learning process.
Submit your report to Cathy by 6/22/2011.
Need help? Ask, read/surf, and try it!

48. PDB: the one-stop shop for structure bioinformatics

49. Selected Structural Biology Databases, Servers and Services
CASP-certified protein structure prediction servers
I-TASSER
ROBETTA
HHpred
METATASSER
MULTICOM
Pcons
SAM-T08
3D-Jury
THREADER
Comparative Modeling Servers
SwissModel
MODELLER
Protein secondary structure prediction servers
PSIpred
JPRED
Database of protein structures
PDB - Protein Data Bank
Structural classifications of proteins
SCOP
CATH
Structural neighbors database
Dali Database

50. Thank You!

51. 3D to 1D?
Science 2003

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

53. Structure prediction servers

54. Hybrid approach for solving macromolecular complex structures

55. (Rost, 1996)

56. 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 Even an incredibly fast computational or physical sampling in 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

57. The Rosetta method DECOYS: DISCRIMINATION: Kochl
Generate a large number
of possible shapes
DISCRIMINATION:
Select the correct, native-like
fold
Need good decoy structures
Need a good energy function
Kochl

58. Nature 2007