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Protein Structure Refinement ─ a dynamic approach

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Presentation on theme: "Protein Structure Refinement ─ a dynamic approach"— Presentation transcript:

1 Protein Structure Refinement ─ a dynamic approach
Hao Fan Biophysical Chemistry University of Groningen The Netherlands

2 Overview Background: Why we need refinement?
Molecular dynamics Test sets of proteins Tests of refinement protocols Explicit water Implicit water Ongoing projects

3 Background : Why we need refinement?
* Structure is key to knowing functions of proteins The gap is growing ! * Predict protein structure from sequence Friedberg et al, Curr. Opin. Stru. Biol. 14, 2004

4 Background : Why we need refinement?
CASP6T0266 CASP6 T0201 * Large progress in knowledge-based methods * Refinement is required for critical applications (e.g. protein – ligand/protein interactions) Moult, Curr. Opin. Stru. Biol. 15, 2005

5 Background : Molecular dynamics
* Newton’s equations of motion * Boltzmann distribution of energy states

6 Background : Molecular dynamics
GROMOS force field * Non-bonded interactions: Electrostatic van der Waals * Bonded interactions: Bonds Angles Dihedrals

7 Homology models (Modeller & Nest)
Background : Test sets of proteins * Semi ab initio models (Rosetta) Test-set: 15 proteins used by Baker et al. to test Rosetta 4 models for each protein Model quality: only main-chain and Cβ atoms most models > 4 Å backbone RMSD Homology models (Modeller & Nest) Test-set: 30 small proteins (crystal structures, resolution > 1.6 Å,) Model quality: all atoms most models < 4 Å backbone RMSD

8 Refine protein structures ─ Can MD help?

9 Explicit water Force field validation Refinement in brute-force MD
Mimicking chaperone in refinement (I) Solvent (II) Chaperone cage System: periodic Nonbonded: cut-off Electrostatics: reaction-Field pH: Temperature: K

10 Force field validation
Is GROMOS force field precise enough? ─ stability of protein native structures X-ray NMR

11 Force field validation
Short summary Most protein core-regions remain near-native within the simulation time Large fluctuations happen in the flexible region (e.g. loops) NMR derived structures behave less stable than the structures derived by X-ray GROMOS 43a1 can be used for protein structure refinement

12 Brute-force MD Example: mercury detoxification protein 1afi
Starting model: good quality Refinement MD 100 ns Rosetta model NMR structure

13 Brute-force MD Example: mercury detoxification protein 1afi
Starting model: good quality RMSD 0.26 nm RMSD 0.16 nm

14 Brute-force MD Example: mercury detoxification protein 1afi
Starting model: bad quality Refinement MD 5 ns Refinement MD 400 ns Rosetta model

15 Brute-force MD Example: mercury detoxification protein 1afi
Starting model: bad quality RMSD 0.87 nm RMSD 0.70 nm RMSD 0.76 nm

16 Brute-force MD Short summary
For structures close to native conformation, MD refinement can be very effective ( ns). For grossly misfolded structures, current timescales are probably too short. Molecular chaperones facilitate the folding of a wide range of proteins in vivo.

17 Mimicking chaperone GroEL/ES cycle 18 nm

18 Mechanisms of assisting folding
Mimicking chaperone Mechanisms of assisting folding Prevent aggregation Reaction cycle : iterative binding and folding Unfold non-native polypeptide Hydrophobic interaction with GroEL Mechanical stess from GroEL upon ATP binding Refold non-native polypeptide Hydrophilic cavity favor burial of hydrophobic surfaces Confinement limit conformational space

19 Mimicking chaperone (I)
─ oscillating solvent environment SPC water Increase partial charges (5 ns) Decrease partial charges (5 ns) O- -0.82e 5 cycle H+ H+ 0.41e 0.41e 3. Fan and Mark, Protein Science (2004) 13,

20 Mimicking chaperone (I)
Example: vaccinia virus DNA topoisomerase I, 1vcc Starting model: bad quality Refinement 50 ns Rosetta model X-ray structure

21 Mimicking chaperone (I)
RMSD 0.59 nm RMSD 0.59 nm

22 Mimicking chaperone (I)
Example: S1 RNA binding domain, 1sro Starting model: bad quality Rosetta model Refinement 50 ns NMR structure

23 Mimicking chaperone (I)
RMSD 0.87 nm RMSD 0.58 nm (12-76 a.a nm)

24 ─ iterative annealing + spatial confinement
Mimicking chaperone (II) ─ iterative annealing + spatial confinement Spherical folding cavity Channels for water flux Nonpolar: CH2 Polar: NH, CO Repulsive: CY

25 Mimicking chaperone (II)
Unfold : binding 5 ns 1.5 nm 6.0 nm

26 Mimicking chaperone (II)
Refold : release Protocol I Protocol II 10 ns Control: refold in explicit water

27 Mimicking chaperone (II)
Example: vaccinia virus DNA topoisomerase I, 1vcc Starting model: bad quality Refinement 150 ns Rosetta model X-ray structure N N N

28 Mimicking chaperone (II)
RMSD 0.59 nm RMSD 0.49 nm

29 Mimicking chaperone (II)
Example: S1 RNA binding domain, 1sro Starting model: bad quality Refinement 150 ns Rosetta model NMR structure N N N

30 Mimicking chaperone (II)
RMSD 0.87 nm RMSD 0.42 nm

31 Mimicking chaperone (II)

32 Mimicking chaperone Short summary
Repetivive changes in environment induce protein unfolding and refolding Spatial confinement facilitates refolding Current protocols seem to favor β-sheet formation Improved protocol may contribute to protein folding and refinement

33 Implicit water Comparison of GB models
Refinement in brute-force SD+GB/SA Implicit chaperone

34 Implicit water Poisson equation Born equation

35 Implicit water GB models “Pair-wise” approximation: fGB
fGB is nearly accurate as the numerical PB solution if i is correct ! Effective Born radius: i i can be solved numerically or analytically

36 Implicit water Still model ( Qiu et al. ) HCT model ( Hawkins et al. )
AGB model ( Gallicchio et al. ) No explicit volume integration (Vj/rij4) Connection dependent scaling factors Two-sphere integral Atom type dependent scaling factors Two-sphere integral Local geometry dependent scaling factors

37 Implicit water mAGB model Zhu et al, J. Phys. Chem. 109, 2005

38 GB model comparison Which GB model is the best ?
Energetics Peptide folding Protein dynamics Test-set: ten proteins Description of proteins Protein 1afi 1ail 1ctf 1lea 1pgb 1shg 1tuc 1ubi 2bby 2ci2 Reso. — 1.9 1.7 1.8 2.0 Nres 72 70 68 56 57 61 76 69 63 Nα 21 60 38 39 14 3 12 35 13 Nβ 18 6 30 28 25 23 4 22 Ncharge 2 -2 -4 1 -1

39 GB model comparison Analysis of structure deviation
Positional root mean square deviation (RMSD) Deviation of structural properties: Rg: Radius of gyration SASA: Solvent accessible surface area NHB: Main-chain hydrogen bonds NC: Sidechain contacts Statistic analysis: two-way analysis of variance (ANOVA) Key factors: Starting velocities, Simulation time scale, Friction coefficient of solvent Null hyphothesis H0: μ1=μ2=μ…

40 GB model comparison Analysis of RMSD Not significant
Mean RMSD ( Still ~ HCT ~ mAGB ) 0.24 ~ 0.27 ~ ~ 0.19 ~ 0.16 ANOVA the probability of H0 being true is 26% and 32% Not significant

41 GB model comparison Analysis of Rg Significant
Mean deviation of Rg ( Still ~ HCT ~ mAGB ) 0.012 ~ ~ 0.001 ANOVA the probability of H0 being true is 0.5% Significant

42 GB model comparison Short summary
All three GB/SA models provide reasonable presentation of solvent effect mAGB model shows statistically apparent advantages in keeping native hydrogen bonds and radius of gyration Highlight the importance of statistical analysis in MD simulation

43 Ongoing projects Brute-force SD+GB/SA refinement Implicit chaperone
Replica-exchange MD Spatial quadrature + mobile cavity Replica Si (i=1, … , M) at temperature Ti Exchange replicas with Metropolis criterion

44 Brute-force SD+GB/SA Reparameterize Still and mAGB models on 4 other proteins ─ Monte-Carlo simulated annealing approach Protein dynamics: a comparison to previous study Backbone RMSD in secondary structures Proteins 1afi 1ail 1ctf 1lea 1pgb 1shg 1tuc 1ubi 2bby 2ci2 ave magb 0.18 0.16 0.13 0.22 0.27 0.11 0.10 0.12 still 0.20 0.29 0.09 magb_new 0.14 0.19 0.21 0.25 0.08 0.15 still_new 0.07 0.17 ANOVA Not Significant

45 Brute-force SD+GB/SA NHB in secondary structures Rg Significant
Proteins 1afi 1ail 1ctf 1lea 1pgb 1shg 1tuc 1ubi 2bby 2ci2 ave. dev. exp. 29 49 41 25 31 19 16 22 21 17 magb 40 34 20 14 15 12 -0.245 still 18 37 28 13 10 11 -0.331 magb_new 39 30 -0.214 still_new 43 33 23 -0.199 Rg Proteins 1afi 1ail 1ctf 1lea 1pgb 1shg 1tuc 1ubi 2bby 2ci2 ave. dev. exp. 1.09 1.28 1.12 1.14 1.06 1.03 1.17 1.20 magb 1.15 1.05 1.08 0.001 still 1.13 1.30 1.18 0.012 magb_new 1.10 1.11 1.04 1.16 -0.005 still_new 1.31 1.19 -0.004 Significant

46 Brute-force SD+GB/SA bbrmsd Initial MD Still_new mAGB_new min peak max
1aoy 0.59 0.63 0.71 0.78 0.60 0.69 0.73 0.74 1stu 0.62 0.65 1.00 0.61 0.75 0.81 0.52 0.56 0.68 1vif 0.82 0.66 0.79 1sro 0.45 0.40 0.44 0.50 0.36 0.47 0.55 0.38 1tuc 1sap 0.35 0.33 0.58 0.28 0.39 0.48 1afi 0.26 0.16 0.20 0.22 0.29 0.19 0.23 1vcc 0.57 2bby 0.53 0.67 2fmr 0.43 0.64 0.37 0.51 1a1z 0.41 0.46 0.54 1ail 1coo 0.87 0.84 1lea 2ezh 0.34 0.32 0.49

47 Brute-force SD+GB/SA Short comments
No significantly improvement from GB models on the semi ab initio models Homology models of better quality ? Combined with other advanced sampling techniques ?

48 Outlook to refinement Can we refine most protein models?
Currently No … Can we refine most protein models? Methods could be helpful: Application of advanced sampling methods Combination of simplified and atomic models Combination of knowledge-based and physical potentials Final solution for refinement even folding may lie ahead !

49 Alan Mark: my Ph.D supervisor University of Groningen
Acknowledgement Alan Mark: my Ph.D supervisor Columbia University Barry Honig Jiang Zhu GB models & Structure refinement University of Groningen Ruud Scheek Utrecht University Johan Kemmink NMR refinement

50 Acknowledgement University of Groningen MD group Siewert-Jan Marrink
Alex de Vries Membrane/protein University of Groningen MD group Xavier Periole Tsjerk Wassenaar Alessandra Villa REMD Statistic analysis Force field development

51 Max-Planck-Institute
Acknowledgement Max-Planck-Institute Berk Hess Stockholm University Erik Lindahl Gromacs BioMaDe George Robillard Xiaoqin Wang Hydrophobin

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