Towards a computational design of an oxygen tolerant H 2 converting enzyme

1. Towards a computational design of an oxygen tolerant H2 converting enzyme Jochen Blumberger University College London, UK CCSWS4 workshop, IPAM Los Angeles, 18 May 2011

2. Methods and properties Electronic structure theory (DFT) Observables: -ionic structure -electronic structure -reaction free energies -redox potentials -pKa values -reaction barriers -charge mobilities Ab-initio MD Classical MD QM/MM MD Statistical mechanics

3. e- Charge transfer in organic solar cell materials e- H+ e- Electron flow in biological wires Redox and PT reactions in solution Gas diffusion in proteins

4. Acknowledgment • Po-hung Wang (UCL): did all work • Robert Best (University of Cambridge, UK) • £££ • Taiwanese government: PhD scholarship • UCL: PhD studentship • Royal Society: University Research Fellowship

5. Outline • Defining the optimization problem: hydrogenase & aerotolerance • A microscopic model for gas diffusion in proteins (forward problem) • Diffusion paths and rates of H2, O2 and CO in WT hydrogenase • Optimization through amino acid mutations (reverse problem)

6. Hydrogenase: Nature’s solution to H2 production and oxidation FeS clusters • catalyses H2 production: • 2H+ + 2e- + energy  H2 • catalyses H2 oxidation: • H2 2H+ + 2e- + energy • highly efficient: turnover • rate ~ 1000 s-1 NiFe or FeFe active site cluster O2

7. Applications in Bio-energy Catalyst in biofuel cells H2 photo-biological production (green algea, cyanobacteria) • enzyme is renewable • as active as Pt but less expensive • selective for substrates • simplified cell design as ion exchange membrane not needed

8. Problem: oxygen sensitivity ofHases • Inhibited by O2 (atmosphere) and CO • Inhibition is irreversible for FeFe-hases • (best H2 producers) • Intense research efforts world-wide • (Armstrong, Leger,Fontecilla-Camps, • Ghirardi,…) • O2 sensitivity hampers large • scale applications

9. Three strategies to makeHaseoxygen-tolerant 3. Facilitating removal of oxidation products 2. Restrict binding of O2 to active site 1. Restricting access of O2 molecules

10. Engineering a molecular filter into Hase Can one modify hase by mutation so that H2 can diffuse into/out of the active site, but O2 and CO cannot ? H O O H O C Property to be optimized: Diffusion rate of a gas molecule from the solvent to the enzyme active site

11. Outline • Defining the optimization problem: hydrogenase & aerotolerance • A microscopic model for gas diffusion in proteins (forward problem) • Diffusion paths and rates of H2, O2 and CO in WT hydrogenase • Optimization through amino acid mutations (reverse problem)

12. Previous work on gas diffusion in proteins Elberand co-workers: locally enhanced sampling (LES) Schulten and co-workers: LES, implicit ligand sampling McCammon and co-workers: very long MD simulations Ciccotti, Vanden-Eijnden and co-workers: temperature accelerated MD valuable but no rates reported

13. MD simulation of gas diffusion in NiFe-hase

14. Trajectory of a single gas molecule diffusive jumps Gas transport by diffusive `jumps’ between protein cavities

15. From trajectories to probability density average over many trajectories H2 gas probability density (brown contour)

16. From probability density to clusters clustering algorithm clusters or cavities (red spheres)

17. A coarse master equation approach to gas transport P. Wang, R. B. Best, JB, J. Am. Chem. Soc. 133, 3548 (2011). P. Wang, R. B. Best, JB, Phys. Chem. Chem. Phys. 13, 7708 (2011). Assuming detailed balance: k56 k45 k32 k24 pi: population of cluster i kij: transition rate between cluster iand j k23 k21 k12

18. Calculation of transition rates • Transition rates between clusters from long equilibrium MD simulation • Solvent-to-protein cluster transitions depend on gas concentration and are • pseudo-unimolecular at constant gas pressure: •  they must be multiplied by Vsim (H2O)/V0(H2O). • Enhanced sampling methods for transitions that are poorly sampled in • equilibrium MD. • Nijsym: number of transitions from j to i • (symmetrised) • Tj: total time spent in j N. V. Buchete, G. Hummer, J. Phys. Chem. B. 112, 6057 (2008).

19. Constant force pulling • Pulling of gas molecule from cluster n to m. • Average over initial conditions gives mean first passage time τmn • Obtain MFPT for different pulling forces, τmn(F) = 1/kmn(F) • Extrapolation to zero force using the Dudko-Hummer-Szabo model (Kramers theory) • Insert k0mn= kmn(0) into the rate matrix O. Dudko, G. Hummer, A. Szabo, Phys. Rev. Lett.96, 108101 (2006)

20. Solution of master equation G Initial conditions (t = 0): pSOLVENT = 1, all other pi = 0 Destination cluster: G (geminate) Then solve to obtain pG (t). k56 k45 k32 k24 k23 k21 k12

21. Link to phenomenological rate constants P. Wang, R. B. Best, JB, J. Am. Chem. Soc. 133, 3548 (2011). P. Wang, R. B. Best, JB, Phys. Chem. Chem. Phys. 13, 7708 (2011). diffusion only • Fit gives phenomenological • diffusion rates k+1 and k-1 that • can be compared to experiment.

22. Summary of computational steps Long equilibrium MD simulation of protein + gas Clustering of gas probability density Transition rates between clusters Solve master equation for given initial conditions Fit time-dependent population of destination cluster to phenomenological rate equation k1, k-1

23. Simulation details • Molecular models • Protein: Gromos96 43a1 (united atom) • Water: SPC/E • H2, O2 and CO: • 3 interaction sites • charges to reproduce experimental • quadrupole moment (H2, O2) and • dipole moment (CO) • Lennard-Jones parameter to fit • experimental solvation structure • Experimental diffusion constant in water • reproduced to within 7% and in n-hexane • to within 43 %.

24. Simulation details (contd) • Equilibrium simulation of hase: • 100 gas molecules initially placed • outside protein (225 mM) • 50 ns NVT, 300 K • Gromos clustering algorithm of • probability density • Constant force pulling: • 50-100 trajectories per force • mean first passage times fit well • to DHS model • relative stat. error in diffusion rates • (from block averaging): 30 %. • protein RMSD ca. 2 A with and • without gas •  most transitions between clusters • well sampled •  Transitions into G not well sampled

25. Outline • Defining the optimization problem: hydrogenase & aerotolerance • A microscopic model for gas diffusion in proteins (forward problem) • Diffusion paths and rates of H2, O2 and CO in WT hydrogenase • Optimization through amino acid mutations (reverse problem)

26. Probability density maps of gas molecules for Hase P. Wang, R. B. Best, JB, J. Am. Chem. Soc. 133, 3548 (2011). P. Wang, R. B. Best, JB, Phys. Chem. Chem. Phys. 13, 7708 (2011).

27. Clusters and diffusion pathways H2, O2 CO

28. Constant force pulling 68G CO

29. Diffusion kinetics H2, O2 CO

30. Committors and reactive flux H2 O2 CO CO spheres = committorΦG blue: ΦG = 0, red: ΦG = 1 tube diameter prop to flux J

31. Conclusions • We have developed a general microscopic model for gas diffusion in • proteins (forward problem) • Computed diffusion rate agrees well with experimental rates • (same order of magnitude) • Simulations can suggest possible mutation sites to block access of • inhibitor molecules (inverse problem)