Life science and nanotechnology software applications L. Litov, P. Petkov, G. Vayssilov

1. Life science and nanotechnology software applications L. Litov, P. Petkov, G. Vayssilov University of Sofia

2. Physics basics Quantum simulations Molecular dynamics Examples – nanotechnology Examples – life science Summary Outlook

3. Physics basics

4. Physics Newton equation Schrödinger equation Probability to find the system in (r,t)

5. Quantum simulations

6. Quantum simulations • Schrödinger equation • Born- Oppenheimer approximation • Schrödinger equation for electrons • Schrödinger equation fore nuclei

7. Quantum simulations • Hartree – Fock approximation - huge computational time • Density Functional Theory (DFT) - Hohenberg-Kohn, Kohn- Sham • Ground state properties of many electron system are determined by electron density • Interacting electrons in static external potential  non-interacting electrons in effective potential • VASP, CP2K, CPMD, GAMESS, GAUSSIAN, Q-Chem

8. Molecular Dynamics

9. Моделиране на взаимодействия на биологични молекули From quantum to classic mechanics For particle with mass m in equilibrium and temperature T, the mean value of the momentum is Heisenberg uncertainty principle Follows that the width is It is possible to omit the quantum effects if the variations sx are less than some critical width defining the accuracy of our calculations.. Critical width

10. Electrons are treated by means of quantum mechanics Hydrogen and Deuterium atoms at temperature 300 K can not be considered as a pure classical objects Heavier atoms can be treated as classical objects (temperatures 300 K). Additional correction are introduced in order to take into account quantum effects. Quantum – Classic mechanics

11. Solving the equations of motion Point in the phase space of the system Hamilton equations - Liouville operator where In Cartesian coordinates и Do not commutate

12. Solving the equations Time propagator One step propagator Applying on We obtain Velocity Verlet, Leap Frog algorithms

13. Every atom should be included -chemical bonds with other atoms -long distance interactions Potential – force field Bond strength Sum over all bonds Bond angle Sum over all angles Torsion Van der Waals interactions Coulomb interaction

14. Potential – force field Parameterization of chemical bonds - Empirical parameters (pk)

15. Potential – force field Parameterization of the other interactions - Empirical parameters (pk) AMBER, CHARMM,GROMOS, GROMACS, LAMMPS, NAMD, VMD

16. New materials - nanothenology

17. nanothenology • Two examples • Interface between Pt8 cluster and Ce21O42nanoparticle • Reduction effect - generation of Ce3+cations • Effect on formation energy of oxygen vacancies • VASP • Hydrogen reverse spillover on zeolite-supported clusters • Ab initio MD simulation of Rh4 and Ag4 clusters • CP2K • Georgi N. Vayssilov, P. Petkov, H. Aleksandrov (Univ. of Sofia)

18. Platinum cluster on ceria nanoparticle • Pt/CeO2 - the key component of the automotive catalyst • Model: cluster Pt8 on Ce21O42 nanoparticle • Reduction of one Ce4+ to Ce3+ in the most stable structures Pt Ce3+ Eads = -5.03 eV Ns = 4 1 Ce3+ O2- Ce4+

19. Clusters Ce21O42 • Plane-wave density-functional calculations • Model clusters Ce21O42 • VASP code • PW91 gradient-corrected functional + U = 4 eV • Plane wave basis, cutoff of 415 eV • Spin-polarized calculations (where appropriate) • Unit cells: 202020 Å, allowing ~10 Å vacuum between neighboring cluster images

20. Platinum cluster on ceria nanoparticle • Energy for formation of an O vacancy Ef is reduced in the presence of platinum:ΔEf = 0.44 eV -1/2 O2 1.67 eV -1/2 O2 1.23 eV

21. Ab initio MD simulations of supported clusters • Support - Zeolite MOR - total 295 atoms per unit cell • a = 18.256, b = 20.534, c = 15.084 A • angles = 90.0 • Si/Al = 89/7 ≈ 13 • Initial M-H distances: ~250, ~275, ~470 … pm Rh4 Ag4

22. Ab initio MD simulation of Rh4 and Ag4 clusters • Periodic ab initio MD simulations – CP2K • DFT: PBE; BO MD and optimization • PW basis, 200 eV cutoff for MD and 400 eV for geometry optimization • NVT ensemble • MD run: time step 1 fs; 1 frame = 10 fs; time ~20 ps • T = 300 K;CSVR thermostat

23. 10ps MD run: Proton transfer to Rh4 Protons to be transferred 23

24. 10ps MD run: Interaction with OH for Ag4 24

25. Life science

26. Understanding of human interferon-gamma binding

27. Active site Res 18-26 Active site Res 18-26 N-terminus N-terminus C-terminus C-terminus 122-143 Human Interferon Gamma

28. Interferon-gamma and its alpha receptor PDB ID: 1fg9 Residues connected by H-bonds

29. Task To find a possible way to inhibit the gamma-interferon activity Block the binding sites of the gamma-interferon Find a ligand binding hIFN-g and blocking its activity Block the binding receptors (hIFNgRa) on the cell surface With mutated hIFN-g peptides, lacking biological activity With some other ligand Need to understand the mechanism of hIFN-g binding to its cell receptors Gamma interferon binding

30. High performance computing, large scale simulation and drug design hIFNg + hIFNgRa in water 26 ns

31. INF-g C-terminus D1 domain (125KTGRKRKR132) D2 domain (137RGRR140)

32. INF-g C-terminus

33. High performance computing, large scale simulation and drug design hINF-g - hIFNgRa interaction simulations GROMACS

35. High performance computing, large scale simulation and drug design INF-g C-terminus Heparin derived oligosaccharide Biochem J. 2004 November 15; 384(Pt 1): 93–99. NMR characterization of the interaction between the C-terminal domain of interferon-γ and heparin-derived oligosaccharides Cécile Vanhaverbeke,*1 Jean-Pierre Simorre,* Rabia Sadir,† Pierre Gans,*2 and Hugues Lortat-Jacob† dp8 PDB ID: 1hpn dp4 dp2

36. High performance computing, large scale simulation and drug design hIFN-gand d8

38. The goal - construction and verification of a stable full atomistic computer model of the whole ribosome, which enables realistic simulations of various biochemical processes in the living cell. Stable ribosome subunits Construction of the whole ribosome including tRNA, mRNA, and a growing peptide chain Determination of the structure of the ribosome in water Investigation of the influence of the type of the cations on the stability of the whole ribosome (role of Na and Mg ions) Non trivial challenging task requiring a Petascale (~3.106 atoms) computing Ribosome structure model

39. Ribosome big subunit • CHARMM27 Force Field • Explicit solvent MD simulation • Crystallographic ions included • NAMD 2.6 • Time step 2.5 fs • NVT • Unstable structure even in about 0.5 ns long MD simulation

40. Ribosome big subunit with Na+ counter ions • Na+ ions added to crystallographic structure compensating the charge of phosphates Na+

41. Trajectories RMSD

42. COX inhibitors • Investigation of the system – enzyme- inhibitor • Cyclooxygenase (COX 1 and COX I2) – responsible for many cell processes • Regulation and production of hormones • Regulation of the Ca transfer • Thrombosis aggregation • Regulation of inflammatory processes ..etc. • COX1 and COX2 bind with arachidonic acid – produces prostaglandines • COX1 and COX2 are targets for all nonsteroidal anti- inflammatory drugs like aspirin, paracetamol etc.

44. Docking • Docking is based on: • Search of the most suitable ligand orientation with respect to the receptor centre • Define the binding affinity – using different scoring functions • Calculation of the binding energy • DOCK 6.4 • Selectivity test • 512 ligands • There is no binding

45. Arachidonic acid • Arachidonic acid binding is reproduced correctly (crystallographic structure) • RMSD = 1,820 Å; ECryst = 55,45 kcal/mol; • ΔE = 4,2 kcal/mol;EDock = 51,25 kcal/mo

46. COX1 and COX2 inhibitors • Investigation of inhibitor ligands • Crystallographic orientation of some inhibitors are reproduced well • Ibuprofen, Fluribiprofen etc • Binding of specific COX2 inhibitors is under investigation • Diclofenak, Celecoxib

47. Computer simulations: are extremely useful in the design of new materials can play significant role in the understanding of biological processes at atomic and molecular level significantly reduce time and cost of development of new drugs (in-silco drug design) Quantum calculations are extremely time consuming – require new algorithms and more powerful (super)computers. Simulations of large (milions of atoms) systems require supercomputing at Petascale level Problem with scaling – require new algorithms in order to reduce procesor communications Reach variety of software is installed on Bulgarian IBM BG/P supercomputer We are welcome to run your jobs at BG supercomputer Conclusions

48. Spare slides

49. Algorithms for solving the equation of motion Error at every step Accumulated error

50. Systematic errors • Force field (Gromacs, NAMD) • Water model – different for NAMD and GROMACS • Box size • Periodic boundary conditions • Need special investigations