Multi-step theoretical schemes for DNAs and proteins

1. Multi-step theoretical schemes for DNAs and proteins University of Southern California, Los Angeles, CA, USA December 7th, 2011. Rosa Di Felice rosa.difelice@unimore.it S3 Center, CNR Institute of Nanoscience Modena, Italy. www.nano.cnr.it

2. People Agostino Migliore Leonardo Espinosa Anna Garbesi Andrea Ferretti Tahereh Ghane Daniele Varsano Arrigo Calzolari Manuela Cavallari HouYu Zhang Giorgia Brancolini Stefano Corni Elisa Molinari

3. Collaborators at S3 Elisa Molinari Tahereh Ghane (triplex DNA) Anna Garbesi Daniele Varsano (TDDFT, xDNA, G4/A4, optics) Stefano Corni Manuela Cavallari (molecular dynamics of G4) Andrea Ferretti (transfer integrals) Agostino Migliore (electron transfer, xDNA) Arrigo Calzolari (G4, M-DNA) Giorgia Brancolini (M-DNA and other derivatives) HouYu Zhang (M-DNA) Leonardo Espinosa (G4-DNA, intercalation, optics) External Collaborators (experiment) Danny Porath (HUJI Jerusalem, Israel) Sasha Kotlyar (TAU Tel Aviv, Israel) External Collaborators (theory) Joshua Jortner (TAU Tel Aviv, Israel) Gianaurelio Cuniberti (TU Dresden, Germany) Angel Rubio (UPV/EHU San Sebastian, Spain) Javier Luque (Universidad Barcelona, Spain) Miguel Fuentes-Cabrera & Bobby Sumpter (ORNL, Oak Ridge, TN, USA) Acknowledgements Funding: CNR, EC projects “DNA-based Nanowires” (2002-2006) & “DNA-based Nanodevices” (2006-2010) & “PROSURF” (2006-2009), EC Marie Curie Network “EXC!TING”, MIUR (Italy) “FIRB-NOMADE”, CRMO Foundation (Modena-Italy, 2010-2012), ESF G4-Net (2009-2012), IIT (2010-2013) Computer time: CINECA Bologna, ORNL Oak Ridge (CNMS2008-016, CNMS2010-034), NERSC Berkeley, BSC Barcelona

4. The many-body electronic structure problem The collection of nuclei and electrons in a piece of a material is a formidable many-body problem, because of the intricate motion of the particles in the many-body system: Electronic structure methods deal with solving this formidable problem starting from the fundamental equation for a system of electrons ({ri}) and nuclei ({RI}) In independent electron approximations, the electronic structure problem involves the solution of a Schroedinger-like equation for each of the electrons in the system

5. Input/output in electronic structure calculations Input Atomic coordinates for all atoms that constitute the target system Chemical nature of the atoms Nature of the interactions Theoretical approximations to the many-body interactions Output Wave functions and energy levels of the electrons in the system Optimized atomic configurations, if requested, with forces on all atoms Postprocessing tools needed for analysis Limitations System size (100-1000 atoms) Computer power, parallel scaling of algorithms, etc.

6. Electronic structure and excitations of single biological molecules Migliore et al., J. Phys. Chem. B113, 9402 (2009) Kubar et al., J. Phys. Chem. B112, 7937 (2008) • Traditional approaches: frozen structures • Few evidences that this is not satisfactory • New trends: conformational and environmental effects • Our own multi-step approach

7. Biological molecules @ surfaces Artzy Schnirman et al., Nano Lett.6, 1870 (2006) batches • Experimental evidence of protein/surface recognition from phage display techniques • Recognition and specificity • Amplification of selective peptides • Lack of mechanism understanding • Theory in progress • Multi-scale modeling, from ab initio to brownian dynamics • Ab initio molecular dynamics

8. Outline • Motivation • Electronic structure of natural and modified DNAs • Protein/surface recognition • Multi-step approach to DNA molecules • Classical molecular dynamics  representative structures • Generation of ab-initio-based force fields if needed • Ab initio electronic structure (DFT) and optics (TDDFT, GW+BSE) • Multi-step approach to protein/surface systems • Ab initio  force field generation • Classical molecular dynamics • Rigid-body docking • Outlook

9. Motivation

10. Why DNA for Biomolecular Electronics? • Overhangs • Sequence control • Molecules for nanodevices need to express 3 main traits:sructuring, recognition, conductivity • DNA • Fantasticstructuring • Inter-strand & inter-partnerrecognition • Controversialconductivity N. Seeman, Nature421, 427 (2003) K. Keren et al., Science302, 1380 (2003)

11. Is DNA a viable electrical material? • Experiments onnative-DNAcharge mobility show poor conductivity for long (>40 nm) molecules deposited on substrates • Improve measurement setups • Stiffer molecules (G4-DNA?) • Softer surfaces (alkanethiol monolayers?) • Avoiding non-specific DNA-substrate interaction & controlling DNA-electrode covalent binding • Improve intrinsic conductivity • Metal insertion • Base modification • Helical conformation D. Porath, G. Cuniberti, R. Di Felice, Top. Curr. Chem.237, 183 (2004)

12. Electronic structure of DNA for… Selective protein bio-sensors A.A. Gorodetsky, A. Ebrahim, J.K. Barton, JACS130, 2924 (2008) J.C. Genereux, A.K. Boal, J.K. Barton, JACS132, 891 (2010) Role in oxidative damage and repair

13. Why proteins on surfaces? ECSTM on proteins • Fields of interest • Nano-electronics • Nano-medicine • Controlled drug delivery • Diagnostics and health care (e.g., biosensors) • Why is surface adsorption important? • Measurement setups (AFM, STM&STS, etc.) • Realization of self-assembling devices • Production of new materials with desired shapes • Toxicity of nanoparticles • Energy production/storage

14. Why proteins on surfaces? Proteins as a smart glue • Fields of interest • Nano-electronics • Nano-medicine • Controlled drug delivery • Diagnostics and health care (e.g., biosensors) • Why is surface adsorption important? • Measurement setups (AFM, STM&STS, etc.) • Realization of self-assembling devices • Production of new materials with desired shapes • Toxicity of nanoparticles • Energy production/storage

15. Protein-surface recognition: experimental status Iterate wash Recover the binders Amplify • Combinatorial biochemical methods  best binder to given surfaces, selective binders • S. Brown, PNAS89, 8651 (1992). • M. Sarikaya et al., Nature Mater.2, 577 (2003). • A. Artzy Schnirman et al., Nano Lett.6, 1870 (2006). • No understanding of the basic physical mechanisms that govern the specific interaction No concepts to enable rational design • Combinatorial approach selects proteins/antibodies for a given surface, not viceversa

16. G4-DNA M-DNA xDNA Lee, PRL 2001 Di Felice, JPCB 2005 Kool, Science 2003 Electronic structure of DNA-derivativesGuanine quadruplexDuplex DNA with base alterationTriplex DNA Metal complexation

17. Properties of interest & methods • Electronic structure by DFT • PWSCF: Plane-wave pseudopotential DFT (LDA, PW91, PBE)http://www.quantum-espresso.org • NWChem, Gaussian: DFT with gaussian basis setshttp://www.emsl.pnl.gov/docs/nwchem/nwchem.html, www.gaussian.com • Optical absorption and circular dichroism • Octopus: www.tddft.org/programs/octopus • Classical molecular dynamics • NAMD, AMBER: http://www.ks.uiuc.edu/Research/namd/, http://ambermd.org/ • Transfer integrals • A. Migliore et al., J. Chem. Phys.124, 064501 (2006)

18. dsDNA -HOMO

19. me/m0∞ mh/m0>5 We started with very simple model systems me/m0~1.4 mh/m0~1.0 • The models should contain essential structural features of the real systems • Model reliability depends on what information one wants to gain • With model guanine stacks we could only investigate how the electron energy levels change upon varying the twist angle and stacking distance  qualitative information, not learning the electron levels of a real DNA molecule with a given sequence R. Di Felice et al., Phys. Rev. B65, 045104 (2002)

20. Progress to realistic poly(dG)-poly(dC) E. Shapir, A. Calzolari, C. Cavazzoni, D. Ryndyk, G. Cuniberti, A. B. Kotlyar, R. Di Felice, D. Porath, Nature Mater.7, 68 (2008) D. Ryndyk, E. Shapir, A. Calzolari, D. Porath, R. Di Felice, G. Cuniberti, ACS Nano3, 1651 (2009). Joint theo-exp papers • Computation • Simplified setup: one molecule in vacuo, frozen configuration • Experimental measurements of tunneling currents • Real setup: one molecule deposited on a metal surface probed with a metal tip

21. Circular dichroism in A-tracts by TDDFT EXP Data by Steen Nielsen, Aarhus University, DK THE L. M. Nielsen et al., J. Phys. Chem. B113, 9614 (2009)

22. What do we learn for dsDNA? • Small band widths • Small measured currents, and even smaller from theoretical predictions • DFT and TDDFT useful instruments

23. Modified DNAs: G4-DNA and xDNA

24. Novel long guanine quadruplexes (G4-DNA wires) AFM EFM G4-DNA dsDNA A. B. Kotlyar et al., Adv. Mater.17, 1901 (2005) H. Cohen et al., Nano Lett. 7, 981 (2007) • AFM topography: Higher stiffness (brighter, thicker) and persistence length than duplex DNA  may improve resistance against deformation on hard surfaces • EFM phase map: Polarizable G4-DNA against non-polarizable dsDNA

25. Other appealing candidates C6 C6 C6 C6 • MDNA • Complexation of 1 transition metal cation per base pair (Zn, Ag, Cu???) • Perspectives: enhanced electron transfer capabilities, detection of single base pairs • xDNA • Size expansion of each natural base with a benzene ring  increased aromaticity • Evidence of higher thermal stability for suitable sequences • Perspectives: enhanced electron transfer capability, augmented genetic alphabet

26. Guanine quadruple helices (G4-DNA molecules) 3’ 3’ 3’ 3’ G5 G4 G3 G2 G-2 G-3 G-4 G-5 3’ 3’ 3’ 3’ Backbone neglection Square+translation symmetries Our building-block Side view (from X-ray structure) Superposition between 1st and 4th planes B. Luisi et al. Science265, 520 (1994)

27. K(I)-G4 Electronic Structure • Ground-state electronic structure • Manifolds  effective semiconductor with higher band widths than dsDNA [indication, not demonstration of faster charge transfer] • Transport • Higher conductance than dsDNA: P.B. Woiczikowski et al., J. Chem. Phys.133, 035103 (2010) A. Calzolari et al., Appl. Phys. Lett.80, 3331 (2002); J. Phys. Chem. B108, 2509 & 13058 (2004)

28. Optical absorption of x-base stacks • H-bonding  redshift of low-energy excitations (not visible here) • Stacking  pronounced hypochromicity • Hypochromicity is stronger for structures with regular overlap between x-pairs • Overall: qualitative behavior similar to stacks of natural pairs, but with strong structural dependence of the fine details Photo-absorption (1/eV) d(CxG)2 d(xGC)2 Energy (eV) We have started to analyze the role of different conformations D. Varsano, A. Garbesi, R. Di Felice, J. Phys. Chem. B111, 14012 (2007)

29. What do we learn from studies of modified DNAs? • Possible faster charge transfer • Relevance of conformational flexibility

30. Hybrid computational approach

31. Multi-step computational strategy • Force-field parametrization for new derivatives • DFT calculations of small fragments • Validation of force-field • Molecular dynamics calculations for ~20-100 ns in explicit solvent • Oligomers of realistic size • Extraction of temporal snapshots or representative conformations (sorting) from the trajectories • (only conformational effects of solvent are included with this approach) • Calculation of electronic structure for MD-extracted structures • Small fragments or real oligomers, depending on property and method • Coordination water molecules or implicit PCM solvent may be included at this level

32. A trajectory for G4-DNA with intercalated porphyrins Trajectory  the time sequence of all atomic coordinates during the dynamics simulation

33. From classical MD to quantum calculations of ground state electronic structure and optical excitations… • Classical trajectory  snaphots (thousands), e.g. every ps • Need for reducing the number of structures for quantum calculations • Need for pruning the whole molecule to the relevant active site to reduce the numebr of atoms in the system and enable quantum calculations • Quantum calculations at regular time intervals • Hundreds of structures to have a reliable time sampling of the statistics • Direct access to statistical averages by averaging over the simulation time • Quantum calculations for sorted representative structures • Few structures, grouped by similarity; occurrence of each representative structure during the dynamics • No obvious time averages, possible definition of weighted averages taking into account the occurrence • Need for a reliable and efficient clustering algorithm, available with MD and docking softwares

34. …From classical MD to quantum … schematically Time ensemble Configuration space • Reduction of the number of structures from the trajectory • Reduction of the system size (pruning)

35. Adenine replacement in duplex DNA: motivation from experiments T. Majima et al. JACS 132, 627 (2010) AD: addition of a NH2 group • D-T gives more stable duplexes than Z-T and even A-T • But… charge transfer ~20 times slower than with Z-T in the same sequence Z-T or D-T ???

36. MD in fully explicit water Snapshot at 20 ns Snapshot at 14 ns Snapshot at 13 ns 5’-CGCGATATCGCG-3’ 3’-GCGCTATAGCGC-5’ 5’-CGCGDTDTCGCG-3’ 3’-GCGCTDTDGCGC-5’ 5’-CGCGZTZTCGCG-3’ 3’-GCGCTZTZGCGC-5’ Computational supercell neutralized with Na+ ions

37. Ongoing work • x is each observation • k is number of clusters • Si is a cluster in the set • i is the mean point in Si Electronic structure and transfer integrals: We sort representative structures from the trajectory

38. Ongoing work A:T D:T Z:T Electronic structure and transfer integrals: We sort representative structures from the trajectory Representative structures from the 15 clusters obtained by applying the Mean Algorithm to all of the trajectories collected in the last 5ns of MD

39. Summary on single DNA molecules • Density functional theory • From the ground state to the excited states (transport and optics)… the road is opening up • Predictive potentiality • Modified DNAs with better charge transfer are viable • Role of conformational flexibility and environment

40. Protein/surface recognition

41. Complexity of protein/surface systems • Verylargesystems (103-106 atoms) • Protein + Inorganic Surface + Solvent • Requirestatistical samplingand/or • simulation oftime evolution • Instant quantities not useful • Interesting dynamics on long time-scale • Involveinteractionsof different origins • Chemical bonds, Coulomb, dispersion... • Different methods most suitable for different portions

42. Computational strategies Complexity of the system Multiscale modeling relatively general but long term Exploiting extreme supercomputer power feasible for some cases

43. Multiscale modeling Small molecules Ab initio energy parameters Classical Molecular Dynamics Small proteins Several competences needed  collaborative effort averaged quantities Docking (rigid protein, implicit water) Large proteins Rebecca Wade http://www.h-its.org/english/homes/wade/index.php

44. Individual amino acids on gold Parametrization of classical force fields for molecular dynamics simulations F. Iori et al., J. Phys. Chem. C 112, 13540(2008). F. Iori et al., J. Comput. Chem. 30, 1465 (2009).

45. Exploiting extreme supercomputing power Protein+Surface+Water allab initio! Car-Parrinello molecular dynamics (CPMD) • Exceptional calculation • Requires very good scalability of the code • Small model proteins, max. tens of ps

46. The target system (CPMD) Why? Polyserine appears in the sequence of well-characterized gold-binding peptides Water layer Protein: poly-Serine -sheet Interstitial water layer Surface: Au(111) 4 layers; 237 supercell; unreconstructed 3D periodic boundary conditions 20 ps In total: # atoms = 587; # electrons = 2552 A. Calzolari, G. Cicero, C. Cavazzoni, A. Catellani, R. Di Felice, S. Corni, JACS 132, 4790(2010)

47. Main results (CPMD) Electron depletion at -sheet 6.55 Electron accumulation at gold 6.50 11.1 O charge (e) Au charge (e) 6.45 Au(bulk) 11.0 6.40 10.9 Statistical distribution of the atomic charges over the whole dynamics • Polyserandwaterdonot chemisorb on Au(111). However, a weak interaction is indeed present. • Protein and water recognize the atomic corrugation of the gold surface. • Protein+hydration layer creates an object with well-definedgeometric properties A. Calzolari, G. Cicero, C. Cavazzoni, A. Catellani, R. Di Felice, S. Corni, JACS 132, 4790(2010)

48. Summary on protein/surface interactions • Multi-step modeling of proteins on surfaces • 2 steps done • Validation of docking still needed • Large-scale simulations of small peptides on surfaces • Charge transfer at the interface, beyond pure physisorption

49. Desirable follow-up & development • Towards simulation of real experimental conditions and measurable quantities • Environment and measurement geometry (electrodes, substrates, etc.) • Excited-state quantities (optics, transport) • Biological processes • Development of empirical parameters for more and more systems, transferability • Desired developments • Theory • Developments of efficient algorithms beyond the ground state • Software • Optimal scaling on parallel computers • Implementation of theoretical tools • Hardware