Molecular Modelling of DNA. Charlie Laughton University of Nottingham

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1 Molecular Modelling of DNA Charlie Laughton University of Nottingham

2 Why model DNA? Sequences/structures you can t crystallise or get by NMR Drug design Understanding protein-dna recognition Quantitating aspects of recognition From a static to a dynamic picture The tyranny of the lattice Proc Natl Acad Sci USA :

3 History Begins in 1983 (Levitt, Karplus) 7 years later than proteins why? Electrostatics of DNA Screening by counterions and solvent

4 Post 1995 the new era Faster computers means better forcefields Can include water Better ways to treat electrostatics 1990: no water 1995: water 2000: water+ewald

5 Forcefields for Nucleic Acids Both AMBER and CHARMM are parameterised Each has its pros and cons: AMBER-99 and CHARMM-27 pretty OK

6 Solvent Periodic boxes of explicit solvent (e.g. TIP3P) generally best Cheap implicit models (e.g. =r ij ) bad Latest implicit models, e.g. GB-SA, fairly good but not cheap.

7 Electrostatics Use methods like Ewald for long-range corrections Include counterions but where do you put them? Phosphate bisectors Electrostatic potential Random

8 Nucleic acid structures Don t be afraid of: RNA, PNAs, modified bases Triplexes, quadruplexes, ribozymes, Some of these may be easier with AMBER (easier to parameterise)

9 DNA environments Stretch it, squash it (AFM) In organic solvents In high vacuum (Mass Spec)

10 Molecular Dynamics DNA is flexible use MD whenever you can to get proper sampling

11 Worked example Why does this ligand recognise this sequence with this affinity?

12 Worked example Step 1: Parameterise the ligand

13 Worked example Step 2: Solvate system with box of water 10 Å buffer Think ahead

14 Box problems

15 Box problems (Truncated octahedral boxes often safest)

16 Worked example Step 3: Add counterions K+ seems best Minimal salt? Placement

17 Equilibration Initially-built system is unlikely to be energetically optimal Bad nb-contacts voids in solvent Bad electrostatic iteractions Need to be careful, or may blow up in MD Energy mins Low-T dynamics on solvent Raise T slowly, relaxing restraints on DNA All this may need c. 100 ps.

Need to be careful, or may blow up in MD Energy mins Low-T dynamics on

18 How long is a piece of string? When MD starts, system may still be relaxing when is it complete? Once relaxed, how long to collect data for ( production run ) parameter equilibration production time

19 Measures of equilibration Energy/temperature: bad RMSD: from start structure: can be misleading from avg better PCA probably best

20 Measures of sampling Normal distributions in parameters, without drift

21 Analysing the data Standard geometrical measures as on Xray structure If your simulation is long enough and well-equilibrated, the trajectory is equivalent to Boltzmann-weighted ensemble get thermodynamics!

22 Ergodic hypothesis

23 Ergodic hypothesis

24 Analysing the ensemble Linear free energy relationships G = <E q > + <E vdw > Tricky - needs lots of parameterisation

25 Analysing the ensemble MM-GBSA approaches G = <E complex >-(<E receptor > + <E ligand >) + (entropy terms) Better for relative than absolute energies Doesn t cope well with induced fit

26 Analysing the ensemble Free energy perturbation (FEP) approaches The Gold standard approach Technically and computationally demanding

27 Some examples

28 Drug Design Structural change predicted to give tighter binding 100x better antitumour agent Wells, (2005)

29 Understanding co-operativity The formation of a 2:1 complex is favoured over the 1:1 complex, because of entropic factors could be calculated from modelling, invisible from NMR data. Harris, (2001) J. Am. Chem. Soc., 123:12658

30 Understanding AFM Proper thermodynamic analysis of simulations of DNA stretching explained why all previous work failed to agree with experiments Harris, (2005) Biophys J., 88:1684

31 Understanding molecular motors Modelling used to predict an energeticallyreasonable pathway between two states observed crystallographically, allowing a movie of the operation of the molecular motor to be produced. Wang (2005)

32 High throughput MD PDB2MD (using NGS service) Laughton (2005) See also Biophys. J. 2004, 87,

33 Conclusions Nowadays, nothing particularly difficult about modelling DNA Properly applied, can give insights into structures, dynamics and recognition properties that can t be obtained by just looking at the original - e.g. Xray structure. Add value to your data!

34 Support Coming soon: CCPB A new BBSRC-funded Collaborative Computational Project: for Biomolecular Simulation Meetings, specialist workshops, lecture tours, web site with guides, contacts, software, etc. Starts 2006.

35 Acknowledgements Sarah Harris Ed Sherer Chris Grindon Zara Sands Peter Girard Huw Williams Mark Beardsell Supat Jiranusornkul Angelo Pugliese Mark Searle Malcolm Stevens Ian Dryden Jonathan Wattis EPSRC BBSRC CR-UK Fulbright Wellcome Trust Modesto Orozco & Javier Luque Peter Coveney & Shantenu Jha Hooshang Nikjoo Ben Luisi & Chris Calladine David Thurston & Steve Neidle

Biomolecular Modelling

Biomolecular Modelling Carmen Domene Physical & Theoretical Chemistry Laboratory University of Oxford, UK THANKS Dr Joachim Hein Dr Iain Bethune Dr Eilidh Grant & Qi Huangfu 2 EPSRC Grant, Simulations