Molecular Modelling of DNA

Charlie LaughtonUniversity of Nottingham

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. 1994 91:3579-83

History…

• Begins in 1983 (Levitt, Karplus) – 7 years later than proteins – why?– Electrostatics of DNA– Screening by counterions

and solvent

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

Forcefields for Nucleic Acids

• Both AMBER and CHARMM are parameterised

• Each has its pros and cons: AMBER-99 and CHARMM-27 pretty OK

Solvent

• Periodic boxes of explicit solvent (e.g. TIP3P) generally best

• ‘Cheap’ implicit models (e.g. =rij) bad• Latest implicit models, e.g. GB-SA,

fairly good but not cheap.

Electrostatics

• Use methods like Ewald for long-range corrections

• Include counterions – but where do you put them?– Phosphate bisectors– Electrostatic potential– Random

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)

DNA environments

• Stretch it, squash it (AFM)• In organic solvents• In high vacuum (Mass Spec)

Molecular Dynamics

• DNA is flexible – use MD whenever you can to get proper sampling

Worked example

• Why does this ligand recognise this sequence with this affinity?

Worked example

• Step 1: Parameterise the ligand

Worked example

• Step 2: Solvate system with box of water– 10 Å ‘buffer’– Think ahead…

Box problems

Box problems

(Truncated octahedral boxes often safest)

Worked example

• Step 3: Add counterions– K+ seems best– Minimal salt?– Placement

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.

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’)

time

parameter

equilibration production

Measures of equilibration

• Energy/temperature: bad• RMSD:

– from start structure: can be misleading – from avg better

• PCA probably best

Measures of sampling

• Normal distributions in parameters, without drift

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!

Ergodic hypothesis

Ergodic hypothesis

Analysing the ensemble

• Linear free energy relationships

G = <Eq> + <EvdW>

• Tricky - needs lots of parameterisation

Analysing the ensemble

• “MM-GBSA” approaches

G = <Ecomplex>-(<Ereceptor> + <Eligand>)

+ (entropy terms)

• Better for relative than absolute energies• Doesn’t cope well with induced fit

Analysing the ensemble

• Free energy perturbation (FEP) approaches– The ‘Gold standard’ approach– Technically and computationally

demanding

Some examples

Drug Design

• Structural change predicted to give tighter binding – 100x better antitumour agent

Wells, (2005)

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

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

Understanding molecular motors

• Modelling used to predict an energetically-reasonable pathway between two states observed crystallographically, allowing a movie of the operation of the molecular motor to be produced.

Wang (2005)

‘High throughput’ MD

• PDB2MD (using NGS service)– Laughton

(2005)– See also

Biophys. J. 2004, 87, 3799-813.

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!

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.

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

• Modesto Orozco & Javier Luque

• Peter Coveney & Shantenu Jha

• Hooshang Nikjoo• Ben Luisi & Chris

Calladine• David Thurston &

Steve Neidle• EPSRC• BBSRC• CR-UK• Fulbright• Wellcome Trust