Hi Henry,
Please see my answers inline below.
On 2/23/14, 10:58 PM, "psu4.uic.edu" <psu4.uic.edu> wrote:
>Dear Amber community,
>
>
>
> It will be interesting to see a non-guided drug molecule could find its
>protein target binding site(s) and/or allosteric sites if we can run
>several micro-second long simulations using pmemd.CUDA.mpi , similar to
>this study.
>
>
>
>http://pubs.acs.org/doi/abs/10.1021/ja202726y
>
>
>
> Unfortunately there are not too many method details reported in the
>manuscript to follow up. We are taking some other long simulation
>examples
>in the Amber community and other D.E. Shaw plus P.E. Vande's
>publications. The proposed settings are below. Wonder if the community
>could kindly offer some comments?
>
There is no reason why this should not work. Note the performance / way it
works would I suspect bt very dependent on the concentration of drug
molecules in the system. Also note that you'll likely want to run multiple
simulations ideally from independent initial equilibrium geometries for
better sampling. For example you could run the protein itself for 500ns or
so and extract geometries every 20ns or so and use these as the seed
structures for the binding simulations.
Note I'm not entirely sure what these simulations ultimately give you -
they are perhaps useful for identifying alosteric sites or potential
intermediates in the binding process. They don't however, give you free
energy or timescale information so be aware of that. They can be used to
make nice movies though. :)
>
>
>a. Force field: ff12SB. It seems to provide good protein stability in
>Professor Case's studies.
>
>
>
>http://archive.ambermd.org/201211/0363.html
>
>
This is a probably a good choice yes. Note you also need parameters for
the ligand. GAFF is probably the reasonable (only?) choice for this unless
you parameterize each ligand manually. Note since charge is likely very
important here you probably want to take the time to do a multi
conformational resp fit on each ligand rather than relying on AM1-BCC.
>
>b. pmemd.CUDA.mpi precision model: SPFP
Should be good - and has the advantage of being deterministic. So you
could always rerun the simulation with a lower value of NTWX if you want
more 'resolution' in the trajectory file.
>c. solvent: TIP3 water in an 10A octahedron truncated water box
Probably good although some people prefer TIP4PEW.
>
>d. Minimization:
>
>
>
>&cntrl
>
> imin = 1,
>
> ntx = 1,
>
> maxcyc = 2000,
>
> ntmin = 2,
>
> ntpr = 100,
>
> ntf = 1,
>
> ntc = 1,
>
> ntb = 1,
>
> cut = 8.0,
>
> &end
>
Seems ok but use the CPU code for the minimization since it more robust
when it comes to initially strained structures (or use the CUDA SPDP or
DPDP precision models).
>
>
>e. Equi 1
>
>
>
>&cntrl
>
> imin = 0,
>
> irest = 0,
>
> ntx = 1,
>
> ntb = 1,
>
> cut = 8.0,
>
> ntr = 1,
>
> ntc = 2,
>
> ntf = 2,
>
> tempi = 0.0,
>
> temp0 = 310.0,
>
> ntt = 3,
>
> gamma_ln = 2.0,
>
> nstlim = 50000,
>
> dt = 0.002,
>
> ntpr = 1000,
>
> ntwx = 25000,
>
> ntwr = 25000,
>
> restraint_wt = 10.0,
>
> restraintmask = '${protein-ligand-mask}',
>
> iwrap = 1,
>
> ioutfm =1,
>
> ig = -1,
>
> &end
Seems reasonable to me - note you want to switch to constant pressure as
soon as possible to prevent vacuum bubbles so you might want to heat to
just 100K or so before finishing the heating with NPT.
>f. NPT equilibration ntt =3
>
>&cntrl
>
> imin = 0,
>
> irest = 1,
>
> ntx = 5,
>
> ntb = 2,
>
> ntp = 1,
>
> pres0 = 1.0,
>
> taup = 2.0,
>
> cut = 8,
>
> ntr = 0,
>
> ntc = 2,
>
> ntf = 2,
>
> temp0 = 310.0,
>
> tempi = 310.0,
>
> ntt = 3,
>
> gamma_ln = 2.0,
>
> nstlim = 50000,
>
> dt = 0.002,
>
> ntpr = 1000,
>
> ntwx = 25000,
>
> ntwr = 25000,
>
> iwrap = 1,
>
> ioutfm =1,
>
> ig = -1,
>
> &end
Also looks good - run this for long enough to make sure the density
equilibrates.
>g. NPT production run: the same as "equi 2" but change to ntt=1, taup =10
>to avoid the NANs issue.
What's the NAN issue? - I wasn't aware of a problem with ntt=3 and NTP.
One thing to note though is that you are probably best using langevin
thermostat for production run for better diffusion BUT note that the value
of gamma_ln essentially acts as a viscosity - the higher it is the more
viscous the system effectively is. Thus you may find there are optimum
values of gamma_ln so you might want to play around with a few simulations
run with different gamma_ln values and see if there is a difference.
Note if you switch to AMBER 14 in a few months when it is released you can
use the montecarlo barostat which willgive you NPT performance similar to
NVT/NVE and across two GPUs you will get significantly better scaling if
the motherboard supports peer to peer over PCI-E 3.0.
All the best
Ross
/\
\/
|\oss Walker
---------------------------------------------------------
| Associate Research Professor |
| San Diego Supercomputer Center |
| Adjunct Associate Professor |
| Dept. of Chemistry and Biochemistry |
| University of California San Diego |
| NVIDIA Fellow |
|
http://www.rosswalker.co.uk |
http://www.wmd-lab.org |
| Tel: +1 858 822 0854 | EMail:- ross.rosswalker.co.uk |
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Received on Mon Feb 24 2014 - 07:30:02 PST
