An example AutoGrid parameter file is given below:
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receptor 3ptb.pdbqs #macromolecule
gridfld 3ptb.maps.fld #grid_data_file
npts 60 60 60 #num.grid points in xyz
spacing .375 #spacing (Angstroms)
gridcenter -1.853 14.311 16.658 #xyz-coordinates or 'auto"
smooth 0.500 #store minimum energy within radius (Angstroms)
map 3ptb.C.map #filename of grid map
nbp_r_eps 4.00 0.0222750 12 6 #C-C lj
nbp_r_eps 3.75 0.0230026 12 6 #C-N lj
nbp_r_eps 3.60 0.0257202 12 6 #C-O lj
nbp_r_eps 4.00 0.0257202 12 6 #C-S lj
nbp_r_eps 3.00 0.0081378 12 6 #C-H lj
nbp_r_eps 3.00 0.0081378 12 6 #C-H lj
nbp_r_eps 3.00 0.0081378 12 6 #C-H lj
sol_par 12.77 0.6844 #C atomic fragmental volume, solvation param.
constant 0.000 #C grid map constant energy
map 3ptb.A.map #filename of grid map
nbp_r_eps 4.00 0.0222750 12 6 #A-C lj
nbp_r_eps 3.75 0.0230026 12 6 #A-N lj
nbp_r_eps 3.60 0.0257202 12 6 #A-O lj
nbp_r_eps 4.00 0.0257202 12 6 #A-S lj
nbp_r_eps 3.00 0.0081378 12 6 #A-H lj
nbp_r_eps 3.00 0.0081378 12 6 #A-H lj
nbp_r_eps 3.00 0.0081378 12 6 #A-H lj
sol_par 10.80 0.1027 #A atomic fragmental volume, solvation param.
constant 0.000 #A grid map constant energy
map 3ptb.N.map #filename of grid map
nbp_r_eps 3.75 0.0230026 12 6 #N-C lj
nbp_r_eps 3.50 0.0237600 12 6 #N-N lj
nbp_r_eps 3.35 0.0265667 12 6 #N-O lj
nbp_r_eps 3.75 0.0265667 12 6 #N-S lj
nbp_r_eps 2.75 0.0084051 12 6 #N-H lj
nbp_r_eps 2.75 0.0084051 12 6 #N-H lj
nbp_r_eps 2.75 0.0084051 12 6 #N-H lj
sol_par 0.00 0.0000 #N atomic fragmental volume, solvation param.
constant 0.000 #N grid map constant energy
map 3ptb.H.map #filename of grid map
nbp_r_eps 3.00 0.0081378 12 6 #H-C lj
nbp_r_eps 2.75 0.0084051 12 6 #H-N lj
nbp_r_eps 1.90 0.3280000 12 10 #H-O hb
nbp_r_eps 2.50 0.0656000 12 10 #H-S hb
nbp_r_eps 2.00 0.0029700 12 6 #H-H lj
nbp_r_eps 2.00 0.0029700 12 6 #H-H lj
nbp_r_eps 2.00 0.0029700 12 6 #H-H lj
sol_par 0.00 0.0000 #H atomic fragmental volume, solvation param.
constant 0.118 #H grid map constant energy
elecmap 3ptb.e.map #electrostatic potential map
dielectric -0.1146 #<0,distance-dep.diel; >0,constant
#fmap 3ptb.f.map #floating grid
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Note how hydrogen bonding is defined for oxygens. If a line in the parameter file contains a `10' in the fourth column, AutoGrid will treat this atom-pair as hydrogen bonding. So in the example above, the last 3 lines in the "mcp2_O.map" block will be treated as hydrogen bonds. AutoGrid scans for any polar hydrogens in the macromolecule. The vector from the hydrogen-donor, along with the vector from the probe-atom at the current grid point, are used to calculate the directional attenuation of the hydrogen bond. In this example, AutoGrid will calculate H-bonds between O-H, O- X and O- M .
Some examples of commented AutoDock parameter files are given below.
In this case, the ligand file ` xk263pm3.pdbq ' has been defined such that it contains 10 rotatable bonds. The docking will be sampled every 7500 steps, from cycle 45 to cycle 50. Either accepted or rejected states will be output. The trajectory file ` xk263pm3 .trj' will hold the state information required to generate the coordinates later on. The external grid energy is set to 0.0, which can allow greater freedom for ligand rotations during docking.
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fld 4phv.nbc_maps.fld # grid data file
map 4phv.nbc_C.map # C-atomic affinity map
map 4phv.nbc_N.map # N-atomic affinity map
map 4phv.nbc_O.map # O-atomic affinity map
map 4phv.nbc_H.map # H-atomic affinity map
map 4phv.nbc_e.map # electrostatics map
about -5.452 -8.626 -0.082 # ligand center
tran0 -5.452 -8.626 -0.082 # initial coordinates/A
quat0 1. 0. 0. 0. # initial quaternion:unit-vector(qx,qy,qz);angle/deg(qw)
ndihe 10 # number of rotatable bonds
dihe0 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. # initial dihedrals/deg
tstep 0.2 # translation step/A
qstep 5. # quaternion step/deg
trnrf 1. # trans reduction factor/per cycle
quarf 1. # quat reduction factor/per cycle
dihrf 1. # tors reduction factor/per cycle
intnbp_coeffs 1272653.000 1127.684 12 6 # C-C internal energy non-bond parameters
intnbp_coeffs 610155.100 783.345 12 6 # C-N internal energy non-bond parameters
intnbp_coeffs 588883.800 633.754 12 6 # C-O internal energy non-bond parameters
intnbp_coeffs 88604.240 226.910 12 6 # C-H internal energy non-bond parameters
intnbp_coeffs 266862.200 546.765 12 6 # N-N internal energy non-bond parameters
intnbp_coeffs 249961.400 445.918 12 6 # N-O internal energy non-bond parameters
intnbp_coeffs 39093.660 155.983 12 6 # N-H internal energy non-bond parameters
intnbp_coeffs 230584.400 368.677 12 6 # O-O internal energy non-bond parameters
intnbp_coeffs 38919.640 124.049 12 6 # O-H internal energy non-bond parameters
intnbp_coeffs 1908.578 46.738 12 6 # H-H internal energy non-bond parameters
rtrf 0.95 # RT reduction factor/per cycle
outlev 1 # diagnostic output level
rmstol 0.5 # cluster tolerance/A
trjfrq 7500 # trajectory frequency
trjbeg 45 # start trj output at cycle
trjend 50 # end trj output at cycle
trjout xk263pm3.trj # trajectory file
trjsel E # A=acc only;E=either acc or rej
extnrg 0.0 # external grid energy
e0max 0.0 # maximum allowable energy to start a run
simanneal # perform automated docking using simulated annealing
analysis # perform a ranked cluster analysis
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The next example DPF shows how to use the cluster mode in AutoDock. The PDBQ files containing the final docked conformations have been extracted from the AutoDock log files (using the UNIX grep command), and stored together in the file "vac1.new.dlg.pdbq". You can extract the "DOCKED:" records during the dockings, or after the dockings have finished. For example:
% egrep '^DOCKED: ' vac1.*.dlg | sed 's/^DOCKED: //' > vac1.grouped.dlg.pdbq
% egrep '^ATOM |HETATM|REMARK' vac1.*.dlg > vac1.grouped.dlg.pdbq
The tolerance for the positional rms deviation is set to 1.5Å, so only conformations with this rms deviation or less will be placed in the same cluster. All conformations will be written out, instead of just the lowest energy representative from each conformationally distinct cluster.
You may include the rmsnosym command, if you do not wish to use symmetry checking while clustering. Also, you must finish the DPF with the analysis command, to instruct AutoDock to perform the clustering and write out the histogram of docked conformations.
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rmstol 1.5 # cluster_tolerance/A
write_all # write all conformations in a cluster
cluster vac1.new.dlg.pdbq # structure binning
analysis # do cluster analysis on results
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This DPF shows how to set up a docking using the genetic algorithm (GA) in combination with the pseudo-Solis and Wets local search algorithm (psw1). This is also known as the Lamarckian Genetic Algorithm or LGA.
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seed time pid # for random number generator
fld 3ptb.maps.fld # grid data file
map 3ptb.C.map # C-atomic affinity map
map 3ptb.A.map # A-atomic affinity map
map 3ptb.N.map # N-atomic affinity map
map 3ptb.H.map # H-atomic affinity map
map 3ptb.e.map # electrostatics map
move benA.pdbq # small molecule
about -1.853 14.311 16.658 # small molecule center
tran0 random # initial coordinates/A or "random"
quat0 random # initial quaternion or "random"
ndihe 0 # number of initial torsions
dihe0 random # initial torsions
torsdof 0 0.3113 # num. non-H tors.degrees of freedom & coeff.
tstep 0.2 # translation step/A
qstep 5. # quaternion step/deg
trnrf 1. # trans reduction factor/per cycle
quarf 1. # quat reduction factor/per cycle
dihrf 1. # tors reduction factor/per cycle
intnbp_r_eps 4.00 0.0222750 12 6 #C-C lj
intnbp_r_eps 4.00 0.0222750 12 6 #C-A lj
intnbp_r_eps 3.75 0.0230026 12 6 #C-N lj
intnbp_r_eps 3.00 0.0081378 12 6 #C-H lj
intnbp_r_eps 4.00 0.0222750 12 6 #A-A lj
intnbp_r_eps 3.75 0.0230026 12 6 #A-N lj
intnbp_r_eps 3.00 0.0081378 12 6 #A-H lj
intnbp_r_eps 3.50 0.0237600 12 6 #N-N lj
intnbp_r_eps 2.75 0.0084051 12 6 #N-H lj
intnbp_r_eps 2.00 0.0029700 12 6 #H-H lj
outlev 1 # diagnostic output level
rmstol 0.5 # cluster tolerance/A
rmsref benA.pdbq # reference structure for RMS calc.
write_all # write all conformations in a cluster
extnrg 1000. # external grid energy
e0max 0. 10000 # max. allowable initial energy, max. num. retries
ga_pop_size 50 # number of individuals in population
ga_num_evals 150000 # maximum number of energy evaluations
ga_num_generations 27000 # maximum number of generations
ga_elitism 1 # num. of top individuals that automatically survive
ga_mutation_rate 0.02 # rate of gene mutation
ga_crossover_rate 0.80 # rate of crossover
ga_window_size 10 # num. of generations for picking worst individual
ga_cauchy_alpha 0 # ~mean of Cauchy distribution for gene mutation
ga_cauchy_beta 1 # ~variance of Cauchy distribution for gene mutation
set_ga # set the above parameters for GA
sw_max_its 300 # number of iterations of Solis & Wets local search
sw_max_succ 4 # number of consecutive successes before changing rho
sw_max_fail 4 # number of consecutive failures before changing rho
sw_rho 1.0 # size of local search space to sample
sw_lb_rho 0.01 # lower bound on rho
ls_search_freq 0.06 # probability of performing local search on an indiv.
set_psw1 # set the above pseudo-Solis & Wets parameters
ga_run 10 # do this many hybrid GA-LS runs
analysis # do cluster analysis on results
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