Python-based
Hierarchical
ENvironment
for
Integrated
Xtallography
Deformable elastic network (DEN) refinement is a refinement protocol that uses a sparse restraint network to maintain local model geometry while allowing for larger global domain motions over the course of several macrocycles of slow-cooling simulated annealing.
The DEN restraint network is a set of atom-atom distance restraints between atoms 3 to 5 Angstrom apart and within 0 and 10 residues in sequence space. The initial target values for these distance restraints may be set to corresponding distances in a reference model (such as a higher resolution structure or homology model) or to the distances in the input model. Over the course of a simulated annealing cycle, the target distances are frequently updated to allow for improved global fit to the diffraction data. DEN restraints have been shown to be particularly useful in improving models with low resolution data.
DEN was originally described by Brunger and colleagues in:
Schroder GF, Levitt M, and Brunger AT. (2010) Super-resolution biomolecular crystallography with low-resolution data. Nature 464(7292), 1218-22.
The impact of the DEN restraint network is controlled by two variable parameters, gamma, which controls the deformability of the network, and the weight, which controls how large of a contribution the DEN restraints make to the overall refinement energy term (see the above reference for details). To get the most out of a DEN refinement, it is important to run a grid search over combinations of gamma and weight values. This process is very time intensive, and is best carried out on a machine with many processors.
The default behavior is to optimize with six gamma values [0.0, 0.2, 0.4, 0.6, 0.8, 1.0] and five weights [3.0, 10.0, 30.0, 100.0, 300.0], which will run most optimally on a machine with at least 30 processor cores. The number of processors used by Phenix is controlled by the main.nproc parameter, so setting main.nproc=30 will run the optimization step on 30 processors.
How to run the command line version:
% phenix.refine model.pdb data.mtz strategy=den
See the full list of options below.
There is also a DEN refinement GUI for those who prefer a graphical interface.
phenix.den_refine uses a modified version of the default phenix.refine parameters optimized for DEN refinement.
The DEN-specific subset of refinement parameters:
refinement {
main {
nproc = Auto
}
refine {
strategy = *individual_sites rigid_body *individual_adp \
group_adp tls occupancies group_anomalous *den
}
den {
reference_file = None
gamma = 0.5
kappa = 0.1
weight = 30
sigma = 0.44
optimize = True
opt_gamma_values = 0 0.2 0.4 0.6 0.8 1
opt_weight_values = 3 10 30 100 300
num_cycles = 10
kappa_burn_in_cycles = 2
bulk_solvent_and_scale = True
refine_adp = True
final_refinement_cycle = False
verbose = False
annealing_type = *torsion cartesian
output_kinemage = False
restraint_network {
lower_distance_cutoff = 3
upper_distance_cutoff = 15
sequence_separation_low = 0
sequence_separation_limit = 10
exclude_hydrogens = True
ndistance_ratio = 1
export_den_pairs = False
den_network_file = None
}
}
tardy {
mode = *every_macro_cycle second_and_before_last once first first_half
start_temperature_kelvin = 3300
final_temperature_kelvin = 300
number_of_cooling_steps = 1440
number_of_time_steps = 1
time_step_pico_seconds = 0.001
prolsq_repulsion_function_changes {
c_rep = 100
k_rep = 1
}
}
simulated_annealing {
start_temperature = 3300
final_temperature = 300
cool_rate = 50
number_of_steps = 24
time_step = 0.001
}
}