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brownian is a top level plop command used to perform perturbed minimization sampling.

The general idea is that there are a few issues with Truncated Newton Minimization:

high sensitivity to initial conditions,
tendency to get stuck in local minima because at every step the energy is a non-increasing function

This attempts to address both of those problems. It addresses sensitivity to initial conditions by varying the starting coordinates slightly and performing a new TN minimization from each of the perturbed starting coordinates. It addresses the tendency of stopping in local minima by perturbing the minimized structure and seeing if there si a lower energy conformation accessible to the perturbed minimized structure. The thought being that the perturbation can move the structure out of a local energy well.

The default stopping rule is consecutive_greater with the number of consecutive steps set to 15. The default temperature is 300.

Example con file:

 datadirectory /home/jane/plop/data
 file logfile stdout
 energy parameters solvent vacuum
 load pdb my_pdb.pdb
 brownian modelfile brownian_models.pdb & 
      best_model brownian_best.pdb &
      min_flag yes &
      TEMPeRaTuRe 30.0 &
      reset_type none &
      minim mxitn 500 iter 500 verbose no alg tn niterup 1 minimend &
      steps 300

temp[erature] # - set the pseudo-temperature, the size of the perturbation is determined by this parameter
model[file] path - set the path to save each of the intermediate structures
steps # - perform a given number of steps
consecutive_greater # - continue sampling until this number of steps have a greater energy than the best energy sampled thus far
min_flag (yes/no) - minimize or not between consecutive perturbations
reset (never/every/lowest) - never => never reset to previous coordinates, every => reset to starting coordinates every step, lowest => reset to lowest energy coordinates after each TN minimization
min ... minimend - set parameters for the minimization between min and minimend
best_model path - the path to save the best energy model sampled

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