Albocycline

---

Summary:

Albocycline is a macrolactone isolated from Streptomyces maizeus. This macrolactone has recently been identified as a promising lead antibiotic candidate for the treatment of both vancomycin-resistant S. aureus (VRSA) and MRSA. In our previous example we showed in detail how to calculate the consistency of theoretical modeling with experiment. The user gained insight on how to run biceps calculations in full. In this example, we will repeat the process parallelizing lambda values with multiprocessing.

To convert this Jupyter Notebook into a script use the following command:

$ jupyter nbconvert --to python albocycline.ipynb

---

import numpy as np
import pandas as pd
import biceps
import warnings
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings('ignore', category=FutureWarning)

####### Data and Output Directories #######
print(f"Possible input data extensions: {biceps.toolbox.list_possible_extensions()}")
energies = np.loadtxt('albocycline/albocycline_QMenergies.dat')*627.509  # convert from hartrees to kcal/mol
energies = energies/0.5959   # convert to reduced free energies F = f/kT
energies -= energies.min()  # set ground state to zero, just in case
dataFiles = 'albocycline/J_NOE'
input_data = biceps.toolbox.sort_data(dataFiles)
print(f"Input data: {biceps.toolbox.list_extensions(input_data)}")
outdir = 'results'
biceps.toolbox.mkdir(outdir)

Possible input data extensions: ['H', 'Ca', 'N', 'J', 'noe', 'pf']
Input data: ['J', 'noe']

####### Parameters #######

nsteps = 100000      # for testing
#nsteps=10000000     # for production

print(f"nSteps of sampling: {nsteps}")
n_lambdas = 3
lambda_values = np.linspace(0.0, 1.0, n_lambdas)
parameters = [
        {"ref": 'uniform', "sigma": (0.05, 20.0, 1.02)},
        {"ref": 'exponential', "sigma": (0.05, 5.0, 1.02), "gamma": (0.2, 5.0, 1.01)}
        ]
pd.DataFrame(parameters)

nSteps of sampling: 100000

	ref	sigma	gamma
0	uniform	(0.05, 20.0, 1.02)	NaN
1	exponential	(0.05, 5.0, 1.02)	(0.2, 5.0, 1.01)

####### Multiprocessing Lambda values #######
@biceps.multiprocess(iterable=lambda_values)
def mp_lambdas(lam):
    ensemble = biceps.Ensemble(lam, energies)
    ensemble.initialize_restraints(input_data, parameters)
    sampler = biceps.PosteriorSampler(ensemble)
    sampler.sample(nsteps=nsteps, verbose=False)
    sampler.traj.process_results(f"{outdir}/traj_lambda{lam}.npz")

Number of CPUs: 10
Number of processes: 3

100%|███████████████████████████████████████████████████████████| 100000/100000 [00:01<00:00, 62547.07it/s]

Accepted 70.378 %

100%|███████████████████████████████████████████████████████████| 100000/100000 [00:01<00:00, 62460.76it/s]

Accepted [ ...Nuisance paramters..., state] %
Accepted 67.006 %

100%|███████████████████████████████████████████████████████████| 100000/100000 [00:01<00:00, 62394.44it/s]

Accepted [32.549 31.386 31.386  6.443] %

Accepted [ ...Nuisance paramters..., state] %

Accepted 71.999 %


Accepted [32.549 31.725 31.725  2.732] %

Accepted [ ...Nuisance paramters..., state] %

Accepted [32.643 31.537 31.537  7.819] %

####### Posterior Analysis #######
A = biceps.Analysis(outdir, nstates=len(energies))

try:
    %matplotlib inline
    A.plot()
except:
    print(ipython_warning)

not all state sampled, these states [86] are not sampled
not all state sampled, these states [ 9 15 48] are not sampled
not all state sampled, these states [ 4  5  7  9 11 15 16 18 21 24 25 27 28 34 40 42 44 48 51 54 58 62 76 77
 81 82 86 87 94 95 98] are not sampled

# NOTE: The following cell is for pretty notebook rendering

from IPython.core.display import HTML

def css_styling():
    styles = open("../../../theme.css", "r").read()
    return HTML(styles)

css_styling()