Exemple #1
0
def main():
    # generate some virtual peptide sequences
    libnum = 1000  # 1000 sequences per sublibrary
    h = Helices(seqnum=libnum)
    r = Random(seqnum=libnum)
    n = AMPngrams(seqnum=libnum, n_min=4)
    h.generate_sequences()
    r.generate_sequences(proba='AMP')
    n.generate_sequences()

    # calculate molecular descirptors for the peptides
    d = PeptideDescriptor(seqs=np.hstack(
        (h.sequences, r.sequences, n.sequences)),
                          scalename='pepcats')
    d.calculate_crosscorr(window=7)

    # train a som on the descriptors and print / plot the training error
    som = SOM(x=12, y=12)
    som.fit(data=d.descriptor, epochs=100000, decay='hill')
    print("Fit error: %.4f" % som.error)
    som.plot_error_history(filename="som_error.png")

    # load known antimicrobial peptides (AMPs) and transmembrane sequences
    dataset = load_AMPvsTM()
    d2 = PeptideDescriptor(dataset.sequences, 'pepcats')
    d2.calculate_crosscorr(7)
    targets = np.array(libnum * [0] + libnum * [1] + libnum * [2] + 206 * [3])
    names = ['Helices', 'Random', 'nGrams', 'AMP']

    # plot som maps with location of AMPs
    som.plot_point_map(np.vstack((d.descriptor, d2.descriptor[206:])),
                       targets,
                       names,
                       filename="peptidesom.png")
    som.plot_density_map(np.vstack((d.descriptor, d2.descriptor)),
                         filename="density.png")
    som.plot_distance_map(colormap='Reds', filename="distances.png")

    colormaps = ['Oranges', 'Purples', 'Greens', 'Reds']
    for i, c in enumerate(set(targets)):
        som.plot_class_density(np.vstack((d.descriptor, d2.descriptor)),
                               targets,
                               c,
                               names,
                               colormap=colormaps[i],
                               filename='class%i.png' % c)

    # get neighboring peptides (AMPs / TMs) for a sequence of interest
    my_d = PeptideDescriptor(seqs='GLFDIVKKVVGALLAG', scalename='pepcats')
    my_d.calculate_crosscorr(window=7)
    som.get_neighbors(datapoint=my_d.descriptor,
                      data=d2.descriptor,
                      labels=dataset.sequences,
                      d=0)
Exemple #2
0
d = PeptideDescriptor(seqs=np.hstack((h.sequences, r.sequences, n.sequences)), scalename='pepcats')
d.calculate_crosscorr(window=7)

# train a som on the descriptors and print / plot the training error
som = SOM(x=12, y=12)
som.fit(data=d.descriptor, epochs=100000, decay='hill')
print("Fit error: %.4f" % som.error)
som.plot_error_history(filename="som_error.png")

# load known antimicrobial peptides (AMPs) and transmembrane sequences
dataset = load_AMPvsTM()
d2 = PeptideDescriptor(dataset.sequences, 'pepcats')
d2.calculate_crosscorr(7)
targets = np.array(libnum*[0] + libnum*[1] + libnum*[2] + 206*[3])
names = ['Helices', 'Random', 'nGrams', 'AMP']

# plot som maps with location of AMPs
som.plot_point_map(np.vstack((d.descriptor, d2.descriptor[206:])), targets, names, filename="peptidesom.png")
som.plot_density_map(np.vstack((d.descriptor, d2.descriptor)), filename="density.png")
som.plot_distance_map(colormap='Reds', filename="distances.png")

colormaps = ['Oranges', 'Purples', 'Greens', 'Reds']
for i, c in enumerate(set(targets)):
    som.plot_class_density(np.vstack((d.descriptor, d2.descriptor)), targets, c, names, colormap=colormaps[i],
                           filename='class%i.png' % c)

# get neighboring peptides (AMPs / TMs) for a sequence of interest
my_d = PeptideDescriptor(seqs='GLFDIVKKVVGALLAG', scalename='pepcats')
my_d.calculate_crosscorr(window=7)
som.get_neighbors(datapoint=my_d.descriptor, data=d2.descriptor, labels=dataset.sequences, d=0)