def testMATLAB(self):
sca = fromfile(pathDatafile('msa_Cys_knot_sca.dat'))
expect = sca.reshape((10, 10))
fasta = FASTA[:, :10]
result = buildSCAMatrix(fasta, turbo=True)
assert_array_almost_equal(expect, result, err_msg='turbo failed')
result = buildSCAMatrix(fasta, turbo=False)
assert_array_almost_equal(expect, result, err_msg='w/out turbo failed')
def testMATLAB(self):
sca = fromfile(pathDatafile("msa_Cys_knot_sca.dat"))
expect = sca.reshape((10, 10))
fasta = FASTA[:, :10]
result = buildSCAMatrix(fasta, turbo=True)
assert_array_almost_equal(expect, result, err_msg="turbo failed")
result = buildSCAMatrix(fasta, turbo=False)
assert_array_almost_equal(expect, result, err_msg="w/out turbo failed")
def testZero(self):
msa = array([list("ACCD"), list("ACDD"), list("ACCC"), list("ACDC")], dtype="|S1")
expect = array([log(0.975 / 0.025) * 0.5, log(0.95 / 0.05) * 0.5])
weight = ((expect ** 2).sum()) ** 0.5
expect = expect / weight * array([log(0.975 / 0.025), log(0.95 / 0.05)])
expect = (expect ** 2).mean() - (expect.mean()) ** 2
expect = array([[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, expect, 0.0], [0.0, 0.0, 0.0, expect]])
result = buildSCAMatrix(msa)
assert_array_almost_equal(expect, result, err_msg="turbo failed")
result = buildSCAMatrix(msa, turbo=False)
assert_array_almost_equal(expect, result, err_msg="w/out turbo failed")
def testZero(self):
msa = array([list('ACCD'),
list('ACDD'),
list('ACCC'),
list('ACDC')],
dtype='|S1')
expect = array([log(0.975 / .025) * .5, log(0.95 / .05) * .5])
weight = ((expect**2).sum())**.5
expect = expect / weight * array([log(0.975 / .025), log(0.95 / .05)])
expect = (expect**2).mean() - (expect.mean())**2
expect = array([
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., expect, 0.],
[0., 0., 0., expect],
])
result = buildSCAMatrix(msa)
assert_array_almost_equal(expect, result, err_msg='turbo failed')
result = buildSCAMatrix(msa, turbo=False)
assert_array_almost_equal(expect, result, err_msg='w/out turbo failed')
import prody.sequence as sequence
import prody
import matplotlib.pyplot as plt
alignment = prody.MSAFile("pkinase.fasta")
#get positions -> by hand for now
positions = [72, 83, 117, 119, 194, 251, 354, 355, 357, 429, 432]
#user alignSequenceToMSA instead to derive positions automatically
#set up webservice to get correspondance between MSA position and a particular PDB structure
alignment.setSlice(positions)
prody.writeMSA("test.fasta", alignment)
pa = prody.parseMSA("pocket_type1.fasta")
labs = pa.getLabels()
seqidmatrix = prody.buildSeqidMatrix(pa)
scamatrix = prody.buildSCAMatrix(pa)
tree = prody.calcTree(names=labs, distance_matrix=seqidmatrix)
plt.figure()
show = prody.showTree(tree, format='plt')
