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@@ 46-76 (lines=31) @@
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| 43 |
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cmd.spectrum('b', 'green_yellow_red', sel1) |
| 44 |
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return GDT |
| 45 |
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| 46 |
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@cmd.extend |
| 47 |
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def gdt_ha(sel1, sel2): |
| 48 |
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""" |
| 49 |
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Calculate the GDT (Global Distance Test) High Accuracy between two RNA (using P atoms) selections. |
| 50 |
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Args: |
| 51 |
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sel1 (str): selection string for the protein to compare. |
| 52 |
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sel2 (str): selection string for the reference protein. |
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""" |
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# Select only C-alpha atoms |
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sel1 += ' and name P' |
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sel2 += ' and name P' |
| 57 |
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cutoffs = [0.5, 1, 2, 4] |
| 58 |
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# Align without transforming for raw alignment data |
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cmd.align(sel1, sel2, cycles=0, transform=0, object='aln') |
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# Get raw alignment data |
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mapping = cmd.get_raw_alignment('aln') |
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distances = [] |
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for pair in mapping: |
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atom1 = '%s and id %d' % (pair[0][0], pair[0][1]) |
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atom2 = '%s and id %d' % (pair[1][0], pair[1][1]) |
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dist = cmd.get_distance(atom1, atom2) |
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distances.append(dist) |
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distances = np.array(distances) |
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gdts = [(distances <= cutoff).mean() for cutoff in cutoffs] |
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out = np.array(list(zip(cutoffs, gdts))).flatten() |
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print("GDT_HS%d: %.4f; GDT_%d: %.4f; GDT_%d: %.4f; GDT_%d: %.4f;" % tuple(out)) |
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GDT = np.mean(gdts) |
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print("GDT_HS: %.4f" % GDT) |
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# Color by distance |
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cmd.spectrum('b', 'green_yellow_red', sel1) |
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return GDT |
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if __name__ == '__main__': |
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# Assume the first two objects are the models to compare |
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@@ 14-44 (lines=31) @@
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import numpy as np |
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print("Loaded calc_gdt.py: gdt(sel1, sel2), gdt_hs(sel1, sel2)") |
| 14 |
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@cmd.extend |
| 15 |
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def gdt(sel1, sel2): |
| 16 |
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""" |
| 17 |
|
Calculate the GDT (Global Distance Test) between two RNA (using P atoms) selections. |
| 18 |
|
Args: |
| 19 |
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sel1 (str): selection string for the protein to compare. |
| 20 |
|
sel2 (str): selection string for the reference protein. |
| 21 |
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""" |
| 22 |
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# Select only C-alpha atoms |
| 23 |
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sel1 += ' and name P' |
| 24 |
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sel2 += ' and name P' |
| 25 |
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cutoffs = [1.0, 2.0, 4.0, 8.0] |
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# Align without transforming for raw alignment data |
| 27 |
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cmd.align(sel1, sel2, cycles=0, transform=0, object='aln') |
| 28 |
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# Get raw alignment data |
| 29 |
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mapping = cmd.get_raw_alignment('aln') |
| 30 |
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distances = [] |
| 31 |
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for pair in mapping: |
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atom1 = '%s and id %d' % (pair[0][0], pair[0][1]) |
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atom2 = '%s and id %d' % (pair[1][0], pair[1][1]) |
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dist = cmd.get_distance(atom1, atom2) |
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distances.append(dist) |
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distances = np.array(distances) |
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gdts = [(distances <= cutoff).mean() for cutoff in cutoffs] |
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out = np.array(list(zip(cutoffs, gdts))).flatten() |
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print("GDT_%d: %.4f; GDT_%d: %.4f; GDT_%d: %.4f; GDT_%d: %.4f;" % tuple(out)) |
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GDT = np.mean(gdts) |
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print("GDT: %.4f" % GDT) |
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# Color by distance |
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cmd.spectrum('b', 'green_yellow_red', sel1) |
| 44 |
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return GDT |
| 45 |
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@cmd.extend |
| 47 |
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def gdt_ha(sel1, sel2): |
mmagnus /
rna-tools
