is a Python-enhanced molecular graphics tool. It excels at 3D visualization of proteins, small molecules, density, surfaces, and trajectories. It also includes molecular editing, ray tracing, and movies. Open Source version.
| generate2.py (pymol-v1.8.6.0.tar.bz2) | : | generate2.py (pymol-v2.1.0.tar.bz2) |
|---|
| # pymol -c generate2.py | | # pymol -c generate2.py |
| | |
| # NOTE: obsolete -- PyMOL now uses Dunbrack rotamers by default | | # NOTE: obsolete -- PyMOL now uses Dunbrack rotamers by default |
| | |
|
| | from __future__ import print_function |
| | |
| from chempy import io | | from chempy import io |
| from glob import glob | | from glob import glob |
| from copy import deepcopy | | from copy import deepcopy |
| | |
| sys.path.append(".") | | sys.path.append(".") |
| | |
| sc_raw = io.pkl.fromFile("sc_raw.pkl") | | sc_raw = io.pkl.fromFile("sc_raw.pkl") |
| | |
| cutoff_angle = 35.0 | | cutoff_angle = 35.0 |
| | |
| sc_clus = {} | | sc_clus = {} |
| | |
| # this loop clusters and averages sidechain conformations | | # this loop clusters and averages sidechain conformations |
| | |
| for resn in sc_raw.keys(): | | for resn in sc_raw.keys(): |
| resn_list = sc_raw[resn] | | resn_list = sc_raw[resn] |
| resn_key_dict = {} | | resn_key_dict = {} |
| # find the most commonly encountered set of torsions | | # find the most commonly encountered set of torsions |
| # (it is most likely to be correct) | | # (it is most likely to be correct) |
| for set in resn_list: | | for set in resn_list: |
|
| lst = deepcopy(set.keys()) | | lst = deepcopy(list(set.keys())) |
| lst.sort() | | lst.sort() |
| tup = tuple(lst) | | tup = tuple(lst) |
|
| if resn_key_dict.has_key(tup): | | if tup in resn_key_dict: |
| resn_key_dict[tup] = resn_key_dict[tup] + 1 | | resn_key_dict[tup] = resn_key_dict[tup] + 1 |
| else: | | else: |
| resn_key_dict[tup] = 1 | | resn_key_dict[tup] = 1 |
| key_lst = [] | | key_lst = [] |
| for a in resn_key_dict.keys(): | | for a in resn_key_dict.keys(): |
| key_lst.append((resn_key_dict[a],a)) | | key_lst.append((resn_key_dict[a],a)) |
| key_lst.sort() | | key_lst.sort() |
| resn_key = list(key_lst[-1][1]) | | resn_key = list(key_lst[-1][1]) |
| resn_key.sort() | | resn_key.sort() |
|
| print resn,resn_key | | print(resn,resn_key) |
| n_dihe = len(resn_key) | | n_dihe = len(resn_key) |
|
| print resn,len(sc_raw[resn])#,resn_key | | print(resn,len(sc_raw[resn]))#,resn_key |
| if n_dihe: # not glycine or alanine | | if n_dihe: # not glycine or alanine |
| # list of dictionaries | | # list of dictionaries |
| # [ {(...)=avg1, (...)=avg2, ... }, {(..)=avg1, ... }, ... ] | | # [ {(...)=avg1, (...)=avg2, ... }, {(..)=avg1, ... }, ... ] |
| avg_ang = [] | | avg_ang = [] |
| # list of list of dictionaries: | | # list of list of dictionaries: |
| # [ [ {(...)=ang1, (...)=ang2, ... }, {(..)=ang1, ... }, ... ] ] | | # [ [ {(...)=ang1, (...)=ang2, ... }, {(..)=ang1, ... }, ... ] ] |
| # where each row corresponds to the | | # where each row corresponds to the |
| # averages used to generate the avg_ang [ [ {(..)=}, | | # averages used to generate the avg_ang [ [ {(..)=}, |
| all_ang = [] | | all_ang = [] |
| while len(resn_list): # do we have any more cases to consider? | | while len(resn_list): # do we have any more cases to consider? |
| # yes, lets consider the conformation "cur" | | # yes, lets consider the conformation "cur" |
| cur = resn_list.pop() | | cur = resn_list.pop() |
| if len(cur)!=n_dihe: | | if len(cur)!=n_dihe: |
| # print "skipping...",cur | | # print "skipping...",cur |
| continue | | continue |
| flag=1 | | flag=1 |
| for k in resn_key: | | for k in resn_key: |
|
| if not cur.has_key(k): | | if k not in cur: |
| flag=0 | | flag=0 |
| if not flag: | | if not flag: |
| continue | | continue |
| recomp_avg = None | | recomp_avg = None |
| closest = None | | closest = None |
| min_dev = 361.00 | | min_dev = 361.00 |
| cnt = 0 | | cnt = 0 |
| for a in avg_ang: # we're going to compare it against all knowns | | for a in avg_ang: # we're going to compare it against all knowns |
| max_dev = 0.0 | | max_dev = 0.0 |
| avg_dev = 0.0 | | avg_dev = 0.0 |
| | |
| skipping to change at line 124 | | skipping to change at line 126 |
|---|
| a['FREQ'] = freq | | a['FREQ'] = freq |
| sort_ang.append((freq,a)) | | sort_ang.append((freq,a)) |
| cnt = cnt + 1 | | cnt = cnt + 1 |
| sort_ang.sort() | | sort_ang.sort() |
| avg_ang = [] | | avg_ang = [] |
| for a in sort_ang: | | for a in sort_ang: |
| avg_ang.insert(0,a[1]) | | avg_ang.insert(0,a[1]) |
| # at this point, we have a list of clustered, averaged torsions | | # at this point, we have a list of clustered, averaged torsions |
| # which can be used by the sidechain placement algorithm | | # which can be used by the sidechain placement algorithm |
| sc_clus[resn] = avg_ang | | sc_clus[resn] = avg_ang |
|
| print " reduced to:",len(sc_clus[resn]) | | print(" reduced to:",len(sc_clus[resn])) |
| | |
| io.pkl.toFile(sc_clus,"sc_library.pkl") | | io.pkl.toFile(sc_clus,"sc_library.pkl") |
| | | |
| End of changes. 7 change blocks. |
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| 6 lines changed or deleted | | 8 lines changed or added |
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