From a38e2acb300ebc7ce439487fe23f0426d6c4aa49 Mon Sep 17 00:00:00 2001 From: Diego Prada Date: Thu, 13 Jun 2024 15:56:23 -0600 Subject: [PATCH] In process --- .../user/tools/molecular_mechanics/1brs.mmtf | Bin 0 -> 61987 bytes .../molecular_mechanics/get_forces.ipynb | 90 ++-- .../get_non_bonded_potential_energy.ipynb | 352 +++------------- .../user/tools/molecular_mechanics/xxx.ipynb | 395 ++++++++++++++++++ molsysmt/build/add_missing_heavy_atoms.py | 1 + .../element/component/get_component_name.py | 11 - .../element/molecule/get_molecule_name.py | 14 +- 7 files changed, 501 insertions(+), 362 deletions(-) create mode 100644 docs/contents/user/tools/molecular_mechanics/1brs.mmtf create mode 100644 docs/contents/user/tools/molecular_mechanics/xxx.ipynb diff --git a/docs/contents/user/tools/molecular_mechanics/1brs.mmtf b/docs/contents/user/tools/molecular_mechanics/1brs.mmtf new file mode 100644 index 0000000000000000000000000000000000000000..04cef1cf7dfb5e27b50c7d4925f951e82da9b695 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indexnametypen atomsn groupsn componentschain indexentity indexentity nameindexnametypen atomsn groupsn componentschain indexentity indexentity name
0Barnaseprotein1727110100Barnase0protein 0protein1727110100protein 0
1Barstarprotein143289111Barstar1protein 1protein143289111protein 1
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Magnitude
[[67.1392933872994 -49.24023595824838 -126.36911331489682]
 [7.590139269828796 -4.8426863215863705 2.0255396582651883]
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 [-29.609317913651466 383.96366426721215 -554.4305481649935]
 [2.5788150089792907 2.188007801771164 -2.544081619940698]
 [0.9272228211630136 0.6260932385921478 -1.185140285640955]]
Unitskilojoule/(mole nanometer)
" + "
Magnitude
[[64.11752532888204 -25.612546341493726 -95.41171813267283]
 [29.116464272607118 -6.594275672920048 9.10719308629632]
 [-9.741821856005117 92.62677688943222 14.043880816083401]
 ...
 [-92.37486266554333 288.4447076302022 -452.25065422523767]
 [-1.4295995784923434 -1.4202567636966705 -0.5309852324426174]
 [-2.964325251057744 -0.30357155576348305 -2.1877003610134125]]
Unitskilojoule/(mole nanometer)
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indexnametypen atomsn groupsn componentschain indexentity indexentity nameindexnametypen atomsn groupsn componentschain indexentity indexentity name
0Barnaseprotein1727110100Barnase0protein 0protein1727110100protein 0
1Barstarprotein143289111Barstar1protein 1protein143289111protein 1
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Magnitude
[[21.801414270929804 8.175184348345258 0.020386384048607792 ...
  -0.09451040118419873 0.021165005335616334 -7.033840420141274]
 [-0.9160014457046187 -0.27825408410387786 0.0008114437300206142 ...
  0.00483401646805539 0.0002443448535342973 0.19720817843085264]
 [0.32827184031615975 0.10296400372886293 0.0005215121500806644 ...
  -0.0027315566462266967 0.0009208518733941802 -0.08533835183138144]
 ...
 [17.56110109289107 7.608162057559303 0.05185398955190158 ...
  -0.11864219067429492 0.09341631975046306 -7.733230153187739]
 [0.2621747339660761 0.09790205362877927 0.00043580910443803775 ...
  -0.00293578518052402 0.001598270627782176 -0.09157096906556225]
 [-0.09559354180359703 -0.2725356851435528 0.0021972802127524496 ...
  0.010619883783460803 -0.017851762060453514 0.504991520431256]]
Unitskilocalorie/mole
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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "distance = msm.structure.get_distances(molecular_system, selection='all in groups of molecule_name==\"Barnase\"',\n", " selection_2='all in groups of molecule_name==\"Barstar\"')\n", @@ -414,31 +204,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "fc77268f-ec08-4e6b-b90f-8656e6d85648", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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formn_atomsn_groupsn_componentsn_chainsn_moleculesn_entitiesn_proteinsn_structures
molsysmt.MolSys1577199222221
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formn_atomsn_groupsn_componentsn_chainsn_moleculesn_entitiesn_proteinsn_structures
pdbfixer.PDBFixer159619911111None
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "msm.info(barnase_barstar)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a7e0feee-8e0d-4c50-8192-ddb500782fc3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "barnase_barstar.topology." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "259e1cb1-d143-460e-8f48-57ce73e51e40", + "metadata": {}, + "outputs": [], + "source": [ + "barnase_barstar = msm.build.add_missing_heavy_atoms(barnase_barstar)\n", + "barnase_barstar = msm.build.add_missing_hydrogens(barnase_barstar, pH=7.4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63d7ccde-fbf3-4702-9214-04ae90d952d9", + "metadata": {}, + "outputs": [], + "source": [ + "msm.molecular_mechanics.potential_energy_minimization(barnase_barstar, in_place=True)\n", + "_ = msm.convert(barnase_barstar, to_form='barnase_barstar.pdb')\n", + "_ = msm.convert(barnase_barstar, to_form='barnase_barstar.h5msm')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/molsysmt/build/add_missing_heavy_atoms.py b/molsysmt/build/add_missing_heavy_atoms.py index 105e53b02..b633140cf 100644 --- a/molsysmt/build/add_missing_heavy_atoms.py +++ b/molsysmt/build/add_missing_heavy_atoms.py @@ -44,6 +44,7 @@ def add_missing_heavy_atoms(molecular_system, selection='all', syntax='MolSysMT' temp_molecular_system.addMissingAtoms() + return temp_molecular_system output_molecular_system = convert(temp_molecular_system, to_form=form_out) set(output_molecular_system, element='component', **atts_from_components) diff --git a/molsysmt/element/component/get_component_name.py b/molsysmt/element/component/get_component_name.py index 0f06f22e7..ecd26ec0e 100644 --- a/molsysmt/element/component/get_component_name.py +++ b/molsysmt/element/component/get_component_name.py @@ -17,9 +17,6 @@ def get_component_name(molecular_system, element='atom', selection='all', redefi else: redefine_types=False - import time - start=time.time() - component_index_from_atom = get_component_index(molecular_system, element='atom', selection='all', syntax='MolSysMT', redefine_indices=redefine_indices, skip_digestion=True) @@ -36,11 +33,6 @@ def get_component_name(molecular_system, element='atom', selection='all', redefi component_name_from_component = [] - end = time.time() - print('<<<', end-start) - - start=time.time() - for component_type, first_atom, n_atoms in zip(component_type_from_component, first_atom_per_component, n_atoms_per_component): @@ -121,9 +113,6 @@ def get_component_name(molecular_system, element='atom', selection='all', redefi raise NotImplementedError - end = time.time() - print('>>>', end-start) - else: from molsysmt.basic import get diff --git a/molsysmt/element/molecule/get_molecule_name.py b/molsysmt/element/molecule/get_molecule_name.py index 32ab68eba..cc21fff17 100644 --- a/molsysmt/element/molecule/get_molecule_name.py +++ b/molsysmt/element/molecule/get_molecule_name.py @@ -3,10 +3,10 @@ @digest() -def get_molecule_name(molecular_system, element='atom', selection='all', redefine_molecules=False, +def get_molecule_name(molecular_system, element='atom', selection='all', redefine_indices=False, redefine_names=False, syntax='MolSysMT', skip_digestion=False): - if redefine_molecules or redefine_names: + if redefine_indices or redefine_names: from ..component import get_component_name, get_component_index @@ -18,21 +18,21 @@ def get_molecule_name(molecular_system, element='atom', selection='all', redefin if element == 'atom': component_indices_from_atom = get_component_index(molecular_system, element='atom', - selection=selection, redefine_indices=redefine_molecules, syntax=syntax) + selection=selection, redefine_indices=redefine_indices, syntax=syntax) output = [molecule_names_from_component[ii] for ii in component_indices_from_atom] elif element == 'group': component_indices_from_group = get_component_index(molecular_system, element='group', - selection=selection, redefine_indices=redefine_molecules, syntax=syntax) + selection=selection, redefine_indices=redefine_indices, syntax=syntax) output = [molecule_names_from_component[ii] for ii in component_indices_from_group] elif element == 'component': component_indices_from_component = get_component_index(molecular_system, - element='component', selection=selection, redefine_indices=redefine_molecules, + element='component', selection=selection, redefine_indices=redefine_indices, syntax=syntax) output = [molecule_names_from_component[ii] for ii in component_indices_from_component] @@ -40,7 +40,7 @@ def get_molecule_name(molecular_system, element='atom', selection='all', redefin elif element == 'molecule': component_indices_from_component = get_component_index(molecular_system, - element='component', selection=selection, redefine_indices=redefine_molecules, + element='component', selection=selection, redefine_indices=redefine_indices, syntax=syntax) output = [molecule_names_from_component[ii] for ii in component_indices_from_component] @@ -48,7 +48,7 @@ def get_molecule_name(molecular_system, element='atom', selection='all', redefin elif element == 'entity': component_indices_from_entity = get_component_index(molecular_system, - element='entity', selection=selection, redefine_indices=redefine_molecules, + element='entity', selection=selection, redefine_indices=redefine_indices, syntax=syntax) output = []