homelette.routines

The homelette.routines submodule contains classes for model generation. Routines are the building blocks that are used to generate homology models.

Currently, a number of pre-implemented routines based on MODELLER, altMOD and ProMod3 are available. It is possible to implement custom routines for model generation and use them in the homelette framework.

Tutorials

The basics of generating homology models with pre-implemented modelling routines are presented in Tutorial 2. Complex modelling with homelette is introduced in Tutorial 6. Implementing custom modelling routines is discussed in Tutorial 4. Assembling custom pipelines is discussed in Tutorial 7.

Classes

The following standard modelling routines are implemented:

Modelling routines for loop modelliing:

Specifically for the modelling of complex structures, the following routines are implemented:

class homelette.routines.Routine_automodel_default(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling using the automodel class from modeller with a default parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation (default 1)

  • n_models (int) – Number of models generated (default 1)

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

modeller.automodel.automodel

library_schedule

modeller.automodel.autosched.normal

md_level

modeller.automodel.refine.very_fast

max_var_iterations

200

repeat_optmization

1
generate_models() → None

Generate models with the parameter set automodel_default.

Return type:

None

class homelette.routines.Routine_automodel_slow(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling using the automodel class from modeller with a slow parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation

  • n_models (int) – Number of models generated

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

modeller.automodel.automodel

library_schedule

modeller.automodel.autosched.slow

md_level

modeller.automodel.refine.very_slow

max_var_iterations

400

repeat_optmization

3
generate_models() → None

Generate models with the parameter set automodel_slow.

Return type:

None

class homelette.routines.Routine_altmod_default(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling using the Automodel_statistical_potential class from altmod with a default parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation

  • n_models (int) – Number of models generated

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (list) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

altmod.Automodel_statistical_potential

library_schedule

modeller.automodel.autosched.normal

md_level

modeller.automodel.refine.very_fast

max_var_iterations

200

repeat_optmization

1

Autmodel_statistical_potential uses the DOPE potential for model refinement.

generate_models() → None

Generate models with the parameter set altmod_default.

Return type:

None

class homelette.routines.Routine_altmod_slow(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling using the Automodel_statistical_potential class from altmod with a slow parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation

  • n_models (int) – Number of models generated

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (list) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

altmod.Automodel_statistical_potential

library_schedule

modeller.automodel.autosched.slow

md_level

modeller.automodel.refine.very_slow

max_var_iterations

400

repeat_optmization

3

Autmodel_statistical_potential uses the DOPE potential for model refinement.

generate_models() → None

Generate models with the parameter set altmod_slow.

Return type:

None

class homelette.routines.Routine_promod3(alignment: Type[Alignment], target: str, templates: Iterable, tag: str)

Class for performing homology modelling using the ProMod3 engine with default parameters.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (iterable) – The iterable containing the identifier of the template used for the modelling

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (iterable) – The iterable containing the identifier of the template used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • routine (str) – The identifier associated with this specific routine: promod3

  • models (list) – List of models generated by the execution of this routine

Raises:

  • ImportError – Unable to import dependencies

  • ValueError – Number of given templates is not 1

generate_models() → None

Generate models with the ProMod3 engine with default parameters.

Return type:

None

class homelette.routines.Routine_loopmodel_default(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, loop_selections: Iterable, n_models: int = 1, n_loop_models: int = 1)

Class for performing homology loop modelling using the loopmodel class from modeller with a default parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • loop_selections (Iterable) – Selection(s) with should be refined with loop modelling, in modeller format (example: [[‘18:A’, ‘22:A’], [‘29:A’, ‘33:A’]])

  • n_models (int) – Number of models generated (default 1)

  • n_loop_models (int) – Number of loop models generated for each model (default 1)

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • loop_selections (Iterable) – Selection(s) with should be refined with loop modelling

  • n_models (int) – Number of models generated

  • n_loop_models (int) – Number of loop models generated for each model

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • loop_selections

  • n_models

  • n_loop_models

The following modelling parameters are set for this class:

modelling parameter

value

model_class

modeller.automodel.LoopModel

library_schedule

modeller.automodel.autosched.normal

md_level

modeller.automodel.refine.very_fast

max_var_iterations

200

repeat_optmization

1

loop_library_schedule

modeller.automodel.autosched.loop

loop_md_level

modeller.automodel.refine.slow

loop_max_var_iterations

200

n_threads

1
generate_models() → None

Generate models with the parameter set loopmodel_default.

Return type:

None

class homelette.routines.Routine_loopmodel_slow(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, loop_selections: Iterable, n_models: int = 1, n_loop_models: int = 1)

Class for performing homology loop modelling using the loopmodel class from modeller with a slow parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • loop_selections (Iterable) – Selection(s) with should be refined with loop modelling, in modeller format (example: [[‘18:A’, ‘22:A’], [‘29:A’, ‘33:A’]])

  • n_models (int) – Number of models generated (default 1)

  • n_loop_models (int) – Number of loop models generated for each model (default 1)

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • loop_selections (Iterable) – Selection(s) with should be refined with loop modelling

  • n_models (int) – Number of models generated

  • n_loop_models (int) – Number of loop models generated for each model

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • loop_selections

  • n_models

  • n_loop_models

The following modelling parameters are set for this class:

modelling parameter

value

model_class

modeller.automodel.LoopModel

library_schedule

modeller.automodel.autosched.slow

md_level

modeller.automodel.refine.very_slow

max_var_iterations

400

repeat_optmization

3

loop_library_schedule

modeller.automodel.autosched.slow

loop_md_level

modeller.automodel.refine.very_slow

loop_max_var_iterations

400

n_threads

1
generate_models() → None

Generate models with the parameter set loopmodel_slow.

Return type:

None

class homelette.routines.Routine_complex_automodel_default(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling of complexes using the automodel class from modeller with a default parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation (default 1)

  • n_models (int) – Number of models generated (default 1)

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

modeller.automodel.automodel

library_schedule

modeller.automodel.autosched.normal

md_level

modeller.automodel.refine.very_fast

max_var_iterations

200

repeat_optmization

1
generate_models() → None

Generate complex models with the parameter set automodel_default.

Return type:

None

class homelette.routines.Routine_complex_automodel_slow(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling of complexes using the automodel class from modeller with a slow parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation (default 1)

  • n_models (int) – Number of models generated (default 1)

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (Iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

modeller.automodel.automodel

library_schedule

modeller.automodel.autosched.slow

md_level

modeller.automodel.refine.very_slow

max_var_iterations

400

repeat_optmization

3
generate_models() → None

Generate complex models with the parameters set automodel_slow.

Return type:

None

class homelette.routines.Routine_complex_altmod_default(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling of complexes using the Automodel_statistical_potential class from altmod with a default parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation

  • n_models (int) – Number of models generated

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (list) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

altmod.Automodel_statistical_potential

library_schedule

modeller.automodel.autosched.normal

md_level

modeller.automodel.refine.very_fast

max_var_iterations

200

repeat_optmization

1

Autmodel_statistical_potential uses the DOPE potential for model refinement.

generate_models() → None

Generate complex models with the parameter set altmod_default.

Return type:

None

class homelette.routines.Routine_complex_altmod_slow(alignment: Type[Alignment], target: str, templates: Iterable, tag: str, n_threads: int = 1, n_models: int = 1)

Class for performing homology modelling of complexes using the Automodel_statistical_potential class from altmod with a slow parameter set.

Parameters:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (iterable) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used in model generation

  • n_models (int) – Number of models generated

Variables:

  • alignment (Alignment) – The alignment object that will be used for modelling

  • target (str) – The identifier of the protein to model

  • templates (list) – The iterable containing the identifier(s) of the template(s) used for the modelling

  • tag (str) – The identifier associated with a specific execution of the routine

  • n_threads (int) – Number of threads used for model generation

  • n_models (int) – Number of models generated

  • routine (str) – The identifier associated with a specific routine

  • models (list) – List of models generated by the execution of this routine

Raises:

ImportError – Unable to import dependencies

Notes

The following modelling parameters can be set when initializing this Routine object:

  • n_models

  • n_threads

The following modelling parameters are set for this class:

modelling parameter

value

model_class

altmod.Automodel_statistical_potential

library_schedule

modeller.automodel.autosched.slow

md_level

modeller.automodel.refine.very_slow

max_var_iterations

400

repeat_optmization

3

Autmodel_statistical_potential uses the DOPE potential for model refinement.

generate_models() → None

Generate complex models with the parameter set altmod_slow.

Return type:

None