| | |
- builtins.object
-
- FoldTreeRequest
- ToolboxKeys
- rosetta.protocols.moves.Mover(builtins.object)
-
- LoopMover
-
- LoopBuilder
- LoopModeler
- LoopProtocol
- rosetta.protocols.moves.MoverCreator(builtins.object)
-
- LoopBuilderCreator
- LoopModelerCreator
- LoopProtocolCreator
class FoldTreeRequest(builtins.object) |
| |
Represent the types of fold tree supported by LoopMover.
This enum supports the bitwise `and` and `or` operators. Strictly
speaking, this is an abuse of enum because both operators can return values
that are not contained in the enum. But this design meets two key
requirements. First, programmers have to work with the enumerated values.
Second, bitwise logic can be used to easily combine flags and to determine
how composite requests should be satisfied.
|
| |
Methods defined here:
- __eq__(...) from builtins.PyCapsule
- __eq__(rosetta.protocols.loop_modeling.FoldTreeRequest, rosetta.protocols.loop_modeling.FoldTreeRequest) -> bool
- __hash__(...) from builtins.PyCapsule
- __hash__(rosetta.protocols.loop_modeling.FoldTreeRequest) -> int
- __init__(...) from builtins.PyCapsule
- __init__(*args, **kwargs)
Overloaded function.
1. __init__(rosetta.protocols.loop_modeling.FoldTreeRequest, int) -> NoneType
2. __init__(rosetta.protocols.loop_modeling.FoldTreeRequest, int) -> NoneType
- __int__(...) from builtins.PyCapsule
- __int__(rosetta.protocols.loop_modeling.FoldTreeRequest) -> int
- __ne__(...) from builtins.PyCapsule
- __ne__(rosetta.protocols.loop_modeling.FoldTreeRequest, rosetta.protocols.loop_modeling.FoldTreeRequest) -> bool
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
- __repr__(...) from builtins.PyCapsule
- __repr__(rosetta.protocols.loop_modeling.FoldTreeRequest) -> str
Data and other attributes defined here:
- FTR_DONT_CARE = FoldTreeRequest.FTR_DONT_CARE
- FTR_LOOPS_WITH_CUTS = FoldTreeRequest.FTR_LOOPS_WITH_CUTS
- FTR_SIMPLE_TREE = FoldTreeRequest.FTR_SIMPLE_TREE
|
class LoopBuilder(LoopMover) |
| |
Build loops from scratch.
Kale Kundert
Roland A. Pache, PhD
Building a loop from scratch is useful in two scenarios. The
first is when there's missing density that needs to be modeled, and the
second is when the whole loop modeling algorithm needs to be benchmarked.
This mover uses kinematic closure (KIC) to build loops. By default, the
loop are built by picking phi and psi values from a Ramachandran
distribution and setting all other DOFs to ideal values. Phi and psi
values can also be picked using fragment libraries. Loop building succeeds
when a model is found that passes a more-lenient-than-usual bump check. If
no such model is found after 1000 iterations, the mover gives up and
reports failure.
This process can be very slow for long loops, because there's nothing
guiding the algorithm towards the right solution. By default, torsions are
just being randomly picked, and they often won't fit in the relatively
narrow space that's available. The problem is worse for interior loops
than it is for surface loops, of course. This algorithm seems to work well
enough on 12 residue loops, but beyond that it may be necessary to develop
a smarter algorithm that preferentially builds into free space.
|
| |
- Method resolution order:
- LoopBuilder
- LoopMover
- rosetta.protocols.moves.Mover
- builtins.object
Methods defined here:
- __init__(...) from builtins.PyCapsule
- __init__(*args, **kwargs)
Overloaded function.
1. __init__(handle) -> NoneType
2. __init__(handle, rosetta.protocols.loop_modeling.LoopBuilder) -> NoneType
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
- assign(...) from builtins.PyCapsule
- assign(self : rosetta.protocols.loop_modeling.LoopBuilder, : rosetta.protocols.loop_modeling.LoopBuilder) -> rosetta.protocols.loop_modeling.LoopBuilder
- get_max_attempts(...) from builtins.PyCapsule
- get_max_attempts(rosetta.protocols.loop_modeling.LoopBuilder) -> int
Return the number of times KIC will be invoked before the
LoopBuilder gives up.
- get_name(...) from builtins.PyCapsule
- get_name(rosetta.protocols.loop_modeling.LoopBuilder) -> str
LoopMover::get_name
- get_score_function(...) from builtins.PyCapsule
- get_score_function(rosetta.protocols.loop_modeling.LoopBuilder) -> rosetta.core.scoring.ScoreFunction
Get the score function to be used on the next call to apply().
- set_max_attempts(...) from builtins.PyCapsule
- set_max_attempts(self : rosetta.protocols.loop_modeling.LoopBuilder, attempts : int) -> NoneType
Specify how many time to invoke KIC before giving up.
- set_score_function(...) from builtins.PyCapsule
- set_score_function(self : rosetta.protocols.loop_modeling.LoopBuilder, score_function : rosetta.core.scoring.ScoreFunction) -> NoneType
Set the score function to be used on the next call to apply().
- use_fragments(...) from builtins.PyCapsule
- use_fragments(self : rosetta.protocols.loop_modeling.LoopBuilder, frag_libs : rosetta.utility.vector1_std_shared_ptr_const_core_fragment_FragSet_t) -> NoneType
Use the given fragment libraries when building the loop.
Methods inherited from LoopMover:
- apply(...) from builtins.PyCapsule
- apply(self : rosetta.protocols.loop_modeling.LoopMover, pose : rosetta.core.pose.Pose) -> NoneType
Sample the pose in the regions specified by get_loops().
The parent class apply() method automatically sets up a fold
tree (if necessary) and keeps track of whether or not the move succeeded.
Child classes should reimplement do_apply() instead of this method.
- get_children_names(...) from builtins.PyCapsule
- get_children_names(*args, **kwargs)
Overloaded function.
1. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
2. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string, indent : str) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
- get_loop(...) from builtins.PyCapsule
- get_loop(self : rosetta.protocols.loop_modeling.LoopMover, index : int) -> rosetta.protocols.loops.Loop
Return the specified loop.
- get_loops(...) from builtins.PyCapsule
- get_loops(*args, **kwargs)
Overloaded function.
1. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
2. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
- request_fold_tree(...) from builtins.PyCapsule
- request_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loop_modeling.FoldTreeRequest
Return an enum representing the kind of fold tree that is
compatible with this mover.
The FoldTreeRequest enum values can be combined using the
bitwise logical operators. For example, you can request either the
standard fold tree or a simple fold tree with `FTR_LOOPS_WITH_CUTS |
FTR_SIMPLE_TREE.`
- set_loop(...) from builtins.PyCapsule
- set_loop(self : rosetta.protocols.loop_modeling.LoopMover, loop : rosetta.protocols.loops.Loop) -> NoneType
Set the loop to be sampled on the next call to apply().
- set_loops(...) from builtins.PyCapsule
- set_loops(*args, **kwargs)
Overloaded function.
1. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
2. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
- setup_fold_tree(...) from builtins.PyCapsule
- setup_fold_tree(pose : rosetta.core.pose.Pose, loops : rosetta.protocols.loops.Loops, request : rosetta.protocols.loop_modeling.FoldTreeRequest) -> NoneType
Setup the given pose with a fold tree that is compatible with the
given loops and requests.
- trust_fold_tree(...) from builtins.PyCapsule
- trust_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> NoneType
Promise that the calling code will setup a fold tree compatible
with request_fold_tree(). If this method is not called, this mover will
setup a fold tree on its own every time apply() is called.
- was_successful(...) from builtins.PyCapsule
- was_successful(rosetta.protocols.loop_modeling.LoopMover) -> bool
Return true if the previous move was successful.
Methods inherited from rosetta.protocols.moves.Mover:
- clear_info(...) from builtins.PyCapsule
- clear_info(rosetta.protocols.moves.Mover) -> NoneType
Strings container can be used to return miscellaneous info (as std::string) from a mover, such as notes about the results of apply(). The job distributor (Apr 09 vintage) will check this function to see if your protocol wants to add string info to the Job that ran this mover. One way this can be useful is that later, a JobOutputter may include/append this info to an output file.
clear_info is called by jd2 before calling apply
- clone(...) from builtins.PyCapsule
- clone(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Return a clone of the Mover object.
- create(...) from builtins.PyCapsule
- create(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- fresh_instance(...) from builtins.PyCapsule
- fresh_instance(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Generates a new Mover object freshly created with the default ctor.
- get_additional_output(...) from builtins.PyCapsule
- get_additional_output(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
fpd
Mechanism by which a mover may return multiple output poses from a single input pose.
- get_current_job(...) from builtins.PyCapsule
- get_current_job(rosetta.protocols.moves.Mover) -> protocols::jobdist::BasicJob
- get_current_tag(...) from builtins.PyCapsule
- get_current_tag(rosetta.protocols.moves.Mover) -> str
A tag is a unique identifier used to identify structures produced
by this Mover. get_current_tag() returns the tag, and set_current_tag( std::string tag )
sets the tag. This functionality is not intended for use with the 2008 job distributor.
- get_input_pose(...) from builtins.PyCapsule
- get_input_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_last_move_status(...) from builtins.PyCapsule
- get_last_move_status(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.MoverStatus
returns status after an apply(). The job distributor (august 08 vintage) will check this function to see if your protocol wants to filter its results - if your protocol wants to say "that run was no good, skip it" then use the protected last_move_status(MoverStatus) to change the value that this function will return.
- get_native_pose(...) from builtins.PyCapsule
- get_native_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_self_ptr(...) from builtins.PyCapsule
- get_self_ptr(*args, **kwargs)
Overloaded function.
1. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
2. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- get_self_weak_ptr(...) from builtins.PyCapsule
- get_self_weak_ptr(*args, **kwargs)
Overloaded function.
1. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_const_protocols_moves_Mover_t
2. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_protocols_moves_Mover_t
- get_type(...) from builtins.PyCapsule
- get_type(rosetta.protocols.moves.Mover) -> str
- info(...) from builtins.PyCapsule
- info(*args, **kwargs)
Overloaded function.
1. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
non-const accessor
2. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
const accessor
- last_proposal_density_ratio(...) from builtins.PyCapsule
- last_proposal_density_ratio(rosetta.protocols.moves.Mover) -> float
- name(...) from builtins.PyCapsule
- name() -> str
- register_options(...) from builtins.PyCapsule
- register_options(*args, **kwargs)
Overloaded function.
1. register_options() -> NoneType
Overload this static method if you access options within the mover.
These options will end up in -help of your application if users of this mover call register_options.
Do this recursively!
If you use movers within your mover, call their register_options in your register_options() method.
2. register_options() -> NoneType
3. register_options() -> NoneType
4. register_options() -> NoneType
5. register_options() -> NoneType
6. register_options() -> NoneType
7. register_options() -> NoneType
8. register_options() -> NoneType
9. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
10. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
11. register_options() -> NoneType
Associates relevant options with the SnugDock class
12. register_options() -> NoneType
Associates relevant options with the SnugDockProtocol class
13. register_options() -> NoneType
Register the options used by this mover with the global options
system.
14. register_options() -> NoneType
15. register_options() -> NoneType
Associate relevant options with the TemperedDocking class.
16. register_options() -> NoneType
17. register_options() -> NoneType
18. register_options() -> NoneType
Associates relevant options with the TemperedDocking class.
19. register_options() -> NoneType
20. register_options() -> NoneType
Associates relevant options with the ConstraintSetMover class
21. register_options() -> NoneType
22. register_options() -> NoneType
Associates relevant options with the DockingInitialPerturbation class
23. register_options() -> NoneType
Associates relevant options with the DockingProtocol class
24. register_options() -> NoneType
Associates relevant options with the TemperedDocking class
25. register_options() -> NoneType
26. register_options() -> NoneType
27. register_options() -> NoneType
28. register_options() -> NoneType
register options
29. register_options() -> NoneType
30. register_options() -> NoneType
Registers applicable options
31. register_options() -> NoneType
Register options with the option system.
32. register_options() -> NoneType
33. register_options() -> NoneType
34. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
35. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycleContainer class
36. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
37. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
38. register_options() -> NoneType
39. register_options() -> NoneType
Register options with the option system.
40. register_options() -> NoneType
- reinitialize_for_each_job(...) from builtins.PyCapsule
- reinitialize_for_each_job(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be freshly regenerated on
each use.
- reinitialize_for_new_input(...) from builtins.PyCapsule
- reinitialize_for_new_input(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be regenerated when the input
pose is about to change, (for example, if the Mover has special code on the first apply() that is only valid for
that one input pose).
- reset_status(...) from builtins.PyCapsule
- reset_status(rosetta.protocols.moves.Mover) -> NoneType
resets status to SUCCESS, meant to be used before an apply(). The job distributor (august 08 vintage) uses this to ensure non-accumulation of status across apply()s.
- set_current_job(...) from builtins.PyCapsule
- set_current_job(self : rosetta.protocols.moves.Mover, job : protocols::jobdist::BasicJob) -> NoneType
////////////////////////////end Job Distributor interface////////////////////////////////////////
- set_current_tag(...) from builtins.PyCapsule
- set_current_tag(self : rosetta.protocols.moves.Mover, new_tag : str) -> NoneType
- set_input_pose(...) from builtins.PyCapsule
- set_input_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for poses contained for rms
- set_native_pose(...) from builtins.PyCapsule
- set_native_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for native poses contained for rms ---- we should get rid of this method? it is widely used, but a bit unsafe
- set_type(...) from builtins.PyCapsule
- set_type(self : rosetta.protocols.moves.Mover, setting : str) -> NoneType
- test_move(...) from builtins.PyCapsule
- test_move(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
: Unit test support function. Apply one move to a given pose.
Allows extra test specific functions to be called before applying
- type(...) from builtins.PyCapsule
- type(*args, **kwargs)
Overloaded function.
1. type(rosetta.protocols.moves.Mover) -> str
2. type(self : rosetta.protocols.moves.Mover, type_in : str) -> NoneType
|
class LoopModeler(LoopMover) |
| |
Attempt to find the native structure for one or more loops.
The typical loop modeling simulation in rosetta has three steps:
loop building, centroid refinement, and fullatom refinement. LoopModeler
carries out all three of these steps and allows each to to be enabled,
disabled, and otherwise configured. By default, nothing needs to be
specified and a standard loop modeling simulation will be performed.
Note that this class is a fairly thin wrapper around other LoopMovers.
LoopBuilder and LoopProtocol in particular do all the heavy lifting. The
main role of this class is actually to provide a reasonable set of default
values, some nice tracer output, and a sophisticated parse_my_tag() method
for use with rosetta scripts.
Note that LoopModeler doesn't implement a proper copy constructor. (In
fact, no LoopMover does.) This means that if a simulation breaks and
nstruct > 1, the remaining simulations will probably break for weird
reasons.
|
| |
- Method resolution order:
- LoopModeler
- LoopMover
- rosetta.protocols.moves.Mover
- builtins.object
Methods defined here:
- __init__(...) from builtins.PyCapsule
- __init__(*args, **kwargs)
Overloaded function.
1. __init__(handle) -> NoneType
2. __init__(handle, rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
- assign(...) from builtins.PyCapsule
- assign(self : rosetta.protocols.loop_modeling.LoopModeler, : rosetta.protocols.loop_modeling.LoopModeler) -> rosetta.protocols.loop_modeling.LoopModeler
- build_stage(...) from builtins.PyCapsule
- build_stage(rosetta.protocols.loop_modeling.LoopModeler) -> rosetta.protocols.loop_modeling.LoopBuilder
Return a pointer to the build stage mover.
- centroid_stage(...) from builtins.PyCapsule
- centroid_stage(rosetta.protocols.loop_modeling.LoopModeler) -> protocols::loop_modeling::LoopProtocol
Return a pointer to the centroid stage mover.
- clone(...) from builtins.PyCapsule
- clone(rosetta.protocols.loop_modeling.LoopModeler) -> rosetta.protocols.moves.Mover
Return a shallow copy of this object.
- disable_build_stage(...) from builtins.PyCapsule
- disable_build_stage(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Disable the build stage.
- disable_centroid_stage(...) from builtins.PyCapsule
- disable_centroid_stage(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Disable the centroid stage.
- disable_fullatom_stage(...) from builtins.PyCapsule
- disable_fullatom_stage(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Disable the fullatom stage.
- enable_build_stage(...) from builtins.PyCapsule
- enable_build_stage(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Enable the build stage.
- enable_centroid_stage(...) from builtins.PyCapsule
- enable_centroid_stage(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Enable the centroid stage.
- enable_fullatom_stage(...) from builtins.PyCapsule
- enable_fullatom_stage(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Enable the fullatom stage.
- fullatom_stage(...) from builtins.PyCapsule
- fullatom_stage(rosetta.protocols.loop_modeling.LoopModeler) -> protocols::loop_modeling::LoopProtocol
Return a pointer to the fullatom stage mover.
- get_name(...) from builtins.PyCapsule
- get_name(rosetta.protocols.loop_modeling.LoopModeler) -> str
LoopMover::get_name
- get_task_factory(...) from builtins.PyCapsule
- get_task_factory(*args, **kwargs)
Overloaded function.
1. get_task_factory(rosetta.protocols.loop_modeling.LoopModeler) -> rosetta.core.pack.task.TaskFactory
Get the task factory to be used on the next call to apply().
If no task factory has been set, this will raise an exception.
2. get_task_factory(self : rosetta.protocols.loop_modeling.LoopModeler, fallback : rosetta.core.pack.task.TaskFactory) -> rosetta.core.pack.task.TaskFactory
Get the task factory to be used on the next call to apply().
If no task factory has been set, the fallback will be returned.
- set_cen_scorefxn(...) from builtins.PyCapsule
- set_cen_scorefxn(self : rosetta.protocols.loop_modeling.LoopModeler, scorefxn : rosetta.core.scoring.ScoreFunction) -> NoneType
Set the score function to be used for the centroid stage.
- set_fa_scorefxn(...) from builtins.PyCapsule
- set_fa_scorefxn(self : rosetta.protocols.loop_modeling.LoopModeler, scorefxn : rosetta.core.scoring.ScoreFunction) -> NoneType
Set the score function to be used for the fullatom stage.
- set_task_factory(...) from builtins.PyCapsule
- set_task_factory(self : rosetta.protocols.loop_modeling.LoopModeler, task_factory : rosetta.core.pack.task.TaskFactory) -> NoneType
Set the task factory to be used on the next call to apply().
- setup_empty_config(...) from builtins.PyCapsule
- setup_empty_config(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Setup the LoopModeler using a minimal configuration.
- setup_kic_config(...) from builtins.PyCapsule
- setup_kic_config(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Setup the LoopModeler using the "next-gen KIC" configuration.
- setup_kic_with_fragments_config(...) from builtins.PyCapsule
- setup_kic_with_fragments_config(rosetta.protocols.loop_modeling.LoopModeler) -> NoneType
Setup the LoopModeler using the "KIC with fragments"
configuration.
Methods inherited from LoopMover:
- apply(...) from builtins.PyCapsule
- apply(self : rosetta.protocols.loop_modeling.LoopMover, pose : rosetta.core.pose.Pose) -> NoneType
Sample the pose in the regions specified by get_loops().
The parent class apply() method automatically sets up a fold
tree (if necessary) and keeps track of whether or not the move succeeded.
Child classes should reimplement do_apply() instead of this method.
- get_children_names(...) from builtins.PyCapsule
- get_children_names(*args, **kwargs)
Overloaded function.
1. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
2. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string, indent : str) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
- get_loop(...) from builtins.PyCapsule
- get_loop(self : rosetta.protocols.loop_modeling.LoopMover, index : int) -> rosetta.protocols.loops.Loop
Return the specified loop.
- get_loops(...) from builtins.PyCapsule
- get_loops(*args, **kwargs)
Overloaded function.
1. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
2. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
- request_fold_tree(...) from builtins.PyCapsule
- request_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loop_modeling.FoldTreeRequest
Return an enum representing the kind of fold tree that is
compatible with this mover.
The FoldTreeRequest enum values can be combined using the
bitwise logical operators. For example, you can request either the
standard fold tree or a simple fold tree with `FTR_LOOPS_WITH_CUTS |
FTR_SIMPLE_TREE.`
- set_loop(...) from builtins.PyCapsule
- set_loop(self : rosetta.protocols.loop_modeling.LoopMover, loop : rosetta.protocols.loops.Loop) -> NoneType
Set the loop to be sampled on the next call to apply().
- set_loops(...) from builtins.PyCapsule
- set_loops(*args, **kwargs)
Overloaded function.
1. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
2. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
- setup_fold_tree(...) from builtins.PyCapsule
- setup_fold_tree(pose : rosetta.core.pose.Pose, loops : rosetta.protocols.loops.Loops, request : rosetta.protocols.loop_modeling.FoldTreeRequest) -> NoneType
Setup the given pose with a fold tree that is compatible with the
given loops and requests.
- trust_fold_tree(...) from builtins.PyCapsule
- trust_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> NoneType
Promise that the calling code will setup a fold tree compatible
with request_fold_tree(). If this method is not called, this mover will
setup a fold tree on its own every time apply() is called.
- was_successful(...) from builtins.PyCapsule
- was_successful(rosetta.protocols.loop_modeling.LoopMover) -> bool
Return true if the previous move was successful.
Methods inherited from rosetta.protocols.moves.Mover:
- clear_info(...) from builtins.PyCapsule
- clear_info(rosetta.protocols.moves.Mover) -> NoneType
Strings container can be used to return miscellaneous info (as std::string) from a mover, such as notes about the results of apply(). The job distributor (Apr 09 vintage) will check this function to see if your protocol wants to add string info to the Job that ran this mover. One way this can be useful is that later, a JobOutputter may include/append this info to an output file.
clear_info is called by jd2 before calling apply
- create(...) from builtins.PyCapsule
- create(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- fresh_instance(...) from builtins.PyCapsule
- fresh_instance(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Generates a new Mover object freshly created with the default ctor.
- get_additional_output(...) from builtins.PyCapsule
- get_additional_output(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
fpd
Mechanism by which a mover may return multiple output poses from a single input pose.
- get_current_job(...) from builtins.PyCapsule
- get_current_job(rosetta.protocols.moves.Mover) -> protocols::jobdist::BasicJob
- get_current_tag(...) from builtins.PyCapsule
- get_current_tag(rosetta.protocols.moves.Mover) -> str
A tag is a unique identifier used to identify structures produced
by this Mover. get_current_tag() returns the tag, and set_current_tag( std::string tag )
sets the tag. This functionality is not intended for use with the 2008 job distributor.
- get_input_pose(...) from builtins.PyCapsule
- get_input_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_last_move_status(...) from builtins.PyCapsule
- get_last_move_status(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.MoverStatus
returns status after an apply(). The job distributor (august 08 vintage) will check this function to see if your protocol wants to filter its results - if your protocol wants to say "that run was no good, skip it" then use the protected last_move_status(MoverStatus) to change the value that this function will return.
- get_native_pose(...) from builtins.PyCapsule
- get_native_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_self_ptr(...) from builtins.PyCapsule
- get_self_ptr(*args, **kwargs)
Overloaded function.
1. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
2. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- get_self_weak_ptr(...) from builtins.PyCapsule
- get_self_weak_ptr(*args, **kwargs)
Overloaded function.
1. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_const_protocols_moves_Mover_t
2. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_protocols_moves_Mover_t
- get_type(...) from builtins.PyCapsule
- get_type(rosetta.protocols.moves.Mover) -> str
- info(...) from builtins.PyCapsule
- info(*args, **kwargs)
Overloaded function.
1. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
non-const accessor
2. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
const accessor
- last_proposal_density_ratio(...) from builtins.PyCapsule
- last_proposal_density_ratio(rosetta.protocols.moves.Mover) -> float
- name(...) from builtins.PyCapsule
- name() -> str
- register_options(...) from builtins.PyCapsule
- register_options(*args, **kwargs)
Overloaded function.
1. register_options() -> NoneType
Overload this static method if you access options within the mover.
These options will end up in -help of your application if users of this mover call register_options.
Do this recursively!
If you use movers within your mover, call their register_options in your register_options() method.
2. register_options() -> NoneType
3. register_options() -> NoneType
4. register_options() -> NoneType
5. register_options() -> NoneType
6. register_options() -> NoneType
7. register_options() -> NoneType
8. register_options() -> NoneType
9. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
10. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
11. register_options() -> NoneType
Associates relevant options with the SnugDock class
12. register_options() -> NoneType
Associates relevant options with the SnugDockProtocol class
13. register_options() -> NoneType
Register the options used by this mover with the global options
system.
14. register_options() -> NoneType
15. register_options() -> NoneType
Associate relevant options with the TemperedDocking class.
16. register_options() -> NoneType
17. register_options() -> NoneType
18. register_options() -> NoneType
Associates relevant options with the TemperedDocking class.
19. register_options() -> NoneType
20. register_options() -> NoneType
Associates relevant options with the ConstraintSetMover class
21. register_options() -> NoneType
22. register_options() -> NoneType
Associates relevant options with the DockingInitialPerturbation class
23. register_options() -> NoneType
Associates relevant options with the DockingProtocol class
24. register_options() -> NoneType
Associates relevant options with the TemperedDocking class
25. register_options() -> NoneType
26. register_options() -> NoneType
27. register_options() -> NoneType
28. register_options() -> NoneType
register options
29. register_options() -> NoneType
30. register_options() -> NoneType
Registers applicable options
31. register_options() -> NoneType
Register options with the option system.
32. register_options() -> NoneType
33. register_options() -> NoneType
34. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
35. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycleContainer class
36. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
37. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
38. register_options() -> NoneType
39. register_options() -> NoneType
Register options with the option system.
40. register_options() -> NoneType
- reinitialize_for_each_job(...) from builtins.PyCapsule
- reinitialize_for_each_job(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be freshly regenerated on
each use.
- reinitialize_for_new_input(...) from builtins.PyCapsule
- reinitialize_for_new_input(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be regenerated when the input
pose is about to change, (for example, if the Mover has special code on the first apply() that is only valid for
that one input pose).
- reset_status(...) from builtins.PyCapsule
- reset_status(rosetta.protocols.moves.Mover) -> NoneType
resets status to SUCCESS, meant to be used before an apply(). The job distributor (august 08 vintage) uses this to ensure non-accumulation of status across apply()s.
- set_current_job(...) from builtins.PyCapsule
- set_current_job(self : rosetta.protocols.moves.Mover, job : protocols::jobdist::BasicJob) -> NoneType
////////////////////////////end Job Distributor interface////////////////////////////////////////
- set_current_tag(...) from builtins.PyCapsule
- set_current_tag(self : rosetta.protocols.moves.Mover, new_tag : str) -> NoneType
- set_input_pose(...) from builtins.PyCapsule
- set_input_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for poses contained for rms
- set_native_pose(...) from builtins.PyCapsule
- set_native_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for native poses contained for rms ---- we should get rid of this method? it is widely used, but a bit unsafe
- set_type(...) from builtins.PyCapsule
- set_type(self : rosetta.protocols.moves.Mover, setting : str) -> NoneType
- test_move(...) from builtins.PyCapsule
- test_move(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
: Unit test support function. Apply one move to a given pose.
Allows extra test specific functions to be called before applying
- type(...) from builtins.PyCapsule
- type(*args, **kwargs)
Overloaded function.
1. type(rosetta.protocols.moves.Mover) -> str
2. type(self : rosetta.protocols.moves.Mover, type_in : str) -> NoneType
|
class LoopMover(rosetta.protocols.moves.Mover) |
| |
Base class for loop-sampling algorithms.
Classes that inherit from LoopMover can plug into the LoopProtocol
framework. The only method that subclasses need to provide is do_apply().
Note that the apply() method itself cannot be overwritten, because
LoopMover uses it to do some useful setup and teardown work. Instead,
there are two versions of do_apply() that subclasses may implement. Both
return a boolean to indicate whether or not the move succeeded. The first
version of do_apply() accepts only a Pose and is expected to operate on all
of the loops returned by get_loops(). The second version accepts a Pose
and a Loop, and is only expected to operate on the given loop. By default,
the first version simply iterates through the loops provided by get_loops()
and calls the second version on each one. This means that if the first
version is overwritten, the second version will no longer be called. If
neither method is reimplemented, a runtime error will be thrown.
LoopMover provides a handful of features that could be useful to a loop
sampling algorithm. As mentioned above, the get_loops() method returns the
loops that should be sampled. There are also a number of methods provided
for controlling how the fold tree is setup up. The request_fold_tree()
method can be reimplemented to return an enum telling what kind of fold
tree this mover requires. When apply() is called for the first time, a
compatible fold tree will be configured. This behavior is disabled if
trust_fold_tree() is called beforehand, in which case responsibility for
constructing a compatible fold tree is passed to the calling code.
|
| |
- Method resolution order:
- LoopMover
- rosetta.protocols.moves.Mover
- builtins.object
Methods defined here:
- __init__(...) from builtins.PyCapsule
- __init__(*args, **kwargs)
Overloaded function.
1. __init__(handle) -> NoneType
2. __init__(handle, rosetta.protocols.loop_modeling.LoopMover) -> NoneType
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
- apply(...) from builtins.PyCapsule
- apply(self : rosetta.protocols.loop_modeling.LoopMover, pose : rosetta.core.pose.Pose) -> NoneType
Sample the pose in the regions specified by get_loops().
The parent class apply() method automatically sets up a fold
tree (if necessary) and keeps track of whether or not the move succeeded.
Child classes should reimplement do_apply() instead of this method.
- assign(...) from builtins.PyCapsule
- assign(self : rosetta.protocols.loop_modeling.LoopMover, : rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loop_modeling.LoopMover
- get_children_names(...) from builtins.PyCapsule
- get_children_names(*args, **kwargs)
Overloaded function.
1. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
2. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string, indent : str) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
- get_loop(...) from builtins.PyCapsule
- get_loop(self : rosetta.protocols.loop_modeling.LoopMover, index : int) -> rosetta.protocols.loops.Loop
Return the specified loop.
- get_loops(...) from builtins.PyCapsule
- get_loops(*args, **kwargs)
Overloaded function.
1. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
2. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
- get_name(...) from builtins.PyCapsule
- get_name(rosetta.protocols.loop_modeling.LoopMover) -> str
Return the name of this mover.
- request_fold_tree(...) from builtins.PyCapsule
- request_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loop_modeling.FoldTreeRequest
Return an enum representing the kind of fold tree that is
compatible with this mover.
The FoldTreeRequest enum values can be combined using the
bitwise logical operators. For example, you can request either the
standard fold tree or a simple fold tree with `FTR_LOOPS_WITH_CUTS |
FTR_SIMPLE_TREE.`
- set_loop(...) from builtins.PyCapsule
- set_loop(self : rosetta.protocols.loop_modeling.LoopMover, loop : rosetta.protocols.loops.Loop) -> NoneType
Set the loop to be sampled on the next call to apply().
- set_loops(...) from builtins.PyCapsule
- set_loops(*args, **kwargs)
Overloaded function.
1. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
2. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
- setup_fold_tree(...) from builtins.PyCapsule
- setup_fold_tree(pose : rosetta.core.pose.Pose, loops : rosetta.protocols.loops.Loops, request : rosetta.protocols.loop_modeling.FoldTreeRequest) -> NoneType
Setup the given pose with a fold tree that is compatible with the
given loops and requests.
- trust_fold_tree(...) from builtins.PyCapsule
- trust_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> NoneType
Promise that the calling code will setup a fold tree compatible
with request_fold_tree(). If this method is not called, this mover will
setup a fold tree on its own every time apply() is called.
- was_successful(...) from builtins.PyCapsule
- was_successful(rosetta.protocols.loop_modeling.LoopMover) -> bool
Return true if the previous move was successful.
Methods inherited from rosetta.protocols.moves.Mover:
- clear_info(...) from builtins.PyCapsule
- clear_info(rosetta.protocols.moves.Mover) -> NoneType
Strings container can be used to return miscellaneous info (as std::string) from a mover, such as notes about the results of apply(). The job distributor (Apr 09 vintage) will check this function to see if your protocol wants to add string info to the Job that ran this mover. One way this can be useful is that later, a JobOutputter may include/append this info to an output file.
clear_info is called by jd2 before calling apply
- clone(...) from builtins.PyCapsule
- clone(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Return a clone of the Mover object.
- create(...) from builtins.PyCapsule
- create(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- fresh_instance(...) from builtins.PyCapsule
- fresh_instance(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Generates a new Mover object freshly created with the default ctor.
- get_additional_output(...) from builtins.PyCapsule
- get_additional_output(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
fpd
Mechanism by which a mover may return multiple output poses from a single input pose.
- get_current_job(...) from builtins.PyCapsule
- get_current_job(rosetta.protocols.moves.Mover) -> protocols::jobdist::BasicJob
- get_current_tag(...) from builtins.PyCapsule
- get_current_tag(rosetta.protocols.moves.Mover) -> str
A tag is a unique identifier used to identify structures produced
by this Mover. get_current_tag() returns the tag, and set_current_tag( std::string tag )
sets the tag. This functionality is not intended for use with the 2008 job distributor.
- get_input_pose(...) from builtins.PyCapsule
- get_input_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_last_move_status(...) from builtins.PyCapsule
- get_last_move_status(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.MoverStatus
returns status after an apply(). The job distributor (august 08 vintage) will check this function to see if your protocol wants to filter its results - if your protocol wants to say "that run was no good, skip it" then use the protected last_move_status(MoverStatus) to change the value that this function will return.
- get_native_pose(...) from builtins.PyCapsule
- get_native_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_self_ptr(...) from builtins.PyCapsule
- get_self_ptr(*args, **kwargs)
Overloaded function.
1. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
2. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- get_self_weak_ptr(...) from builtins.PyCapsule
- get_self_weak_ptr(*args, **kwargs)
Overloaded function.
1. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_const_protocols_moves_Mover_t
2. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_protocols_moves_Mover_t
- get_type(...) from builtins.PyCapsule
- get_type(rosetta.protocols.moves.Mover) -> str
- info(...) from builtins.PyCapsule
- info(*args, **kwargs)
Overloaded function.
1. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
non-const accessor
2. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
const accessor
- last_proposal_density_ratio(...) from builtins.PyCapsule
- last_proposal_density_ratio(rosetta.protocols.moves.Mover) -> float
- name(...) from builtins.PyCapsule
- name() -> str
- register_options(...) from builtins.PyCapsule
- register_options(*args, **kwargs)
Overloaded function.
1. register_options() -> NoneType
Overload this static method if you access options within the mover.
These options will end up in -help of your application if users of this mover call register_options.
Do this recursively!
If you use movers within your mover, call their register_options in your register_options() method.
2. register_options() -> NoneType
3. register_options() -> NoneType
4. register_options() -> NoneType
5. register_options() -> NoneType
6. register_options() -> NoneType
7. register_options() -> NoneType
8. register_options() -> NoneType
9. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
10. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
11. register_options() -> NoneType
Associates relevant options with the SnugDock class
12. register_options() -> NoneType
Associates relevant options with the SnugDockProtocol class
13. register_options() -> NoneType
Register the options used by this mover with the global options
system.
14. register_options() -> NoneType
15. register_options() -> NoneType
Associate relevant options with the TemperedDocking class.
16. register_options() -> NoneType
17. register_options() -> NoneType
18. register_options() -> NoneType
Associates relevant options with the TemperedDocking class.
19. register_options() -> NoneType
20. register_options() -> NoneType
Associates relevant options with the ConstraintSetMover class
21. register_options() -> NoneType
22. register_options() -> NoneType
Associates relevant options with the DockingInitialPerturbation class
23. register_options() -> NoneType
Associates relevant options with the DockingProtocol class
24. register_options() -> NoneType
Associates relevant options with the TemperedDocking class
25. register_options() -> NoneType
26. register_options() -> NoneType
27. register_options() -> NoneType
28. register_options() -> NoneType
register options
29. register_options() -> NoneType
30. register_options() -> NoneType
Registers applicable options
31. register_options() -> NoneType
Register options with the option system.
32. register_options() -> NoneType
33. register_options() -> NoneType
34. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
35. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycleContainer class
36. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
37. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
38. register_options() -> NoneType
39. register_options() -> NoneType
Register options with the option system.
40. register_options() -> NoneType
- reinitialize_for_each_job(...) from builtins.PyCapsule
- reinitialize_for_each_job(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be freshly regenerated on
each use.
- reinitialize_for_new_input(...) from builtins.PyCapsule
- reinitialize_for_new_input(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be regenerated when the input
pose is about to change, (for example, if the Mover has special code on the first apply() that is only valid for
that one input pose).
- reset_status(...) from builtins.PyCapsule
- reset_status(rosetta.protocols.moves.Mover) -> NoneType
resets status to SUCCESS, meant to be used before an apply(). The job distributor (august 08 vintage) uses this to ensure non-accumulation of status across apply()s.
- set_current_job(...) from builtins.PyCapsule
- set_current_job(self : rosetta.protocols.moves.Mover, job : protocols::jobdist::BasicJob) -> NoneType
////////////////////////////end Job Distributor interface////////////////////////////////////////
- set_current_tag(...) from builtins.PyCapsule
- set_current_tag(self : rosetta.protocols.moves.Mover, new_tag : str) -> NoneType
- set_input_pose(...) from builtins.PyCapsule
- set_input_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for poses contained for rms
- set_native_pose(...) from builtins.PyCapsule
- set_native_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for native poses contained for rms ---- we should get rid of this method? it is widely used, but a bit unsafe
- set_type(...) from builtins.PyCapsule
- set_type(self : rosetta.protocols.moves.Mover, setting : str) -> NoneType
- test_move(...) from builtins.PyCapsule
- test_move(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
: Unit test support function. Apply one move to a given pose.
Allows extra test specific functions to be called before applying
- type(...) from builtins.PyCapsule
- type(*args, **kwargs)
Overloaded function.
1. type(rosetta.protocols.moves.Mover) -> str
2. type(self : rosetta.protocols.moves.Mover, type_in : str) -> NoneType
|
class LoopProtocol(LoopMover) |
| |
Monte Carlo search for low energy loop conformations.
This class provides an easy way to run a Monte Carlo simulation
searching for the lowest energy conformations for a set of loops. This
simulation is organized into three nested loops. On each iteration of the
outermost loop, the lowest scoring pose is recovered and the repulsive
terms in the score function may be ramped (although this ramping is
disabled by default). On each iteration of the intermediate loop, the
temperature may be ramped (this ramping is enabled by default). And on
each iteration of the innermost loop, a new conformation is sampled and
either accepted or rejected according to the Metropolis criterion. The
intermediate loop usually goes through more than 100 iterations, while the
innermost and outermost loops only go through less than 5.
Like any mover, all the work is done by the apply() method. The rest of
the methods of this class are just getters and setters that can be used to
control various aspects of the simulation. The add_mover(), add_filter(),
and add_acceptance_check() methods are worth drawing some attention to.
These methods are used to build up the group of LoopMover objects that
samples new loop conformations in the innermost loop. The movers are
guaranteed to be applied in the order they are added to the protocol.
|
| |
- Method resolution order:
- LoopProtocol
- LoopMover
- rosetta.protocols.moves.Mover
- builtins.object
Methods defined here:
- __init__(...) from builtins.PyCapsule
- __init__(*args, **kwargs)
Overloaded function.
1. __init__(handle) -> NoneType
2. __init__(handle, rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
- __new__(*args, **kwargs) from builtins.type
- Create and return a new object. See help(type) for accurate signature.
- add_acceptance_check(...) from builtins.PyCapsule
- add_acceptance_check(*args, **kwargs)
Overloaded function.
1. add_acceptance_check(rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
Add an acceptance check to the simulation.
An acceptance check is always performed after all the loop
movers have been applied, unless one of the movers failed. This method
allows additional acceptance checks to be included in the protocol by
adding a loop mover that does nothing but make that check.
2. add_acceptance_check(self : rosetta.protocols.loop_modeling.LoopProtocol, name : str) -> NoneType
Add an acceptance check to the simulation.
An acceptance check is always performed after all the loop
movers have been applied, unless one of the movers failed. This method
allows additional acceptance checks to be included in the protocol by
adding a loop mover that does nothing but make that check.
- add_filter(...) from builtins.PyCapsule
- add_filter(self : rosetta.protocols.loop_modeling.LoopProtocol, filter : rosetta.protocols.filters.Filter) -> NoneType
Add a Filter to the simulation.
- add_mover(...) from builtins.PyCapsule
- add_mover(self : rosetta.protocols.loop_modeling.LoopProtocol, mover : rosetta.protocols.loop_modeling.LoopMover) -> NoneType
Add a LoopMover to the simulation.
Loop movers will be applied in the order they're added.
add_refiner()
mark_as_default()
- add_refiner(...) from builtins.PyCapsule
- add_refiner(self : rosetta.protocols.loop_modeling.LoopProtocol, refiner : rosetta.protocols.loop_modeling.LoopMover) -> NoneType
Add a LoopMover to the simulation as a refiner.
This method is very similar to add_mover(). Both methods add a
new LoopMover to the simulation. This difference is subtle, but relevant
when a LoopProtocol is being filled with a default set of movers that
might be modified later.
This situation arises when a LoopModeler object is being constructed, or
when a <LoopModeler> tag is being parsed by rosetta scripts. In both of
these cases, centroid and fullatom LoopProtocols are created right away
and filled with default movers. This avoids the necessity of specifying
all the usual movers every time loop modeling is invoked. But the
default movers may be subsequently overridden. In this case, there is a
difference between LoopMovers added via add_mover() (i.e. movers) and
add_refiner() (i.e. refiners).
The difference is that movers can be marked as "default" and then be
implicitly overridden. Consider the following sequence of calls:
protocol->add_mover(new CcdMover);
protocol->mark_as_default();
protocol->add_mover(new KicMover);
This will result in a LoopProtocol containing only a KicMover. When
mark_as_default() is called, all movers within the protocol are set to be
replaced the next time add_mover() is called. You can think of those
movers as being part of a default configuration that should be replaced
when a new configuration is given.
Refiners are not affected by mark_as_default(). Calling add_refiner()
will always add a refiner and will never remove old ones, although old
refiners can be explicitly removed with clear_refiners(). Refiners are
also applied after movers, regardless of the order in which add_mover()
and add_refiner() were called. The purpose of all this is to make it
easy to replace the parts of a LoopProtocol that are more variable
without having to also replace those that are more static.
The variable part of a LoopProtocol is typically the sampling algorithm:
KIC or CCD, often with myriad options. The static part is typically the
refinement algorithm: minimization, rotamer trials, and repacking, all
with carefully tuned parameters. Using add_mover(), mark_as_default(),
and add_refiner() makes it easy to change the sampling algorithm without
having to worry about the refinement algorithm.
add_mover()
mark_as_default()
- assign(...) from builtins.PyCapsule
- assign(self : rosetta.protocols.loop_modeling.LoopProtocol, : rosetta.protocols.loop_modeling.LoopProtocol) -> rosetta.protocols.loop_modeling.LoopProtocol
- clear_movers(...) from builtins.PyCapsule
- clear_movers(rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
Remove all movers from the protocol.
add_mover()
add_refiner()
clear_refiners()
clear_movers_and_refiners()
- clear_movers_and_refiners(...) from builtins.PyCapsule
- clear_movers_and_refiners(rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
Remove all movers and refiners from the protocol.
add_mover()
add_refiner()
clear_movers()
clear_refiners()
- clear_refiners(...) from builtins.PyCapsule
- clear_refiners(rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
Remove all refiners from the protocol.
add_mover()
add_refiner()
clear_movers()
clear_movers_and_refiners()
- get_mover_cycles(...) from builtins.PyCapsule
- get_mover_cycles(rosetta.protocols.loop_modeling.LoopProtocol) -> int
Return how many times the loops movers will be invoked after the
score function and temperature have been updated.
- get_name(...) from builtins.PyCapsule
- get_name(rosetta.protocols.loop_modeling.LoopProtocol) -> str
LoopMover::get_name
- get_score_function(...) from builtins.PyCapsule
- get_score_function(rosetta.protocols.loop_modeling.LoopProtocol) -> rosetta.core.scoring.ScoreFunction
Get the score function to be used on the next call to apply().
- get_sfxn_cycles(...) from builtins.PyCapsule
- get_sfxn_cycles(rosetta.protocols.loop_modeling.LoopProtocol) -> int
Get the number of iterations the loop that ramps the score
function will make.
- get_temp_cycles(...) from builtins.PyCapsule
- get_temp_cycles(rosetta.protocols.loop_modeling.LoopProtocol) -> int
Get the number of iterations the loop that ramps the temperature
should make.
This will throw an exception if the 'times_loop_length' argument
was passed to set_temp_cycles() and no loop has been specified yet. The
value returned will always be the actual number of iterations that will
be carried out.
- mark_as_default(...) from builtins.PyCapsule
- mark_as_default(rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
Indicate that every mover that is currently part of the protocol
should be removed the next time a mover is added.
add_mover()
add_refiner()
- mark_as_test_run(...) from builtins.PyCapsule
- mark_as_test_run(rosetta.protocols.loop_modeling.LoopProtocol) -> NoneType
Indicate that this protocol is being used for a test run.
This method simply limits the number of iterations that will be
performed. It can be "undone" by explicitly setting the score function,
temperature, and mover cycles back to normal values.
- set_mover_cycles(...) from builtins.PyCapsule
- set_mover_cycles(self : rosetta.protocols.loop_modeling.LoopProtocol, x : int) -> NoneType
Specify how many times the loops movers should be invoked after
the score function and temperature have been updated.
The mover loop is the innermost loop. It is contained by the
score function loop and the temperature loop.
- set_rama_term_ramping(...) from builtins.PyCapsule
- set_rama_term_ramping(self : rosetta.protocols.loop_modeling.LoopProtocol, value : bool) -> NoneType
Enable or disable ramping the repulsive term of the score
function.
- set_repulsive_term_ramping(...) from builtins.PyCapsule
- set_repulsive_term_ramping(self : rosetta.protocols.loop_modeling.LoopProtocol, value : bool) -> NoneType
Enable or disable ramping the repulsive term of the score
function.
- set_score_function(...) from builtins.PyCapsule
- set_score_function(self : rosetta.protocols.loop_modeling.LoopProtocol, score_function : rosetta.core.scoring.ScoreFunction) -> NoneType
Set the score function to be used on the next call to apply().
- set_sfxn_cycles(...) from builtins.PyCapsule
- set_sfxn_cycles(self : rosetta.protocols.loop_modeling.LoopProtocol, x : int) -> NoneType
Set the number of iterations the loop that ramps the score
function should make.
The score function loop is the outermost loop. It contains the
temperature loop and the mover loop.
- set_temp_cycles(...) from builtins.PyCapsule
- set_temp_cycles(*args, **kwargs)
Overloaded function.
1. set_temp_cycles(self : rosetta.protocols.loop_modeling.LoopProtocol, x : int) -> NoneType
Set the number of iterations the loop that ramps the temperature
should make.
The temperature loop is contained by the score function loop.
It contains the mover loop. If the second boolean argument is true, the
number of iterations will be the first value times the combined length of
all the loops being sampled.
2. set_temp_cycles(self : rosetta.protocols.loop_modeling.LoopProtocol, x : int, times_loop_length : bool) -> NoneType
Set the number of iterations the loop that ramps the temperature
should make.
The temperature loop is contained by the score function loop.
It contains the mover loop. If the second boolean argument is true, the
number of iterations will be the first value times the combined length of
all the loops being sampled.
- set_temperature_ramping(...) from builtins.PyCapsule
- set_temperature_ramping(self : rosetta.protocols.loop_modeling.LoopProtocol, value : bool) -> NoneType
Enable or disable temperature ramping in this simulation.
- set_temperature_schedule(...) from builtins.PyCapsule
- set_temperature_schedule(self : rosetta.protocols.loop_modeling.LoopProtocol, start : float, stop : float) -> NoneType
Set the initial and final temperatures for the simulation.
The temperature will be linearly interpolated between these
values during the simulation.
Methods inherited from LoopMover:
- apply(...) from builtins.PyCapsule
- apply(self : rosetta.protocols.loop_modeling.LoopMover, pose : rosetta.core.pose.Pose) -> NoneType
Sample the pose in the regions specified by get_loops().
The parent class apply() method automatically sets up a fold
tree (if necessary) and keeps track of whether or not the move succeeded.
Child classes should reimplement do_apply() instead of this method.
- get_children_names(...) from builtins.PyCapsule
- get_children_names(*args, **kwargs)
Overloaded function.
1. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
2. get_children_names(self : rosetta.protocols.loop_modeling.LoopMover, names : rosetta.utility.vector1_std_string, indent : str) -> NoneType
Add the names of all the algorithms invoked by this loop mover to
the given list. Indentation is used to represent hierarchy.
- get_loop(...) from builtins.PyCapsule
- get_loop(self : rosetta.protocols.loop_modeling.LoopMover, index : int) -> rosetta.protocols.loops.Loop
Return the specified loop.
- get_loops(...) from builtins.PyCapsule
- get_loops(*args, **kwargs)
Overloaded function.
1. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
2. get_loops(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loops.Loops
Return the loops to be sampled on the next call to apply().
- request_fold_tree(...) from builtins.PyCapsule
- request_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> rosetta.protocols.loop_modeling.FoldTreeRequest
Return an enum representing the kind of fold tree that is
compatible with this mover.
The FoldTreeRequest enum values can be combined using the
bitwise logical operators. For example, you can request either the
standard fold tree or a simple fold tree with `FTR_LOOPS_WITH_CUTS |
FTR_SIMPLE_TREE.`
- set_loop(...) from builtins.PyCapsule
- set_loop(self : rosetta.protocols.loop_modeling.LoopMover, loop : rosetta.protocols.loops.Loop) -> NoneType
Set the loop to be sampled on the next call to apply().
- set_loops(...) from builtins.PyCapsule
- set_loops(*args, **kwargs)
Overloaded function.
1. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
2. set_loops(self : rosetta.protocols.loop_modeling.LoopMover, loops : rosetta.protocols.loops.Loops) -> NoneType
Set the loops to be sampled on the next call to apply().
- setup_fold_tree(...) from builtins.PyCapsule
- setup_fold_tree(pose : rosetta.core.pose.Pose, loops : rosetta.protocols.loops.Loops, request : rosetta.protocols.loop_modeling.FoldTreeRequest) -> NoneType
Setup the given pose with a fold tree that is compatible with the
given loops and requests.
- trust_fold_tree(...) from builtins.PyCapsule
- trust_fold_tree(rosetta.protocols.loop_modeling.LoopMover) -> NoneType
Promise that the calling code will setup a fold tree compatible
with request_fold_tree(). If this method is not called, this mover will
setup a fold tree on its own every time apply() is called.
- was_successful(...) from builtins.PyCapsule
- was_successful(rosetta.protocols.loop_modeling.LoopMover) -> bool
Return true if the previous move was successful.
Methods inherited from rosetta.protocols.moves.Mover:
- clear_info(...) from builtins.PyCapsule
- clear_info(rosetta.protocols.moves.Mover) -> NoneType
Strings container can be used to return miscellaneous info (as std::string) from a mover, such as notes about the results of apply(). The job distributor (Apr 09 vintage) will check this function to see if your protocol wants to add string info to the Job that ran this mover. One way this can be useful is that later, a JobOutputter may include/append this info to an output file.
clear_info is called by jd2 before calling apply
- clone(...) from builtins.PyCapsule
- clone(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Return a clone of the Mover object.
- create(...) from builtins.PyCapsule
- create(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- fresh_instance(...) from builtins.PyCapsule
- fresh_instance(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
Generates a new Mover object freshly created with the default ctor.
- get_additional_output(...) from builtins.PyCapsule
- get_additional_output(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
fpd
Mechanism by which a mover may return multiple output poses from a single input pose.
- get_current_job(...) from builtins.PyCapsule
- get_current_job(rosetta.protocols.moves.Mover) -> protocols::jobdist::BasicJob
- get_current_tag(...) from builtins.PyCapsule
- get_current_tag(rosetta.protocols.moves.Mover) -> str
A tag is a unique identifier used to identify structures produced
by this Mover. get_current_tag() returns the tag, and set_current_tag( std::string tag )
sets the tag. This functionality is not intended for use with the 2008 job distributor.
- get_input_pose(...) from builtins.PyCapsule
- get_input_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_last_move_status(...) from builtins.PyCapsule
- get_last_move_status(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.MoverStatus
returns status after an apply(). The job distributor (august 08 vintage) will check this function to see if your protocol wants to filter its results - if your protocol wants to say "that run was no good, skip it" then use the protected last_move_status(MoverStatus) to change the value that this function will return.
- get_native_pose(...) from builtins.PyCapsule
- get_native_pose(rosetta.protocols.moves.Mover) -> rosetta.core.pose.Pose
- get_self_ptr(...) from builtins.PyCapsule
- get_self_ptr(*args, **kwargs)
Overloaded function.
1. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
2. get_self_ptr(rosetta.protocols.moves.Mover) -> rosetta.protocols.moves.Mover
- get_self_weak_ptr(...) from builtins.PyCapsule
- get_self_weak_ptr(*args, **kwargs)
Overloaded function.
1. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_const_protocols_moves_Mover_t
2. get_self_weak_ptr(rosetta.protocols.moves.Mover) -> rosetta.std.weak_ptr_protocols_moves_Mover_t
- get_type(...) from builtins.PyCapsule
- get_type(rosetta.protocols.moves.Mover) -> str
- info(...) from builtins.PyCapsule
- info(*args, **kwargs)
Overloaded function.
1. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
non-const accessor
2. info(rosetta.protocols.moves.Mover) -> rosetta.std.list_std_string_std_allocator_std_string_t
const accessor
- last_proposal_density_ratio(...) from builtins.PyCapsule
- last_proposal_density_ratio(rosetta.protocols.moves.Mover) -> float
- name(...) from builtins.PyCapsule
- name() -> str
- register_options(...) from builtins.PyCapsule
- register_options(*args, **kwargs)
Overloaded function.
1. register_options() -> NoneType
Overload this static method if you access options within the mover.
These options will end up in -help of your application if users of this mover call register_options.
Do this recursively!
If you use movers within your mover, call their register_options in your register_options() method.
2. register_options() -> NoneType
3. register_options() -> NoneType
4. register_options() -> NoneType
5. register_options() -> NoneType
6. register_options() -> NoneType
7. register_options() -> NoneType
8. register_options() -> NoneType
9. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
10. register_options() -> NoneType
Associates relevant options with the AntibodyModeler class
11. register_options() -> NoneType
Associates relevant options with the SnugDock class
12. register_options() -> NoneType
Associates relevant options with the SnugDockProtocol class
13. register_options() -> NoneType
Register the options used by this mover with the global options
system.
14. register_options() -> NoneType
15. register_options() -> NoneType
Associate relevant options with the TemperedDocking class.
16. register_options() -> NoneType
17. register_options() -> NoneType
18. register_options() -> NoneType
Associates relevant options with the TemperedDocking class.
19. register_options() -> NoneType
20. register_options() -> NoneType
Associates relevant options with the ConstraintSetMover class
21. register_options() -> NoneType
22. register_options() -> NoneType
Associates relevant options with the DockingInitialPerturbation class
23. register_options() -> NoneType
Associates relevant options with the DockingProtocol class
24. register_options() -> NoneType
Associates relevant options with the TemperedDocking class
25. register_options() -> NoneType
26. register_options() -> NoneType
27. register_options() -> NoneType
28. register_options() -> NoneType
register options
29. register_options() -> NoneType
30. register_options() -> NoneType
Registers applicable options
31. register_options() -> NoneType
Register options with the option system.
32. register_options() -> NoneType
33. register_options() -> NoneType
34. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
35. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycleContainer class
36. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
37. register_options() -> NoneType
Associates relevant options with the LoopRefineInnerCycle class
38. register_options() -> NoneType
39. register_options() -> NoneType
Register options with the option system.
40. register_options() -> NoneType
- reinitialize_for_each_job(...) from builtins.PyCapsule
- reinitialize_for_each_job(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be freshly regenerated on
each use.
- reinitialize_for_new_input(...) from builtins.PyCapsule
- reinitialize_for_new_input(rosetta.protocols.moves.Mover) -> bool
Inform the Job Distributor (August '08 vintage) whether this object needs to be regenerated when the input
pose is about to change, (for example, if the Mover has special code on the first apply() that is only valid for
that one input pose).
- reset_status(...) from builtins.PyCapsule
- reset_status(rosetta.protocols.moves.Mover) -> NoneType
resets status to SUCCESS, meant to be used before an apply(). The job distributor (august 08 vintage) uses this to ensure non-accumulation of status across apply()s.
- set_current_job(...) from builtins.PyCapsule
- set_current_job(self : rosetta.protocols.moves.Mover, job : protocols::jobdist::BasicJob) -> NoneType
////////////////////////////end Job Distributor interface////////////////////////////////////////
- set_current_tag(...) from builtins.PyCapsule
- set_current_tag(self : rosetta.protocols.moves.Mover, new_tag : str) -> NoneType
- set_input_pose(...) from builtins.PyCapsule
- set_input_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for poses contained for rms
- set_native_pose(...) from builtins.PyCapsule
- set_native_pose(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
setter for native poses contained for rms ---- we should get rid of this method? it is widely used, but a bit unsafe
- set_type(...) from builtins.PyCapsule
- set_type(self : rosetta.protocols.moves.Mover, setting : str) -> NoneType
- test_move(...) from builtins.PyCapsule
- test_move(self : rosetta.protocols.moves.Mover, pose : rosetta.core.pose.Pose) -> NoneType
: Unit test support function. Apply one move to a given pose.
Allows extra test specific functions to be called before applying
- type(...) from builtins.PyCapsule
- type(*args, **kwargs)
Overloaded function.
1. type(rosetta.protocols.moves.Mover) -> str
2. type(self : rosetta.protocols.moves.Mover, type_in : str) -> NoneType
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