CurrentProbabilityMetric

Autogenerated Tag Syntax Documentation:

A PerResidueRealMetric for returning just the probability of the amino acid currently present in the pose from a PerResidueProbabilitiesMetric.

References and author information for the CurrentProbabilityMetric simple metric:

CurrentProbabilityMetric SimpleMetric's author(s): Moritz Ertelt, University of Leipzig [moritz.ertelt@gmail.com]

<CurrentProbabilityMetric name="(&string;)" custom_type="(&string;)"
        metric="(&string;)" use_cached_data="(false &bool;)"
        cache_prefix="(&string;)" cache_suffix="(&string;)"
        fail_on_missing_cache="(true &bool;)" />

custom_type: Allows multiple configured SimpleMetrics of a single type to be called in a single RunSimpleMetrics and SimpleMetricFeatures. The custom_type name will be added to the data tag in the scorefile or features database.
metric: (REQUIRED) A PerResidueProbabilitiesMetric to lookup the probability of the current amino acid in the pose from.
use_cached_data: Use any data stored in the datacache that matches the set metrics name (and any prefix/suffix.) Data is stored during a SimpleMetric's apply function, which is called during RunSimpleMetrics
cache_prefix: Any prefix used during apply (RunSimpleMetrics), that we will match on if use_cache is true
cache_suffix: Any suffix used during apply (RunSimpleMetrics), that we will match on if use_cache is true
fail_on_missing_cache: If use_cached_data is True and cache is not found, should we fail?

General description

A PerResidueRealMetric that returns just the probability for the sequence currently present in the pose from a PerResidueProbabilitiesMetric (going from length*20 to length*1 probabilities). Useful for filtering or visualization of probabilities. This metric alone does not require compilation with extras=tensorflow,torch but the model predictions that are typically input do. See Building Rosetta with TensorFlow and Torch for the compilation setup.

Example

This example predicts the amino acid probabilities for chain A of our protein using ProteinMPNN and then returns just the probabilities for the current sequence from them. Additionally it uses the metric_to_bfactor option of RunSimpleMetrics mover which enables easy visualization of the values in ChimeraX/PyMol.

<ROSETTASCRIPTS>
    <RESIDUE_SELECTORS>
        <Chain name="res" chains="A" />
    </RESIDUE_SELECTORS>
    <SIMPLE_METRICS>
        <ProteinMPNNProbabilitiesMetric name="prediction"/>
        <CurrentProbabilityMetric name="current" metric="prediction" custom_type="probs"/>
    </SIMPLE_METRICS>
    <FILTERS>
    </FILTERS>
    <MOVERS>
        <RunSimpleMetrics name="analysis" metrics="current" metric_to_bfactor="probs"/>
    </MOVERS>
    <PROTOCOLS>
        <Add mover_name="analysis"/>
    </PROTOCOLS>
</ROSETTASCRIPTS>

Reference

The implementation in Rosetta is currently unpublished.

CurrentProbabilityMetric