MIFSTProbabilitiesMetric

Autogenerated Tag Syntax Documentation:

A PerResidueProbabilitiesMetric that stores amino acid probabilities predicted by the MIF-ST model.

References and author information for the MIFSTProbabilitiesMetric simple metric:

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

<MIFSTProbabilitiesMetric name="(&string;)" custom_type="(&string;)"
        residue_selector="(&string;)" feature_selector="(&string;)"
        multirun="(true &bool;)" use_gpu="(false &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.
residue_selector: A residue selector specifying which residue or residues to predict for. The name of a previously declared residue selector or a logical expression of AND, NOT (!), OR, parentheses, and the names of previously declared residue selectors. Any capitalization of AND, NOT, and OR is accepted. An exclamation mark can be used instead of NOT. Boolean operators have their traditional priorities: NOT then AND then OR. For example, if selectors s1, s2, and s3 have been declared, you could write: 's1 or s2 and not s3' which would select a particular residue if that residue were selected by s1 or if it were selected by s2 but not by s3.
feature_selector: A residue selector specifying which parts of the posed are used as features in prediction. The name of a previously declared residue selector or a logical expression of AND, NOT (!), OR, parentheses, and the names of previously declared residue selectors. Any capitalization of AND, NOT, and OR is accepted. An exclamation mark can be used instead of NOT. Boolean operators have their traditional priorities: NOT then AND then OR. For example, if selectors s1, s2, and s3 have been declared, you could write: 's1 or s2 and not s3' which would select a particular residue if that residue were selected by s1 or if it were selected by s2 but not by s3.
multirun: Whether to run multirun the network (one inference pass for all selected residues
use_gpu: Whether to run the network on the GPU (if one is available)

General description

A metric for estimating the probability of an amino acid at a given position, as predicted by the Masked Inverse Folding with Sequence Transfer (MIF-ST) model from Yang et al.. This metric requires to be build with extras=torch, see Building Rosetta with TensorFlow and Torch for the compilation setup.

Example

The example predicts the amino acid probabilities for chain A using only the coordinates and sequence of chain A.It does so by running one prediction for each position while masking its residue type. With multirun=true & use_gpu=true all predictions are batched together and run on the GPU (if available). Lastly it uses these predictions to score the current sequence using the pseudo-perplexity metric.

<ROSETTASCRIPTS>
    <RESIDUE_SELECTORS>
        <Chain name="res" chains="A" />
    </RESIDUE_SELECTORS>
    <SIMPLE_METRICS>
        <MIFSTProbabilitiesMetric name="prediction" residue_selector="res" feature_selector="res" multirun="true" use_gpu="true"/>
        <PseudoPerplexityMetric name="perplex" metric="prediction"/>
    </SIMPLE_METRICS>
    <FILTERS>
    </FILTERS>
    <MOVERS>
        <RunSimpleMetrics name="run" metrics="perplex"/>
    </MOVERS>
    <PROTOCOLS>
        <Add mover_name="run"/>
    </PROTOCOLS>
</ROSETTASCRIPTS>

Reference

@article {Yang2022.05.25.493516,
	author = {Kevin K. Yang and Hugh Yeh and Niccol{\`o} Zanichelli},
	title = {Masked Inverse Folding with Sequence Transfer for Protein Representation Learning},
	elocation-id = {2022.05.25.493516},
	year = {2023},
	doi = {10.1101/2022.05.25.493516},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2023/03/19/2022.05.25.493516},
	eprint = {https://www.biorxiv.org/content/early/2023/03/19/2022.05.25.493516.full.pdf},
	journal = {bioRxiv}
}

MIFSTProbabilitiesMetric