AddResidueCouplingConstraint

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AddResidueCouplingConstraint

<AddResidueCouplingConstraint name="(&string)" tensorfile ="(&string)" indexfile="(&string)" strength="(1 &Real)" alphabet="(ARNDCQEGHILKMFPSTWYV- &string)"/>

tensorfile: Location of the tensor storing co-evo. data.
indexfile: Location of the index file defining the tensor format.
strength: Defines the strength of co-evo. constraints (default=1).
alphabet: Defines the alphabet used in creating the co-evo. tensor (default=Gremlin order).

Description

This mover is intended for leveraging co-evolutionary information for protein design to favor sequences which recapitulate correlated mutations as observed in nature. The co-evo. information is derived from a multiple sequence alignment of homologous sequences which is analyzed by a mutual information/direct-coupling method, e.g. Gremlin.

In short, the mover takes a correlation matrix as input and then adds it as new term to the scoring function.

The matrices get input as tensor of the shape [N, A, N, A] where N is the length of the protein and A is the length of the alphabet (typically 21 amino acids + 1 for gaps). The order of the alphabet used can be defined by the user. To access the measure of co-evolution between, e.g., residue 1 (ALA) and residue 2 (ARG) the tensor lookup would be "[1, 0, 2, 1]" (using the GREMLIN alphabet order). The co-evolution strength is multiplied by a user defined strength factor (default=1) and then added as new term to the scoring function.

For more information see:

Rosetta Design with Co-Evolutionary Information Retains Protein Function
S. Schmitz, M. Ertelt, R. Merkl, J. Meiler (Plos comp. biol. 2021)

Tensor generation

The mover was designed to take co-evo. tensors from Gremlin as input, however, if the tensor is formatted into the right Rosetta tensor format the choice of method should not matter.

A python script to generate a tensor from a multiple sequence alignment (MSA) with Gremlin and then format it into something Rosetta can read, can be found in tools/GREMLIN/Gremlin2RosettaTensor.py. The script takes an MSA and the wanted name of the output files as input and will output tensor.bin, indexFile.index and tensor.json.

Caveats

The Rosetta tensor format expects the tensor.json file to be in the same directory as the tensor.bin file.

Usage example

Example xml for protein design:

<ROSETTASCRIPTS>
    <SCOREFXNS>
        <ScoreFunction name="scorefxn_cst" weights="ref2015.wts">
                <Reweight scoretype="res_type_linking_constraint" weight="1.0"/>
        </ScoreFunction>
        <ScoreFunction name="scorefxn" weights="ref2015.wts"/>
    </SCOREFXNS>
    <TASKOPERATIONS>
        <InitializeFromCommandline name="ifcl"/>
            <ReadResfile name="rrf" filename="./design.resfile"/>
    </TASKOPERATIONS>
    <MOVERS>
        <AddResidueCouplingConstraint name="favor" tensor_file="./tensor.bin" index_file="./indexList" strength="1.0" alphabet="ARNDCQEGHILKMFPSTWYV-"/>
        <PackRotamersMover name="design" scorefxn="scorefxn_cst" task_operations="ifcl,rrf" />
    </MOVERS>
    <FILTERS>
    </FILTERS>
    <APPLY_TO_POSE>
    </APPLY_TO_POSE>
    <PROTOCOLS>
        <Add mover="favor" />
        <Add mover="design" />
    </PROTOCOLS>
    <OUTPUT scorefxn="scorefxn" />
</ROSETTASCRIPTS>

AddResidueCouplingConstraint