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# ColabMPNN | ||
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[![Build Status](https://github.com/anton083/ColabMPNN.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/anton083/ColabMPNN.jl/actions/workflows/CI.yml?query=branch%3Amain) | ||
[![Build Status](https://github.com/MurrellGroup/ColabMPNN.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/MurrellGroup/ColabMPNN.jl/actions/workflows/CI.yml?query=branch%3Amain) | ||
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A Julia wrapper for ColabDesign's MPNN module using PyCall. | ||
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For more details, see the [original Python documentation](https://github.com/sokrypton/ColabDesign/blob/main/mpnn/README.md) | ||
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## Installation | ||
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Add ColabMPNN to your Julia environment in the REPL: | ||
``` | ||
]add https://github.com/MurrellGroup/ColabMPNN.jl | ||
``` | ||
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## Usage | ||
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Create a model using the `mk_mpnn_model` function. See arguments in the [Python code](https://github.com/sokrypton/ColabDesign/blob/main/colabdesign/mpnn/model.py#L24). | ||
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```julia | ||
mpnn_model = mk_mpnn_model() | ||
``` | ||
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Inputs are prepared to a model using, with the model as first argument. See | ||
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```julia | ||
prep_inputs(mpnn_model, pdb_filename="example.pdb", chain="A") | ||
``` | ||
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Sample sequences using the `sample` function, or in parallel with `sample_parallel`, with the model as the first argument. These functions return a `Samples` instance. | ||
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```julia | ||
samples = sample_parallel(mpnn_model) | ||
``` | ||
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The `Samples` type has the following fields: | ||
- `seq::Vector{String}` | ||
- `seqid::Vector{Float64}` | ||
- `score::Vector{Float64}` | ||
- `logits::Array{Float32, 3}` | ||
- `decoding_order::Array{Int32, 3}` | ||
- `S::Array{Float32, 3}` |
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