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NA-MPNN

Introduction

This repository holds the training and inference code for NA-MPNN. See below for installation and examples.

RNA sequence design and protein–DNA specificity prediction with NA-MPNN https://www.biorxiv.org/content/10.1101/2025.10.03.679414v2

Installation

Installation of NA-MPNN should take between 10-30 minutes depending on your system and internet connection. The bulk of the time is spent setting up the conda environment. The inference code has been tested on Linux and Windows operating systems.

The inference code has been tested on a CPU with the following python/module versions:

  • Python v3.12.11
  • NumPy v2.3.3
  • pandas v2.3.2
  • ProDy v2.6.1
  • OpenBabel v3.1.0
  • PyTorch v2.5.1
  • CUDA v12.4

1. Clone NA-MPNN

git clone https://github.com/baker-laboratory/NA-MPNN

2. Environment Setup

# Create and activate a new conda environment.
conda create -n NA-MPNN -y
conda activate NA-MPNN

# Install dependencies.
conda install \
    -c pytorch \
    -c nvidia \
    -c conda-forge \
    ipykernel \
    openbabel \
    ProDy \
    pyarrow \
    pandas \
    pytorch \
    pytorch-cuda \
    cmake

3. (Optional) Setup pdbx

Note: this step is only necessary if you want to work with the training code. For inference only, pdbx is not required. As such, the cmake dependency in the previous step is also optional if you only want to run inference.

Clone and build pdbx:

# Change to the NA-MPNN directory.
cd NA-MPNN

# Clone pdbx.
git clone https://github.com/soedinglab/pdbx.git ./pdbx

# Build pdbx.
cmake -S ./pdbx -B ./pdbx/build -DCMAKE_POLICY_VERSION_MINIMUM=3.5
cmake --build ./pdbx/build

Add pdbx to your python path:

# Set the PYTHONPATH environment variable to include pdbx.
conda env config vars set PYTHONPATH="$(pwd)/pdbx"

# Reactivate the conda environment to apply the changes.
conda deactivate && conda activate NA-MPNN

Examples

The following demonstrations should run in less than 1 minute on a CPU.

Design

python ./inference/run.py \
    --model_type "na_mpnn" \
    --mode "design" \
    --pdb_path "./inference/examples/4oqu.pdb" \
    --out_folder "./out/design"

One of the outputs of the design model is a fasta file containing the designed sequences. A non-standard one letter alphabet is used to represent protein and nucleic acid residues. The mapping from one letter code to residue type is as follows:

A: ALA
R: ARG
N: ASN
D: ASP
C: CYS
Q: GLN
E: GLU
G: GLY
H: HIS
I: ILE
L: LEU
K: LYS
M: MET
F: PHE
P: PRO
S: SER
T: THR
W: TRP
Y: TYR
V: VAL
X: UNK
a: DA
c: DC
g: DG
t: DT
x: DX
b: A
d: C
h: G
u: U
y: RX

Specificity

python ./inference/run.py \
    --model_type "na_mpnn" \
    --mode "specificity" \
    --pdb_path "./inference/examples/1am9.pdb" \
    --out_folder "./out/specificity" \
    --design_na_only 1 \
    --output_pdbs 0 \
    --output_sequences 0 \
    --output_specificity 1 \
    --omit_AA "ARNDCQEGHILKMFPSTWYVX"

The omit_AA flag is technically optional but is included in this case since we are only making specificity predictions for nucleic acid residues (protein residues are fixed for protein-DNA specificity prediction).

For compatibility reasons, the predicted PPM has "predictions" for all residues (including fixed protein residues). To extract the predicted DNA PPM, you can use the following code snippet:

import numpy as np

specificity_output_path = "./out/specificity/specificity/1am9.npz"
specificity_output = np.load(specificity_output_path, allow_pickle=True)

# Load useful arrays from the npz file.
predicted_ppm = specificity_output["predicted_ppm"]
dna_mask = specificity_output["dna_mask"].astype(bool)
restype_to_int = specificity_output["restype_to_int"].item()

residues_to_consider = [
    restype_to_int["DA"],
    restype_to_int["DC"],
    restype_to_int["DG"],
    restype_to_int["DT"],
]

# Subset the PPM.
predicted_ppm_dna = predicted_ppm[dna_mask][:, residues_to_consider]

For Additional Hyperparameter Options

python ./inference/run.py --help

Splits

The splits folder contains information about the structures and PPM IDs used for training and evaluation. This includes information on the train/validation/test splits.