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ProteinMPNN (Protein Message Passing Neural Network) is a cutting-edge, deep learning-based, graph neural network designed to predict amino acid sequences for given protein backbones.
Overview
ProteinMPNN (Protein Message Passing Neural Network) is a cutting-edge, deep learning-based, graph neural network designed to predict amino acid sequences for given protein backbones.
This network leverages evolutionary, functional, and structural information to generate sequences that are likely to fold into the desired 3D structures.
The input to the neural network is a 3D structure of a protein in PDB format, and the output is the amino-acid sequence in Multi-FASTA format.
ProteinMPNN is one of many NIMs that you can apply to tasks in biosciences and drug discovery. NIMs make it easy to chain models together to develop a complete in silico drug discovery pipeline.
For example, you can use a ProteinMPNN NIM as a subsequent step after RFdiffusion generative model NIM in order to determine possible amino acid sequence.
Highlights
- Increased productivity: NIMs enable developers to build generative AI applications quickly, in minutes rather than weeks, by providing a standardized way to add AI capabilities to their applications. Simplified deployment: NIMs provide containers that can be easily deployed on various platforms, including clouds, data centers, or workstations, making it convenient for developers to test and deploy their applications.
- ProteinMPNN potential applications spans from accelerating drug discovery to advancing synthetic biology.
Details
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Pricing
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Try this product free for 31 days according to the free trial terms set by the vendor.
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.12xlarge Inference (Batch) Recommended | Model inference on the ml.g5.12xlarge instance type, batch mode | $1.00 |
ml.g6e.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $1.00 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $1.00 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $1.00 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $1.00 |
ml.p4de.24xlarge Inference (Real-Time) | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $1.00 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $1.00 |
ml.p5e.48xlarge Inference (Real-Time) | Model inference on the ml.p5e.48xlarge instance type, real-time mode | $1.00 |
ml.p5en.48xlarge Inference (Real-Time) | Model inference on the ml.p5en.48xlarge instance type, real-time mode | $1.00 |
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters on /invocations and /ping APIs that can be used to control the generated text. See examples and field descriptions below.
- Input MIME type
- application/json
# Example PDB content for ProteinMPNN (a small test structure)
# In practice, you would read this from a PDB file
example_pdb = """ATOM 1 N ALA A 1 0.000 0.000 0.000 1.00 0.00 N
ATOM 2 CA ALA A 1 1.458 0.000 0.000 1.00 0.00 C
ATOM 3 C ALA A 1 2.009 1.420 0.000 1.00 0.00 C
ATOM 4 O ALA A 1 1.246 2.390 0.000 1.00 0.00 O
ATOM 5 N GLY A 2 3.326 1.554 0.000 1.00 0.00 N
ATOM 6 CA GLY A 2 3.941 2.871 0.000 1.00 0.00 C
ATOM 7 C GLY A 2 5.449 2.801 0.000 1.00 0.00 C
ATOM 8 O GLY A 2 6.067 1.739 0.000 1.00 0.00 O
END"""
# ProteinMPNN predict payload
payload = {
"input_pdb": example_pdb,
"num_seq_per_target": 3,
"sampling_temp": [0.1],
"use_soluble_model": False
}
response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(
EndpointName=model.endpoint_name,
ContentType="application/json",
Body=json.dumps(payload),
)
output = json.loads(response["Body"].read().decode("utf8"))
print("Designed sequences (mfasta):")
print(output.get("mfasta", ""))
print("\nScores:", output.get("scores", []))
Resources
Support
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