ProGen2 API¶

ProGen2, with models up to 6.4B parameters, is adept at generating viable protein sequences and predicting their fitness. It's utilized in protein design, trained on data from genomic and metagenomic sources.

Postman API Docs

Python SDK Docs

Github Link

Paper Link

API Call with Python requests¶

We need to make sure we have the Python requests module loaded first.

import time
from biolmai import BioLM
from IPython.display import JSON  # Helpful UI for JSON display

ProGen2 Medium¶

data = {
    "params": {
        "temperature": 0.1,  # Temperature
        "top_p": 0.6,  # Nucleus sampling probability
        "num_samples": 2,  # Number of proteins to generate
        "max_length": 175  # Max length of generated proteins
    },
    "items": [
        {
            "context": "MA"  # Starting amino acid(s) for protein
        }
    ]
}

result = BioLM(entity="progen2-medium", action="generate",  items=data["items"], params=data["params"])

result

[{'sequence': 'MASSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS',
  'll_sum': -24.980133056640625,
  'll_mean': -0.14356398582458496},
 {'sequence': 'MASSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS',
  'll_sum': -24.980133056640625,
  'll_mean': -0.14356398582458496}]

We can view this a bit more easily below:

JSON(result)

<IPython.core.display.JSON object>

ProGen2 OAS¶

data = {
    "params": {
        "temperature": 0.7,  # Temperature
        "top_p": 0.6,  # Nucleus sampling probability
        "num_samples": 2,  # Number of proteins to generate
        "max_length": 175  # Max length of generated proteins
    },
    "items": [
        {
            "context": "EVQ"  # Starting amino acids for Antibody
        }
    ]
}

result = BioLM(entity="progen2-oas", action="generate",  items=data["items"], params=data["params"])

result

[{'sequence': 'EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARDRRDGYNYFDYWGQGTLVTVSS',
  'll_sum': -22.199049949645996,
  'll_mean': -0.18499208241701126},
 {'sequence': 'EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARDGRIAAAGTSYYYYGMDVWGQGTTVTVSS',
  'll_sum': -25.153675079345703,
  'll_mean': -0.19806043803691864}]

JSON(result)

<IPython.core.display.JSON object>

ProGen2 BFD90¶

data = {
    "params": {
        "temperature": 0.7,  # Temperature
        "top_p": 0.6,  # Nucleus sampling probability
        "num_samples": 2,  # Number of proteins to generate
        "max_length": 175  # Max length of generated proteins
    },
    "items": [
        {
            "context": "M"  # Starting amino acid(s) for protein
        }
    ]
}

result = BioLM(entity="progen2-bfd90", action="generate",  items=data["items"], params=data["params"])

result

JSON(result)

<IPython.core.display.JSON object>

Next Steps¶

Check out additional tutorials at jupyter.biolm.ai, or head over to our BioLM Documentation to explore additional models and functionality.

See more use-cases and APIs on your BioLM Console Catalog.¶

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Contact us to learn more.¶

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