openprotein#

This document describes the base dataclasses and primitives for working with the platform.

This includes the OpenProtein session object, as well as primitives like the Protein and Model objects.

Session#

Create an authorized session to OpenProtein.AI backend.

openprotein.connect(username=None, password=None, backend=None, timeout=180)[source]#

Connect and create a OpenProtein session.

Parameters:

username (str, optional) – The username of the user. If not provided, taken from the environment variable OPENPROTEIN_USERNAME or a configuration file at ~/.openprotein/config.toml.
password (str, optional) – The password of the user. If not provided, taken from the environment variable OPENPROTEIN_PASSWORD or a configuration file at ~/.openprotein/config.toml.

Examples

>>> session = openprotein.connect("username", "password")

class openprotein.OpenProtein(username, password, backend, timeout=180)[source]#

The base class for accessing OpenProtein API functionality.

property data: DataAPI#: The data submodule gives access to functionality for uploading and accessing user data.

property jobs: JobsAPI#: The jobs submodule gives access to functionality for listing jobs and checking their status.

property align: AlignAPI#: The Align submodule gives access to the sequence alignment capabilities by building MSAs and prompts that can be used with PoET.

property prompt: PromptAPI#: The Align submodule gives access to the sequence alignment capabilities by building MSAs and prompts that can be used with PoET.

property embedding: EmbeddingsAPI#: The embedding submodule gives access to protein embedding models and their inference endpoints.

property embeddings: EmbeddingsAPI#: The embedding submodule gives access to protein embedding models and their inference endpoints.

property svd: SVDAPI#: The embedding submodule gives access to protein embedding models and their inference endpoints.

property umap: UMAPAPI#: The embedding submodule gives access to protein embedding models and their inference endpoints.

property predictor: PredictorAPI#: The predictor submodule gives access to training and predicting with predictors built on top of embeddings.

property design: DesignAPI#: The designer submodule gives access to functionality for designing new sequences using models from predictor train.

property fold: FoldAPI#: The fold submodule gives access to functionality for folding sequences and returning PDBs.

property models: ModelsAPI#: The models submodule provides a unified entry point to all protein models.