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mise.dev.toml

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[tools]
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dprint = "latest"
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pandoc = "latest"

mise.toml

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pixi = "latest"
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[env]
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# _.python.venv = ".pixi/envs/default"
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_.python.venv = ".pixi/envs/default"

source/python-api/api-reference/embedding.rst

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Create embeddings for your protein sequences using open-source and proprietary models!
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Note that for PoET Models, you will also need to utilize our :doc:`align <align>`. workflow.
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Note that for PoET Models, you will also need to utilize our :doc:`align <align>` workflow.
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Interface
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---------

source/python-api/foundation-models/SVD-embeddings.ipynb

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"source": [
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"## Next steps\n",
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"\n",
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"For more information, visit the [Embeddings API](https://docs.openprotein.ai/api-python/embedding.html) reference."
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"For more information, visit the [Embeddings API](../api-reference/embedding.html) reference."
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]
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}
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],

source/python-api/foundation-models/attention-maps.ipynb

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"source": [
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"## Next steps\n",
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"\n",
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"For more information, visit the [Embeddings API](https://docs.openprotein.ai/api-python/embedding.html) reference."
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"For more information, visit the [Embeddings API](../api-reference/embedding.html) reference."
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]
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}
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],

source/python-api/foundation-models/logits.ipynb

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"source": [
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"## Next steps\n",
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"\n",
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"For more information, visit the [Embeddings API](https://docs.openprotein.ai/api-python/embedding.html) reference."
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"For more information, visit the [Embeddings API](../api-reference/embedding.html) reference."
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]
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}
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],

source/python-api/poet/creating-MSA.ipynb

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"source": [
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"## Next steps\n",
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"\n",
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"Learn more about the MSAs on our [MSA API](https://docs.openprotein.ai/api-python/align.html) page.\n",
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"Learn more about the MSAs on our [MSA API](../api-reference/align.html#openprotein.align.AlignAPI.create_msa) page.\n",
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"\n",
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"You can use your MSA to create a prompt and start generating, scoring, and analyzing sequences with our state-of-the-art PoET model. See [Creating a prompt](https://colab.research.google.com/drive/1BTuPVz4B5jLNvnsEXSJhSjBrOEWJ_ygj?usp=drive_link) for instructions.\n",
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"You can use your MSA to create a prompt and start generating, scoring, and analyzing sequences with our state-of-the-art PoET model. See [Creating a prompt](./creating-prompt.ipynb) for instructions.\n",
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"\n",
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"You can also use your MSA with our structure prediction tool to visualize the 3D structure of sequence. See [Structure prediction](https://docs.openprotein.ai/api-python/demos/fold_demo.html) for more information."
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"You can also use your MSA with our structure prediction tool to visualize the 3D structure of sequence. See [Using AlphaFold2](../structure-prediction/Using_AlphaFold2.html) for more information."
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]
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}
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source/python-api/poet/scoring-sequences.ipynb

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"\n",
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"## What you need before getting started\n",
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"\n",
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"You need a previously generated multiple sequence alignment (MSA) and a prompt. See [Creating an MSA](https://colab.research.google.com/drive/1uoX7g6CBYU_PQzh5mEI6WAR8MbVDLq9A#scrollTo=kB3_IJ-HZh-g) and [Creating a prompt](https://colab.research.google.com/drive/1BTuPVz4B5jLNvnsEXSJhSjBrOEWJ_ygj#scrollTo=XnXhgmXVbMxD) for more information.\n",
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"You need a previously generated multiple sequence alignment (MSA) and a prompt. See [Creating an MSA](./creating-MSA.ipynb) and [Creating a prompt](./creating-prompt.ipynb) for more information.\n",
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"\n",
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"## Scoring your sequences\n",
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"\n",
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"source": [
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"## Next steps\n",
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"\n",
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"Learn more about the `score` function in our [PoET API](https://docs.openprotein.ai/api-python/embedding.html#openprotein.api.embedding.PoETModel.score) page.\n",
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"Learn more about the `score` function in our [PoET Model API](../api-reference/embedding.html#openprotein.embeddings.PoETModel.score) page.\n",
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"\n",
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"Now that you have a list of sequence variants of interest, you can use the PoET model to perform a [single site analysis] to score all single substitution variants of your parent sequence conditioned on the prompt. See [Using PoET single site analysis](https://colab.research.google.com/drive/1iWZkoQtQxbLoxrzhUeHFTNO5TtWoMIvX?usp=drive_link) for more information."
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source/python-api/poet/single-site-analysis.ipynb

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"\n",
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"This tutorial shows you how to perform a single site analysis using PoET, which scores all single substitution variants of an input sequence with a given prompt. Use this as a starting point to design single mutant or combinatorial variant libraries and predict the strength of protein activity.\n",
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"\n",
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"Find more information about the PoET model on our [PoET API](https://docs.openprotein.ai/api-python/embedding.html#openprotein.api.embedding.PoETModel) page.\n",
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"Find more information about the PoET model on our [PoET model API](../api-reference/embedding.html#openprotein.embeddings.PoETModel) page.\n",
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"\n",
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"## What you need before getting started\n",
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"\n",
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"You need a prompt and a multiple sequence alignment (MSA). For more information, see [Creating a multiple sequence alignment](https://colab.research.google.com/drive/1uoX7g6CBYU_PQzh5mEI6WAR8MbVDLq9A?usp=drive_link) and [Creating a prompt](https://colab.research.google.com/drive/1BTuPVz4B5jLNvnsEXSJhSjBrOEWJ_ygj?usp=drive_link)."
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"You need a prompt and a multiple sequence alignment (MSA). For more information, see [Creating a multiple sequence alignment](./creating-MSA.ipynb) and [Creating a prompt](./creating-prompt.ipynb)."
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},
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"outputs": [],
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"\n",
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"poet = session.embedding.get_model(\"poet\")\n",
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"sspjob = poet.single_site(prompt=prompt, sequence=\"MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQRVEDAFYTLVREIRQYRLKKISKEEKTPGCVKIKKCIIM\".encode())"
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]

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