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Bump version to 2.3.1
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PiperOrigin-RevId: 501297397
Change-Id: Ic1bb166581047e0e8c46845f41f8c55c10f32ef9
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Htomlinson14 authored and copybara-github committed Jan 11, 2023
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6 changes: 3 additions & 3 deletions notebooks/AlphaFold.ipynb
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"source": [
"# AlphaFold Colab\n",
"\n",
"This Colab notebook allows you to easily predict the structure of a protein using a slightly simplified version of [AlphaFold v2.3.0](https://doi.org/10.1038/s41586-021-03819-2). \n",
"This Colab notebook allows you to easily predict the structure of a protein using a slightly simplified version of [AlphaFold v2.3.1](https://doi.org/10.1038/s41586-021-03819-2). \n",
"\n",
"**Differences to AlphaFold v2.3.0**\n",
"**Differences to AlphaFold v2.3.1**\n",
"\n",
"In comparison to AlphaFold v2.3.0, this Colab notebook uses **no templates (homologous structures)** and a selected portion of the [BFD database](https://bfd.mmseqs.com/). We have validated these changes on several thousand recent PDB structures. While accuracy will be near-identical to the full AlphaFold system on many targets, a small fraction have a large drop in accuracy due to the smaller MSA and lack of templates. For best reliability, we recommend instead using the [full open source AlphaFold](https://github.com/deepmind/alphafold/), or the [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/).\n",
"In comparison to AlphaFold v2.3.1, this Colab notebook uses **no templates (homologous structures)** and a selected portion of the [BFD database](https://bfd.mmseqs.com/). We have validated these changes on several thousand recent PDB structures. While accuracy will be near-identical to the full AlphaFold system on many targets, a small fraction have a large drop in accuracy due to the smaller MSA and lack of templates. For best reliability, we recommend instead using the [full open source AlphaFold](https://github.com/deepmind/alphafold/), or the [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/).\n",
"\n",
"**This Colab has a small drop in average accuracy for multimers compared to local AlphaFold installation, for full multimer accuracy it is highly recommended to run [AlphaFold locally](https://github.com/deepmind/alphafold#running-alphafold).** Moreover, the AlphaFold-Multimer requires searching for MSA for every unique sequence in the complex, hence it is substantially slower. If your notebook times-out due to slow multimer MSA search, we recommend either using Colab Pro or running AlphaFold locally.\n",
"\n",
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2 changes: 1 addition & 1 deletion setup.py
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setup(
name='alphafold',
version='2.3.0',
version='2.3.1',
description='An implementation of the inference pipeline of AlphaFold v2.0.'
'This is a completely new model that was entered as AlphaFold2 in CASP14 '
'and published in Nature.',
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