Bump version to 2.3.1

PiperOrigin-RevId: 501297397
Change-Id: Ic1bb166581047e0e8c46845f41f8c55c10f32ef9

notebooks/AlphaFold.ipynb

@@ -8,11 +8,11 @@
       "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",

setup.py

@@ -18,7 +18,7 @@ from setuptools import setup
 
 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.',