diff --git a/stab_ESM_IF.ipynb b/stab_ESM_IF.ipynb
index d4b6620..6f06892 100644
--- a/stab_ESM_IF.ipynb
+++ b/stab_ESM_IF.ipynb
@@ -1,23 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": [],
-      "gpuType": "T4"
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    },
-    "accelerator": "GPU"
-  },
   "cells": [
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "AcjxZ9mXikrK"
+      },
       "source": [
         "# <b><font color='#009e74'>Absolute folding stabiity prediction via generative models</font></b>\n",
         "Colaboratory implementation of : **Cagiada M., Ovchinnikov S. & Lindorff-Larsen K.** - [Predicting absolute protein folding stability using generative models\n",
@@ -31,13 +18,13 @@
         "\n",
         "where $\\mathscr{L}_i^{WT}$ is the amino acid likelihood extracted from ESM-IF for the wild-type amino acid at position i, to evaluate the absolute stability ($\\Delta G_{f-u}$) for a specific protein folding.\n",
         "\n"
-      ],
-      "metadata": {
-        "id": "AcjxZ9mXikrK"
-      }
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "a4dANSUAinaS"
+      },
       "source": [
         "Additional notes:\n",
         "\n",
@@ -60,10 +47,7 @@
         "\n",
         "- An **alternative sequence** can be used in the input instead of the sequence extracted from the PDB file, **HOWEVER** it must be the same length as the original sequence.\n",
         "****"
-      ],
-      "metadata": {
-        "id": "a4dANSUAinaS"
-      }
+      ]
     },
     {
       "cell_type": "code",
@@ -117,7 +101,7 @@
         "    os.system(f\"pip install torch-spline-conv -f https://data.pyg.org/whl/torch-{TORCH}+{CUDA}.html\")\n",
         "    os.system(f\"pip install torch-geometric\")\n",
         "    os.system(f\"pip install biopython\")\n",
-        "    os.system(f\"pip install biotite\")\n",
+        "    os.system(f\"pip install biotite==0.41\")\n",
         "\n",
         "    print(\"installing esmfold...\")\n",
         "    # install esmfold\n",
@@ -150,6 +134,12 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "lWXN38psLY3u"
+      },
+      "outputs": [],
       "source": [
         "#@title <b><font color='#f0e422'>PRELIMINARY OPERATIONS:</font> Load EXTRA functions\n",
         "#@markdown Run the cell to load the required functions\n",
@@ -201,16 +191,16 @@
         "\n",
         "    score = token_probs[0,idx, mt_encoded]\n",
         "    return score.item()"
-      ],
-      "metadata": {
-        "id": "lWXN38psLY3u",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "fbmpNhrvbKil"
+      },
+      "outputs": [],
       "source": [
         "#@title <b><font color='#56b4e9'> DATA UPLOADING</font>\n",
         "#@markdown Fill in the fields and run the cell to set up the job name, import the structure, select the chain and upload an alternative sequence (not mandatory).\n",
@@ -264,16 +254,16 @@
         "\n",
         "print('... Target sequence:', sequence_structure)\n",
         "#@markdown ****"
-      ],
-      "metadata": {
-        "id": "fbmpNhrvbKil",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "ocOnty1TP9Ec"
+      },
+      "outputs": [],
       "source": [
         "#@title <b><font color='#56b4e9'> MODEL RUN</font>\n",
         "#@markdown Run this cell to evaluate the ΔG for the selected structure and sequence\n",
@@ -303,31 +293,28 @@
         "  os.rename(f\"inputs/query_protein.pdb\",f\"outputs/{output_name_pdb}\")\n",
         "except:\n",
         "  print('!!!! Data not saved, please re-upload the structure by running the uploading cell')\n"
-      ],
-      "metadata": {
-        "id": "ocOnty1TP9Ec",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "HdIPxqSE6zWi"
+      },
+      "outputs": [],
       "source": [
         "#@title <b><font color='#56b4e9'>DOWNLOAD RESULTS </font></b>\n",
         "#@markdown **N.B:** This will download **ALL** the predictions produced during the current session as zip file\n",
         "os.system( \"zip -r {} {}\".format( f\"dG_runs.zip\" , f\"outputs\" ) )\n",
         "files.download(f\"dG_runs.zip\")\n"
-      ],
-      "metadata": {
-        "id": "HdIPxqSE6zWi",
-        "cellView": "form"
-      },
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "0NULd4ycW4dw"
+      },
       "source": [
         "<b><font color='#56b4e9'>EXTRA </font></b>\n",
         "\n",
@@ -396,10 +383,23 @@
         "citation here\n",
         "\n",
         "```\n"
-      ],
-      "metadata": {
-        "id": "0NULd4ycW4dw"
-      }
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
     }
-  ]
-}
\ No newline at end of file
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}