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4 | 4 | "metadata": {
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5 | 5 | "accelerator": "GPU",
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6 | 6 | "colab": {
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7 |
| - "name": "AlphaFold2.ipynb", |
8 | 7 | "provenance": [],
|
9 | 8 | "machine_shape": "hm",
|
10 |
| - "gpuType": "T4", |
11 |
| - "include_colab_link": true |
| 9 | + "gpuType": "A100" |
12 | 10 | },
|
13 | 11 | "kernelspec": {
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14 | 12 | "display_name": "Python 3",
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|
28 | 26 | }
|
29 | 27 | },
|
30 | 28 | "cells": [
|
31 |
| - { |
32 |
| - "cell_type": "markdown", |
33 |
| - "metadata": { |
34 |
| - "id": "view-in-github", |
35 |
| - "colab_type": "text" |
36 |
| - }, |
37 |
| - "source": [ |
38 |
| - "<a href=\"https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" |
39 |
| - ] |
40 |
| - }, |
41 | 29 | {
|
42 | 30 | "cell_type": "markdown",
|
43 | 31 | "metadata": {
|
|
232 | 220 | "cell_type": "code",
|
233 | 221 | "source": [
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234 | 222 | "#@markdown ### Advanced settings\n",
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235 |
| - "model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\", \"deepfold_v1\"]\n", |
| 223 | + "model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\", \"deepfold_v1\", \"alphafold2\"]\n", |
236 | 224 | "#@markdown - if `auto` selected, will use `alphafold2_ptm` for monomer prediction and `alphafold2_multimer_v3` for complex prediction.\n",
|
237 | 225 | "#@markdown Any of the mode_types can be used (regardless if input is monomer or complex).\n",
|
238 | 226 | "num_recycles = \"3\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n",
|
|
243 | 231 | "#@markdown - max amber relax iterations, `0` = unlimited (AlphaFold2 default, can take very long)\n",
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244 | 232 | "pairing_strategy = \"greedy\" #@param [\"greedy\", \"complete\"] {type:\"string\"}\n",
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245 | 233 | "#@markdown - `greedy` = pair any taxonomically matching subsets, `complete` = all sequences have to match in one line.\n",
|
246 |
| - "\n", |
| 234 | + "calc_extra_ptm = False #@param {type:\"boolean\"}\n", |
| 235 | + "#@markdown - return pairwise chain iptm/actifptm\n", |
247 | 236 | "\n",
|
248 | 237 | "#@markdown #### Sample settings\n",
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249 | 238 | "#@markdown - enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.\n",
|
|
382 | 371 | " input_features_callback=input_features_callback,\n",
|
383 | 372 | " save_recycles=save_recycles,\n",
|
384 | 373 | " user_agent=\"colabfold/google-colab-main\",\n",
|
| 374 | + " calc_extra_ptm=calc_extra_ptm,\n", |
385 | 375 | ")\n",
|
386 | 376 | "results_zip = f\"{jobname}.result.zip\"\n",
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387 | 377 | "os.system(f\"zip -r {results_zip} {jobname}\")"
|
|
508 | 498 | },
|
509 | 499 | {
|
510 | 500 | "cell_type": "code",
|
511 |
| - "metadata": { |
512 |
| - "id": "33g5IIegij5R", |
513 |
| - "cellView": "form" |
514 |
| - }, |
515 | 501 | "source": [
|
516 | 502 | "#@title Package and download results\n",
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517 | 503 | "#@markdown If you are having issues downloading the result archive, try disabling your adblocker and run this cell again. If that fails click on the little folder icon to the left, navigate to file: `jobname.result.zip`, right-click and select \\\"Download\\\" (see [screenshot](https://pbs.twimg.com/media/E6wRW2lWUAEOuoe?format=jpg&name=small)).\n",
|
|
527 | 513 | " uploaded.Upload()\n",
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528 | 514 | " print(f\"Uploaded {jobname}.result.zip to Google Drive with ID {uploaded.get('id')}\")"
|
529 | 515 | ],
|
| 516 | + "metadata": { |
| 517 | + "cellView": "form", |
| 518 | + "id": "R_AH6JSXaeb2" |
| 519 | + }, |
530 | 520 | "execution_count": null,
|
531 | 521 | "outputs": []
|
532 | 522 | },
|
|
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