{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced Usage: Asp 7\n", "\n", "Run this notebook on Google Colab:\n", "\n", "[](https://colab.research.google.com/github/AG-Peter/encodermap/blob/main/tutorials/notebooks_starter/02_Advanced_Usage-Asp7_Example.ipynb)\n", "\n", "Find the documentation of EncoderMap:\n", "\n", "https://ag-peter.github.io/encodermap\n", "\n", "**Goals:**\n", "\n", "In this tutorial you will learn:\n", "- [What's different when data lies in a periodic space.](#periodic_variables)\n", "- [How to visualize and observe training progression using `Tensorboard`.](#tensorboard)\n", "- [How to use EncoderMap's InteractivePlotting session.](#interactive_plotting)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**For Google colab only:**\n", "\n", "If you're on Google colab, please uncomment these lines and install EncoderMap." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-12-29T12:06:18.262751Z", "iopub.status.busy": "2024-12-29T12:06:18.262352Z", "iopub.status.idle": "2024-12-29T12:06:18.265187Z", "shell.execute_reply": "2024-12-29T12:06:18.264718Z" } }, "outputs": [], "source": [ "# !wget https://gist.githubusercontent.com/kevinsawade/deda578a3c6f26640ae905a3557e4ed1/raw/b7403a37710cb881839186da96d4d117e50abf36/install_encodermap_google_colab.sh\n", "# !sudo bash install_encodermap_google_colab.sh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you're on Google colab, you also want to download the data we will use in this notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-12-29T12:06:18.267333Z", "iopub.status.busy": "2024-12-29T12:06:18.267206Z", "iopub.status.idle": "2024-12-29T12:06:18.269354Z", "shell.execute_reply": "2024-12-29T12:06:18.269016Z" } }, "outputs": [], "source": [ "# !wget https://raw.githubusercontent.com/AG-Peter/encodermap/main/tutorials/notebooks_starter/asp7.csv\n", "# !wget https://raw.githubusercontent.com/AG-Peter/encodermap/main/tutorials/notebooks_starter/asp7.pdb\n", "# !wget https://raw.githubusercontent.com/AG-Peter/encodermap/main/tutorials/notebooks_starter/asp7.xtc" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Primer\n", "\n", "### Imports and load data\n", "\n", "In this tutorial we will use example data from a molecular dynamics simulation and learn more about advanced usage of EncoderMap. Encoder map can create low-dimensional maps of the vast conformational spaces of molecules. This allows easy identification of the most common molecular conformations and helps to understand the relations between these conformations. In this example, we will use data from a simulation of a simple peptide: hepta-aspartic-acid.\n", "\n", "First we need to import some libraries:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-12-29T12:06:18.271208Z", "iopub.status.busy": "2024-12-29T12:06:18.271077Z", "iopub.status.idle": "2024-12-29T12:06:22.115778Z", "shell.execute_reply": "2024-12-29T12:06:22.114948Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/kevin/git/encoder_map_private/encodermap/__init__.py:194: GPUsAreDisabledWarning: EncoderMap disables the GPU per default because most tensorflow code runs with a higher compatibility when the GPU is disabled. If you want to enable GPUs manually, set the environment variable 'ENCODERMAP_ENABLE_GPU' to 'True' before importing EncoderMap. To do this in python you can run:\n", "\n", "import os; os.environ['ENCODERMAP_ENABLE_GPU'] = 'True'\n", "\n", "before importing encodermap.\n", " _warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "58cdd700603545eeb84c9728181528de", "version_major": 2, "version_minor": 0 }, "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import encodermap as em\n", "import numpy as np\n", "import plotly.express as px\n", "import plotly.io as pio\n", "from math import pi\n", "try:\n", " from google.colab import data_table, output\n", " data_table.enable_dataframe_formatter()\n", " output.enable_custom_widget_manager()\n", " renderer = \"colab\"\n", "except ModuleNotFoundError:\n", " renderer = \"plotly_mimetype+notebook\"\n", "pio.renderers.default = renderer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need to load the input data. Different kinds of variables can be used to describe molecular conformations: e.g. Cartesian coordinates, distances, angles, dihedrals... In principle EncoderMap can deal with any of these inputs, however, some are better suited than others. The molecular conformation does not change when the molecule is translated or rotated. The chosen input variables should reflect that and be translationally and rotationally invariant. \n", "\n", "In this example we use the backbone dihedral angles phi and psi as input as they are translationally and rotationally invariant and describe the backbone of a protein/peptide very well.\n", "\n", "The \"asp7.csv\" file contains one column for each dihedral and one row for each frame of the trajectory. Additionally, the last column contains a cluster_id from a gromos clustering which we can later use for comparison. We can load this data using `np.loadtxt()`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-12-29T12:06:22.118516Z", "iopub.status.busy": "2024-12-29T12:06:22.118390Z", "iopub.status.idle": "2024-12-29T12:06:22.130143Z", "shell.execute_reply": "2024-12-29T12:06:22.129167Z" } }, "outputs": [], "source": [ "csv_path = \"asp7.csv\"\n", "data = np.loadtxt(csv_path, skiprows=1, delimiter=\",\")\n", "dihedrals = data[:, :-1]\n", "cluster_ids = data[:, -1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can view the molecular dynamics simulation right here in this jupyter notebook using the `nglview` package. This cell loads the `asp7.xtc` trajectory and `asp7.pdb` topology file and displays them as a ball and stick representation.\n", "\n", "If you don't have access to these files, you can replace the line\n", "\n", "```python\n", "traj = md.load('asp7.xtc', top='asp7.pdb')\n", "```\n", "\n", "with\n", "\n", "```python\n", "traj = md.load_pdb('https://files.rcsb.org/view/1YUF.pdb')\n", "```\n", "\n", "to load a small molecular conformation ensemble from the protein database.\n", "\n", "**Hint:**\n", "\n", "Sometimes the view can be not centered. Use the 'center' button in the gui to center the structure." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-12-29T12:06:22.132744Z", "iopub.status.busy": "2024-12-29T12:06:22.132589Z", "iopub.status.idle": "2024-12-29T12:06:22.307406Z", "shell.execute_reply": "2024-12-29T12:06:22.306571Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/kevin/git/encoder_map_private/encodermap/loading/features.py:1005: UserWarning:\n", "\n", "You requested a `em.loading.features.Feature` to calculate features in a periodic box, using the minimum image convention, but the trajectory you provided does not have unitcell information. 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\n | field | \nvalue | \n
---|---|---|
0 | \nn_frames | \n10001 | \n
1 | \nn_atoms | \n57 | \n
2 | \ndt (ps) | \n500.0 | \n
3 | \ntraj_file | \nasp7.xtc | \n
4 | \ntop_file | \nasp7.pdb | \n
5 | \nindex | \n[::] | \n
6 | \ncommon_str | \nNone | \n