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"name": "#%% md\n"
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"source": [
"# Working with trajectory ensembles\n",
"\n",
"**Welcome**\n",
"\n",
"Welcome to the MD section of the EncoderMap tutorial. All EncoderMap tutorials are provided as jupyter notebooks, that you can run locally, on binderhub, or even on google colab.\n",
"\n",
"\n",
"Run this notebook on Google Colab:\n",
"\n",
"[](https://colab.research.google.com/github/AG-Peter/encodermap/blob/main/tutorials/notebooks_MD/01_Working_with_trajectory_ensembles.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 CVs are.](#primer)\n",
"- [How EncoderMaps' new SingleTraj class loads MD data.](#classes-for-working-with-md-data)\n",
"- [How a SingleTraj can be associated with CVs.](#loading-cvs)\n",
"- [How to construct a TrajEnsemble from multiple SingleTrajs.](#the-trajensemble-class-contains-multiple-singletrajs)\n",
"- [How you can write your own Features that will put CVs into SingleTraj and TrajEnsemble.](#write-custom-features-no-1)\n",
"- [How to save your SingleTrajs and TrajEnsembles along with their CVs.](#saving-trajectory-and-cvs-into-one-file)\n",
"\n",
"### For Google colab only:\n",
"\n",
"If you're on Google colab, please uncomment these lines and install EncoderMap."
]
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{
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"source": [
"# !wget https://gist.githubusercontent.com/kevinsawade/deda578a3c6f26640ae905a3557e4ed1/raw/b7403a37710cb881839186da96d4d117e50abf36/install_encodermap_google_colab.sh\n",
"# !sudo bash install_encodermap_google_colab.sh"
]
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"metadata": {
"editable": true,
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"source": [
"\n",
"\n",
"## Primer\n",
"\n",
"### Collective Variables\n",
"\n",
"Collective Variables (CVs) are often used to simplify and filter the xyz-Data of MD simulations. They are often employed to make sense of a complex protein (or more general molecular) system by breaking it down into just a few well-defined descriptors. They are similar to reaction coordinates, which are 1-dimensional variables along a reaction pathway but can be higher dimensional. When we think about a receptor-ligand system, the distance between the two species is often used as a reaction coordinate. A set of this distance CV and the relative rotation between receptor and ligand can add much more information and help understand the system. It becomes apparent that clever selection of CVs is an important task that many scientists in different fields face day to day.\n",
"\n",
"\n",
"\n",
"With the tools presented in this notebook you will be able to work in a fluent and natural way with MD trajectories and their associated CV data. In the scope of this work we define CVs as:\n",
"\n",
"**A collection of values that is in one of its dimensions aligned with the frames/timesteps of the underlying simulation data.**"
]
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"source": [
"### Example CVs\n",
"\n",
"Here's a list of widely used CVs:\n",
"\n",
"- Distances: This category of CVs can contain a 1-diemsnional scalar value describing the distance between two species in a receptor-ligand system. A 1-dimensional CV of the end-to-end distance of a protein can describe the proteins folding state in an approximate and generalizing manner. Distance CVs can also be higher-dimensional. The pairwise distances between $\\mathrm{C_\\alpha}$ atoms of a protein with $n$ residues can be captured as a $n \\times n$ matrix. A so-called hollow matrix, where all diagonal-elements are zero which is also symmetric. Most often a vector of length $\\binom{n}{2}$ is used to describe the pairwise distances in a protein. The distance between a protein and a membrane/interface would also fall into this category.\n",
" \n",
"- Angles: Angular CVs lie in a periodic space of $(-\\pi, \\pi]$ or $(0, 2\\pi]$, or $(0, 360]$. The well-known Ramachandran plot uses a protein's $\\phi$ and $\\psi$ angles to define regions of $psi$-$phi$ combinations which correlate to different secondary structure motifs. Besides the backbone angles other angles can become important in understanding a protein's conformations. Lysine is often modified via acetylation, phosphorylation, methylation, ubiquitylation and it's sidechain angles ($\\chi_1$ to $\\chi_5$) can be important descriptors in such a system. Pseudo-Dihedral angles lie also in this category. \n",
"\n",
"- Integer/binary values. If a protein has well-defined states (folded and unfolder) a binary value describing these states could also be a useful CV.\n",
"\n",
"- Values from other calculations and hyperparameters. An example for this category could be the temperature at which the simulation was carried out, when simulations were conducted at multiple temperatures. If phase space sub states were obtained by either using Markov-Chain models, or by using some sort of clustering algorithm (GROMOS), the membership to such cluster could also present CVs.\n",
"\n",
"- Positional values: Maybe even the full position of an atom or a group of atoms could be an important descriptor for a system.\n",
"\n",
"- etc."
]
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"cell_type": "markdown",
"metadata": {},
"source": [
"#### Raw data\n",
"\n",
"We can view the raw xyz-data of trajectories as a high-dimensional array with a shape of (n_frames, n_atoms, 3). The last axis correxponds to the cartesian coordinates. Indexing this axis will give us the *x*, *y*, and *z* coordinates, respectively."
]
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{
"cell_type": "code",
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"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"
]
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"source": [
"# todo: delete\n",
"from __future__ import annotations\n",
"import plotly.graph_objects as go\n",
"import numpy as np\n",
"from pathlib import Path\n",
"\n",
"import encodermap as em\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
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"text": [
"/home/kevin/git/encoder_map_private/encodermap/kondata.py:477: UserWarning: EncoderMap's repository at https://sawade.io/encodermap_data/linear_dimers is unreachable.\n",
" warnings.warn(f\"EncoderMap's repository at {url} is unreachable.\")\n"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"First frame, first atom, x-coordinate:\n",
"traj.xyz[0, 0, 0]=6.1210003\n",
"First 2 frames, first atom, y-coordinates:\n",
"traj.xyz[:2, 0, 1]=array([6.42 , 6.3940005], dtype=float32)\n",
"All frames, all atoms. Only z-coordinates:\n",
"[[1.1060001 1.092 1.028 ... 5.7790003 5.7790003 5.881 ]\n",
" [1.056 1.0610001 0.96400005 ... 5.76 5.689 5.8650002 ]\n",
" [1.062 1.0090001 1.144 ... 5.8500004 5.9550004 5.8240004 ]\n",
" ...\n",
" [0.12100001 0.031 0.12400001 ... 4.783 4.846 4.7850003 ]\n",
" [0.116 0.081 0.125 ... 4.617 4.6980004 4.5950003 ]\n",
" [0.06900001 0.06 0.063 ... 4.5290003 4.486 4.6260004 ]]\n"
]
}
],
"source": [
"traj = em.load_project('linear_dimers', traj=0)\n",
"\n",
"print(f\"First frame, first atom, x-coordinate:\\n{traj.xyz[0, 0, 0]=}\")\n",
"print(f\"First 2 frames, first atom, y-coordinates:\\n{traj.xyz[:2, 0, 1]=}\")\n",
"print(f\"All frames, all atoms. Only z-coordinates:\\n{traj.xyz[..., -1]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 3-dimensions of this data can be used for plotting. However, we don't gain much from these plots."
]
},
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