{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time-Resolved Difference Map \n", "\n", "Time-resolved crystallography experiments make use of X-ray diffraction to monitor structural changes in a crystalline sample. Such experiments typically use a pump-probe setup where atomic motions are induced by a \"pump\" (often some sort of a laser pulse) and then are monitored using a probe X-ray pulse. Due to the need for an X-ray pulse with a short time duration, it is most common to conduct time-resolved diffraction experiments using an XFEL or using a Laue (pink beam) beamline at a synchrotron in order to achieve the desired X-ray intensity. \n", "\n", "A common methodology for investigating structural changes in time-resolved experiments is using difference maps between structure factor amplitudes collected with and without a perturbation. These $(|F_{On}| - |F_{Off}|)$ maps may be noisy due to systematic errors or scaling artifacts, and have historically been weighted based on the magnitude of the difference signal and/or the error estimates associated with the measured values. \n", "\n", "Photoactive yellow protein (PYP) is a model system in time-resolved crystallography due to the trans-cis isomerization of its chromophore which occurs upon absorption of blue light. Here, we will use `reciprocalspaceship` to produce a weighted difference map of PYP to investigate the structural changes that occur 2ms after illumination with blue light. This dataset was collected at the [BioCARS Laue beamline APS-14-ID](https://biocars.uchicago.edu/) from a PYP crystal with $P 6_3$ symmetry, and were processed using Precognition (Renz Research, Inc.), a software suite for Laue data processing. This data was collected and provided by Marius Schmidt and Vukica Šrajer." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_context(\"notebook\", font_scale=1.4)\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import reciprocalspaceship as rs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.8.9\n" ] } ], "source": [ "print(rs.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### Load `On` and `Off` PYP DataSets \n", "\n", "The `.hkl` files used in this example came directly from Precognition and contain scaled, merged structure amplitudes (**F**) and associated errors (**SigF**). Precognition uses a different convention for the reciprocal space asymmetric unit (ASU) than CCP4/Phenix, so we will map all reflections to the ASU. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def load(hkl, cell=(66.9, 66.9, 40.8, 90.0, 90.0, 120.0), sg=173):\n", " \"\"\"\n", " Load HKL file from Precognition and map reflections to the reciprocal space ASU.\n", " \"\"\"\n", " dataset = rs.read_precognition(hkl, *cell, sg=sg)\n", " dataset.hkl_to_asu(inplace=True)\n", " dataset.drop(columns=\"M/ISYM\", inplace=True)\n", " return dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "off = load(\"data/PYP_varEll_2sig_off_1.6A.hkl\")\n", "on = load(\"data/PYP_varEll_2sig_2ms_1.6A.hkl\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since difference maps can only be made with Fourier magnitudes that were measured in both datasets, we will subset the datasets to their common Miller indices." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "pyp = off.merge(on, left_index=True, right_index=True, suffixes=(\"_off\", \"_on\"))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " F_off SigF_off F_on SigF_on\n", "H K L \n", "0 1 2 56.646 0.036 75.991 0.032\n", " 3 89.061 0.03 89.667 0.031\n", " 4 95.419 0.048 93.886 0.047\n", " 5 160.466 0.038 162.553 0.035\n", " 6 77.869 0.075 79.139 0.083" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pyp.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### Compute Difference Map Coefficients and Errors \n", "\n", "We will compute $(|F_{On}| - |F_{Off}|)$ for use as the coefficients of the difference map, and we will propagate the uncertainties in quadrature. These propagated uncertainties will be used when computing weights for each Miller index." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "pyp[\"DF\"] = (pyp[\"F_on\"] - pyp[\"F_off\"]).astype(\"SFAmplitude\")\n", "pyp[\"SigDF\"] = np.sqrt(pyp[\"SigF_on\"]**2 + pyp[\"SigF_off\"]**2).astype(\"Stddev\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### Compute Difference Map Weights \n", "\n", "There are several weighting schemes that have been used to produce time-resolved difference maps. Many of them take the form below, involving a term based on the uncertainty in the difference structure factor amplitude ($\\sigma_{\\Delta F}$), and optionally, a scale term based on the the magnitude of the observed $\\Delta F$. With $\\alpha=0$, these weights take the form employed in [Ursby and Bourgeois, **Acta Cryst** (1997)](https://scripts.iucr.org/cgi-bin/paper?he0183). On the other hand, [Šrajer *et al*, **Biochemistry** (2001)](https://pubs.acs.org/doi/10.1021/bi010715u) employed weights with $\\alpha=1$ in order to decrease the impact of abnormally large values of $\\Delta F$ that may have erroneously small uncertainties. Finally, other weighting schemes have employed intermediate values of $\\alpha$ ([Hekstra *et al*, **Nature** (2016)](https://www.nature.com/articles/nature20571)). \n", "\n", "\\begin{equation}\n", "w = \\left(1 + \\frac{\\sigma_{\\Delta F}^2}{\\langle \\sigma_{\\Delta F}^2 \\rangle} + \\alpha\\frac{|\\Delta F|^2}{\\langle |\\Delta F|^2 \\rangle} \\right)^{-1}\n", "\\end{equation} \n", "\n", "For illustration purposes, we will compute weights with $\\alpha=0.05$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def compute_weights(df, sigdf, alpha=0):\n", " \"\"\"\n", " Compute weights for each structure factor based on deltaF and its uncertainty\n", " \"\"\"\n", " w = (1 + (sigdf**2 / (sigdf**2).mean()) + alpha*(df**2 / (df**2).mean()))\n", " return w**-1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "pyp[\"W\"] = compute_weights(pyp[\"DF\"], pyp[\"SigDF\"], alpha=0.05)\n", "pyp[\"WDF\"] = (pyp[\"W\"]*pyp[\"DF\"]).astype(\"F\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's visualize the weights relative to the magnitude of $\\Delta F$ and the signal-to-noise ratio in order to understand how they will affect different structure factors." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(10, 7))\n", "pts = ax.scatter(pyp.DF, pyp.DF.abs()/pyp.SigDF, c=pyp.W)\n", "ax.set_xlabel(r\"$\\Delta F$\")\n", "ax.set_ylabel(r\"$\\frac{\\left| \\Delta F \\right|}{\\sigma_{\\Delta F}}$\")\n", "\n", "# Inset\n", "axins = ax.inset_axes([0.6, 0.6, 0.37, 0.37])\n", "axins.scatter(pyp.DF, pyp.DF.abs()/pyp.SigDF, c=pyp.W)\n", "x1, x2, y1, y2 = -25, 25, 0, 20\n", "axins.set_xlim(x1, x2)\n", "axins.set_ylim(y1, y2)\n", "ax.indicate_inset_zoom(axins)\n", "\n", "fig.colorbar(pts, label=\"Weight\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen in the above plot, difference Fourier coefficients with low signal-to-noise ratios (large $\\sigma_{\\Delta F}$ relative to $|\\Delta F|$) are assigned lower weight. Difference Fourier coefficients with large amplitude are also assigned lower weight." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "### Visualize Weighted Difference Map \n", "\n", "We have our difference Fourier amplitudes and weights, so we just need phases in order to make a difference map. We will use phases from a refined \"dark\" state model of PYP, PDB: 2PHY. Phases were computed using [phenix.fmodel](https://www.phenix-online.org/documentation/reference/fmodel.html). " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "ref = rs.read_mtz(\"data/2PHY.pdb.mtz\")\n", "pyp[\"PHIFMODEL\"] = ref.loc[pyp.index, \"PHIFMODEL\"]\n", "pyp.write_mtz(\"data/PYP_diffmap.mtz\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now visualize the effects of weighting the $(F_{On} - F_{Off})$ difference map. The map is overlaid with the refined model of the dark state (red; PDB: 2PHY) and the pB intermediate state of the chromophore (green; PDB: 3UME)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
\n", " \n", "
Time-resolved difference map of PYP photocycle with and without weights\n", "
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%html\n", "
\n", " \n", "
Time-resolved difference map of PYP photocycle with and without weights. The dark state structure is shown in \n", "
" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }