Source code for MDAnalysis.analysis.rms

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# R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler,
# D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein.
# MDAnalysis: A Python package for the rapid analysis of molecular dynamics
# simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th
# Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy.
# doi: 10.25080/majora-629e541a-00e
# N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein.
# MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.
# J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787
Calculating root mean square quantities --- :mod:`MDAnalysis.analysis.rms`

:Author: Oliver Beckstein, David L. Dotson, John Detlefs
:Year: 2016
:Copyright: GNU Public License v2

.. versionadded:: 0.7.7
.. versionchanged:: 0.11.0
   Added :class:`RMSF` analysis.
.. versionchanged:: 0.16.0
   Refactored RMSD to fit AnalysisBase API

The module contains code to analyze root mean square quantities such
as the coordinat root mean square distance (:class:`RMSD`) or the
per-residue root mean square fluctuations (:class:`RMSF`).

This module uses the fast QCP algorithm [Theobald2005]_ to calculate
the root mean square distance (RMSD) between two coordinate sets (as
implemented in

When using this module in published work please cite [Theobald2005]_.

See Also
   aligning structures based on RMSD
   implements the fast RMSD algorithm.

Example applications

Calculating RMSD for multiple domains

In this example we will globally fit a protein to a reference
structure and investigate the relative movements of domains by
computing the RMSD of the domains to the reference. The example is a
DIMS trajectory of adenylate kinase, which samples a large
closed-to-open transition. The protein consists of the CORE, LID, and
NMP domain.

* superimpose on the closed structure (frame 0 of the trajectory),
  using backbone atoms

* calculate the backbone RMSD and RMSD for CORE, LID, NMP (backbone atoms)

The trajectory is included with the test data files. The data in
:attr:`RMSD.results.rmsd` is plotted with :func:`matplotlib.pyplot.plot` (see Figure :ref:`RMSD plot figure <figure-RMSD>`)::

   import MDAnalysis
   from MDAnalysis.tests.datafiles import PSF,DCD,CRD
   u = MDAnalysis.Universe(PSF,DCD)
   ref = MDAnalysis.Universe(PSF,DCD)     # reference closed AdK (1AKE) (with the default ref_frame=0)
   #ref = MDAnalysis.Universe(PSF,CRD)    # reference open AdK (4AKE)

   import MDAnalysis.analysis.rms

   R = MDAnalysis.analysis.rms.RMSD(u, ref,
              select="backbone",             # superimpose on whole backbone of the whole protein
              groupselections=["backbone and (resid 1-29 or resid 60-121 or resid 160-214)",   # CORE
                               "backbone and resid 122-159",                                   # LID
                               "backbone and resid 30-59"])                                    # NMP

   import matplotlib.pyplot as plt
   rmsd = R.results.rmsd.T   # transpose makes it easier for plotting
   time = rmsd[1]
   fig = plt.figure(figsize=(4,4))
   ax = fig.add_subplot(111)
   ax.plot(time, rmsd[2], 'k-',  label="all")
   ax.plot(time, rmsd[3], 'k--', label="CORE")
   ax.plot(time, rmsd[4], 'r--', label="LID")
   ax.plot(time, rmsd[5], 'b--', label="NMP")
   ax.set_xlabel("time (ps)")
   ax.set_ylabel(r"RMSD ($\\AA$)")
.. _figure-RMSD:

.. figure:: /images/RSMD_plot.png
      :scale: 50 %
      :alt: RMSD plot
      RMSD plot for backbone and CORE, LID, NMP domain of the protein.


.. autofunction:: rmsd

Analysis classes

.. autoclass:: RMSD

   .. attribute:: results.rmsd

       Contains the time series of the RMSD as an N×3 :class:`numpy.ndarray`
       array with content ``[[frame, time (ps), RMSD (A)], [...], ...]``.

       .. versionadded:: 2.0.0

   .. attribute:: rmsd

       Alias to the :attr:`results.rmsd` attribute.

       .. deprecated:: 2.0.0
          Will be removed in MDAnalysis 3.0.0. Please use :attr:`results.rmsd`

.. autoclass:: RMSF

   .. attribute:: results.rmsf

      Results are stored in this N-length :class:`numpy.ndarray` array,
      giving RMSFs for each of the given atoms.

      .. versionadded:: 2.0.0

   .. attribute:: rmsf

      Alias to the :attr:`results.rmsf` attribute.

      .. deprecated:: 2.0.0
         Will be removed in MDAnalysis 3.0.0. Please use :attr:`results.rmsf`

import numpy as np

import logging
import warnings

import MDAnalysis.lib.qcprot as qcp
from MDAnalysis.analysis.base import AnalysisBase
from MDAnalysis.exceptions import SelectionError, NoDataError
from MDAnalysis.lib.util import asiterable, iterable, get_weights

logger = logging.getLogger('MDAnalysis.analysis.rmsd')

[docs]def rmsd(a, b, weights=None, center=False, superposition=False): r"""Returns RMSD between two coordinate sets `a` and `b`. `a` and `b` are arrays of the coordinates of N atoms of shape :math:`N times 3` as generated by, e.g., :meth:`MDAnalysis.core.groups.AtomGroup.positions`. Note ---- If you use trajectory data from simulations performed under **periodic boundary conditions** then you *must make your molecules whole* before performing RMSD calculations so that the centers of mass of the mobile and reference structure are properly superimposed. Parameters ---------- a : array_like coordinates to align to `b` b : array_like coordinates to align to (same shape as `a`) weights : array_like (optional) 1D array with weights, use to compute weighted average center : bool (optional) subtract center of geometry before calculation. With weights given compute weighted average as center. superposition : bool (optional) perform a rotational and translational superposition with the fast QCP algorithm [Theobald2005]_ before calculating the RMSD; implies ``center=True``. Returns ------- rmsd : float RMSD between `a` and `b` Notes ----- The RMSD :math:`\rho(t)` as a function of time is calculated as .. math:: \rho(t) = \sqrt{\frac{1}{N} \sum_{i=1}^N w_i \left(\mathbf{x}_i(t) - \mathbf{x}_i^{\text{ref}}\right)^2} It is the Euclidean distance in configuration space of the current configuration (possibly after optimal translation and rotation) from a reference configuration divided by :math:`1/\sqrt{N}` where :math:`N` is the number of coordinates. The weights :math:`w_i` are calculated from the input weights `weights` :math:`w'_i` as relative to the mean: .. math:: w_i = \frac{w'_i}{\langle w' \rangle} Example ------- >>> u = Universe(PSF,DCD) >>> bb = u.select_atoms('backbone') >>> A = bb.positions.copy() # coordinates of first frame >>> u.trajectory[-1] # forward to last frame >>> B = bb.positions.copy() # coordinates of last frame >>> rmsd(A, B, center=True) 3.9482355416565049 .. versionchanged: 0.8.1 *center* keyword added .. versionchanged: 0.14.0 *superposition* keyword added """ a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) N = b.shape[0] if a.shape != b.shape: raise ValueError('a and b must have same shape') # superposition only works if structures are centered if center or superposition: # make copies (do not change the user data!) # weights=None is equivalent to all weights 1 a = a - np.average(a, axis=0, weights=weights) b = b - np.average(b, axis=0, weights=weights) if weights is not None: if len(weights) != len(a): raise ValueError('weights must have same length as a and b') # weights are constructed as relative to the mean weights = np.asarray(weights, dtype=np.float64) / np.mean(weights) if superposition: return qcp.CalcRMSDRotationalMatrix(a, b, N, None, weights) else: if weights is not None: return np.sqrt(np.sum(weights[:, np.newaxis] * ((a - b) ** 2)) / N) else: return np.sqrt(np.sum((a - b) ** 2) / N)
def process_selection(select): """Return a canonical selection dictionary. Parameters ---------- select : str or tuple or dict - `str` -> Any valid string selection - `dict` -> ``{'mobile':sel1, 'reference':sel2}`` - `tuple` -> ``(sel1, sel2)`` Returns ------- dict selections for 'reference' and 'mobile'. Values are guarenteed to be iterable (so that one can provide selections to retain order) Notes ----- The dictionary input for `select` can be generated by :func:`fasta2select` based on a ClustalW_ or STAMP_ sequence alignment. """ if isinstance(select, str): select = {'reference': str(select), 'mobile': str(select)} elif type(select) is tuple: try: select = {'mobile': select[0], 'reference': select[1]} except IndexError: raise IndexError( "select must contain two selection strings " "(reference, mobile)") from None elif type(select) is dict: # compatability hack to use new nomenclature try: select['mobile'] select['reference'] except KeyError: raise KeyError( "select dictionary must contain entries for keys " "'mobile' and 'reference'." ) from None else: raise TypeError("'select' must be either a string, 2-tuple, or dict") select['mobile'] = asiterable(select['mobile']) select['reference'] = asiterable(select['reference']) return select
[docs]class RMSD(AnalysisBase): r"""Class to perform RMSD analysis on a trajectory. The RMSD will be computed for two groups of atoms and all frames in the trajectory belonging to `atomgroup`. The groups of atoms are obtained by applying the selection selection `select` to the changing `atomgroup` and the fixed `reference`. Note ---- If you use trajectory data from simulations performed under **periodic boundary conditions** then you *must make your molecules whole* before performing RMSD calculations so that the centers of mass of the selected and reference structure are properly superimposed. Run the analysis with :meth:``, which stores the results in the array :attr:`RMSD.results.rmsd`. .. versionchanged:: 1.0.0 ``save()`` method was removed, use ``np.savetxt()`` on :attr:`RMSD.results.rmsd` instead. .. versionchanged:: 2.0.0 :attr:`rmsd` results are now stored in a :class:`MDAnalysis.analysis.base.Results` instance. """ def __init__(self, atomgroup, reference=None, select='all', groupselections=None, weights=None, weights_groupselections=False, tol_mass=0.1, ref_frame=0, **kwargs): r"""Parameters ---------- atomgroup : AtomGroup or Universe Group of atoms for which the RMSD is calculated. If a trajectory is associated with the atoms then the computation iterates over the trajectory. reference : AtomGroup or Universe (optional) Group of reference atoms; if ``None`` then the current frame of `atomgroup` is used. select : str or dict or tuple (optional) The selection to operate on; can be one of: 1. any valid selection string for :meth:`~MDAnalysis.core.groups.AtomGroup.select_atoms` that produces identical selections in `atomgroup` and `reference`; or 2. a dictionary ``{'mobile': sel1, 'reference': sel2}`` where *sel1* and *sel2* are valid selection strings that are applied to `atomgroup` and `reference` respectively (the :func:`MDAnalysis.analysis.align.fasta2select` function returns such a dictionary based on a ClustalW_ or STAMP_ sequence alignment); or 3. a tuple ``(sel1, sel2)`` When using 2. or 3. with *sel1* and *sel2* then these selection strings are applied to `atomgroup` and `reference` respectively and should generate *groups of equivalent atoms*. *sel1* and *sel2* can each also be a *list of selection strings* to generate a :class:`~MDAnalysis.core.groups.AtomGroup` with defined atom order as described under :ref:`ordered-selections-label`). groupselections : list (optional) A list of selections as described for `select`, with the difference that these selections are *always applied to the full universes*, i.e., ``atomgroup.universe.select_atoms(sel1)`` and ``reference.universe.select_atoms(sel2)``. Each selection describes additional RMSDs to be computed *after the structures have been superimposed* according to `select`. No additional fitting is performed.The output contains one additional column for each selection. .. Note:: Experimental feature. Only limited error checking implemented. weights : {"mass", ``None``} or array_like (optional) 1. "mass" will use masses as weights for both `select` and `groupselections`. 2. ``None`` will weigh each atom equally for both `select` and `groupselections`. 3. If 1D float array of the same length as `atomgroup` is provided, use each element of the `array_like` as a weight for the corresponding atom in `select`, and assumes ``None`` for `groupselections`. weights_groupselections : False or list of {"mass", ``None`` or array_like} (optional) 1. ``False`` will apply imposed weights to `groupselections` from ``weights`` option if ``weights`` is either ``"mass"`` or ``None``. Otherwise will assume a list of length equal to length of `groupselections` filled with ``None`` values. 2. A list of {"mass", ``None`` or array_like} with the length of `groupselections` will apply the weights to `groupselections` correspondingly. tol_mass : float (optional) Reject match if the atomic masses for matched atoms differ by more than `tol_mass`. ref_frame : int (optional) frame index to select frame from `reference` verbose : bool (optional) Show detailed progress of the calculation if set to ``True``; the default is ``False``. Raises ------ SelectionError If the selections from `atomgroup` and `reference` do not match. TypeError If `weights` or `weights_groupselections` is not of the appropriate type; see also :func:`MDAnalysis.lib.util.get_weights` ValueError If `weights` are not compatible with `atomgroup` (not the same length) or if it is not a 1D array (see :func:`MDAnalysis.lib.util.get_weights`). A :exc:`ValueError` is also raised if the length of `weights_groupselections` are not compatible with `groupselections`. Notes ----- The root mean square deviation :math:`\rho(t)` of a group of :math:`N` atoms relative to a reference structure as a function of time is calculated as .. math:: \rho(t) = \sqrt{\frac{1}{N} \sum_{i=1}^N w_i \left(\mathbf{x}_i(t) - \mathbf{x}_i^{\text{ref}}\right)^2} The weights :math:`w_i` are calculated from the input weights `weights` :math:`w'_i` as relative to the mean of the input weights: .. math:: w_i = \frac{w'_i}{\langle w' \rangle} The selected coordinates from `atomgroup` are optimally superimposed (translation and rotation) on the `reference` coordinates at each time step as to minimize the RMSD. Douglas Theobald's fast QCP algorithm [Theobald2005]_ is used for the rotational superposition and to calculate the RMSD (see :mod:`MDAnalysis.lib.qcprot` for implementation details). The class runs various checks on the input to ensure that the two atom groups can be compared. This includes a comparison of atom masses (i.e., only the positions of atoms of the same mass will be considered to be correct for comparison). If masses should not be checked, just set `tol_mass` to a large value such as 1000. .. _ClustalW: .. _STAMP: See Also -------- rmsd .. versionadded:: 0.7.7 .. versionchanged:: 0.8 `groupselections` added .. versionchanged:: 0.16.0 Flexible weighting scheme with new `weights` keyword. .. deprecated:: 0.16.0 Instead of ``mass_weighted=True`` (removal in 0.17.0) use new ``weights='mass'``; refactored to fit with AnalysisBase API .. versionchanged:: 0.17.0 removed deprecated `mass_weighted` keyword; `groupselections` are *not* rotationally superimposed any more. .. versionchanged:: 1.0.0 `filename` keyword was removed. """ super(RMSD, self).__init__(atomgroup.universe.trajectory, **kwargs) self.atomgroup = atomgroup self.reference = reference if reference is not None else self.atomgroup select = process_selection(select) self.groupselections = ([process_selection(s) for s in groupselections] if groupselections is not None else []) self.weights = weights self.tol_mass = tol_mass self.ref_frame = ref_frame self.weights_groupselections = weights_groupselections self.ref_atoms = self.reference.select_atoms(*select['reference']) self.mobile_atoms = self.atomgroup.select_atoms(*select['mobile']) if len(self.ref_atoms) != len(self.mobile_atoms): err = ("Reference and trajectory atom selections do " "not contain the same number of atoms: " "N_ref={0:d}, N_traj={1:d}".format(self.ref_atoms.n_atoms, self.mobile_atoms.n_atoms)) logger.exception(err) raise SelectionError(err)"RMS calculation " "for {0:d} atoms.".format(len(self.ref_atoms))) mass_mismatches = (np.absolute((self.ref_atoms.masses - self.mobile_atoms.masses)) > self.tol_mass) if np.any(mass_mismatches): # diagnostic output: logger.error("Atoms: reference | mobile") for ar, at in zip(self.ref_atoms, self.mobile_atoms): if != logger.error("{0!s:>4} {1:3d} {2!s:>3} {3!s:>3} {4:6.3f}" "| {5!s:>4} {6:3d} {7!s:>3} {8!s:>3}" "{9:6.3f}".format(ar.segid, ar.resid, ar.resname,, ar.mass, at.segid, at.resid, at.resname,, at.mass)) errmsg = ("Inconsistent selections, masses differ by more than" "{0:f}; mis-matching atoms" "are shown above.".format(self.tol_mass)) logger.error(errmsg) raise SelectionError(errmsg) del mass_mismatches # TODO: # - make a group comparison a class that contains the checks above # - use this class for the *select* group and the additional # *groupselections* groups each a dict with reference/mobile self._groupselections_atoms = [ { 'reference': self.reference.universe.select_atoms(*s['reference']), 'mobile': self.atomgroup.universe.select_atoms(*s['mobile']), } for s in self.groupselections] # sanity check for igroup, (sel, atoms) in enumerate(zip(self.groupselections, self._groupselections_atoms)): if len(atoms['mobile']) != len(atoms['reference']): logger.exception('SelectionError: Group Selection') raise SelectionError( "Group selection {0}: {1} | {2}: Reference and trajectory " "atom selections do not contain the same number of atoms: " "N_ref={3}, N_traj={4}".format( igroup, sel['reference'], sel['mobile'], len(atoms['reference']), len(atoms['mobile']))) # check weights type acceptable_dtypes = (np.dtype('float64'), np.dtype('int64')) msg = ("weights should only be 'mass', None or 1D float array." "For weights on groupselections, " "use **weight_groupselections**") if iterable(self.weights): element_lens = [] for element in self.weights: if iterable(element): element_lens.append(len(element)) else: element_lens.append(1) if np.unique(element_lens).size > 1: # jagged data structure raise TypeError(msg) if np.array(element).dtype not in acceptable_dtypes: raise TypeError(msg) if iterable(self.weights) or self.weights != "mass": get_weights(self.mobile_atoms, self.weights) if self.weights_groupselections: if len(self.weights_groupselections) != len(self.groupselections): raise ValueError("Length of weights_groupselections is not equal to " "length of groupselections ") for weights, atoms, selection in zip(self.weights_groupselections, self._groupselections_atoms, self.groupselections): try: if iterable(weights) or weights != "mass": get_weights(atoms['mobile'], weights) except Exception as e: raise type(e)(str(e) + ' happens in selection %s' % selection['mobile']) def _prepare(self): self._n_atoms = self.mobile_atoms.n_atoms if not self.weights_groupselections: if not iterable(self.weights): # apply 'mass' or 'None' to groupselections self.weights_groupselections = [self.weights] * len(self.groupselections) else: self.weights_groupselections = [None] * len(self.groupselections) for igroup, (weights, atoms) in enumerate(zip(self.weights_groupselections, self._groupselections_atoms)): if str(weights) == 'mass': self.weights_groupselections[igroup] = atoms['mobile'].masses if weights is not None: self.weights_groupselections[igroup] = np.asarray(self.weights_groupselections[igroup], dtype=np.float64) / \ np.mean(self.weights_groupselections[igroup]) # add the array of weights to weights_select self.weights_select = get_weights(self.mobile_atoms, self.weights) self.weights_ref = get_weights(self.ref_atoms, self.weights) if self.weights_select is not None: self.weights_select = np.asarray(self.weights_select, dtype=np.float64) / \ np.mean(self.weights_select) self.weights_ref = np.asarray(self.weights_ref, dtype=np.float64) / \ np.mean(self.weights_ref) current_frame = self.reference.universe.trajectory.ts.frame try: # Move to the ref_frame # (coordinates MUST be stored in case the ref traj is advanced # elsewhere or if ref == mobile universe) self.reference.universe.trajectory[self.ref_frame] self._ref_com = # makes a copy self._ref_coordinates = self.ref_atoms.positions - self._ref_com if self._groupselections_atoms: self._groupselections_ref_coords64 = [(self.reference. select_atoms(*s['reference']). positions.astype(np.float64)) for s in self.groupselections] finally: # Move back to the original frame self.reference.universe.trajectory[current_frame] self._ref_coordinates64 = self._ref_coordinates.astype(np.float64) if self._groupselections_atoms: # Only carry out a rotation if we want to calculate secondary # RMSDs. # R: rotation matrix that aligns r-r_com, x~-x~com # (x~: selected coordinates, x: all coordinates) # Final transformed traj coordinates: x' = (x-x~_com)*R + ref_com self._rot = np.zeros(9, dtype=np.float64) # allocate space self._R = self._rot.reshape(3, 3) else: self._rot = None self.results.rmsd = np.zeros((self.n_frames, 3 + len(self._groupselections_atoms))) self._mobile_coordinates64 = self.mobile_atoms.positions.copy().astype(np.float64) def _single_frame(self): mobile_com = self._mobile_coordinates64[:] = self.mobile_atoms.positions self._mobile_coordinates64 -= mobile_com self.results.rmsd[self._frame_index, :2] = self._ts.frame, self._trajectory.time if self._groupselections_atoms: # superimpose structures: MDAnalysis qcprot needs Nx3 coordinate # array with float64 datatype (float32 leads to errors up to 1e-3 in # RMSD). Note that R is defined in such a way that it acts **to the # left** so that we can easily use broadcasting and save one # expensive numpy transposition. self.results.rmsd[self._frame_index, 2] = qcp.CalcRMSDRotationalMatrix( self._ref_coordinates64, self._mobile_coordinates64, self._n_atoms, self._rot, self.weights_select) self._R[:, :] = self._rot.reshape(3, 3) # Transform each atom in the trajectory (use inplace ops to # avoid copying arrays) (Marginally (~3%) faster than # "ts.positions[:] = (ts.positions - x_com) * R + ref_com".) self._ts.positions[:] -= mobile_com # R acts to the left & is broadcasted N times. self._ts.positions[:] =, self._R) self._ts.positions += self._ref_com # 2) calculate secondary RMSDs (without any further # superposition) for igroup, (refpos, atoms) in enumerate( zip(self._groupselections_ref_coords64, self._groupselections_atoms), 3): self.results.rmsd[self._frame_index, igroup] = rmsd( refpos, atoms['mobile'].positions, weights=self.weights_groupselections[igroup-3], center=False, superposition=False) else: # only calculate RMSD by setting the Rmatrix to None (no need # to carry out the rotation as we already get the optimum RMSD) self.results.rmsd[self._frame_index, 2] = qcp.CalcRMSDRotationalMatrix( self._ref_coordinates64, self._mobile_coordinates64, self._n_atoms, None, self.weights_select) @property def rmsd(self): wmsg = ("The `rmsd` attribute was deprecated in MDAnalysis 2.0.0 and " "will be removed in MDAnalysis 3.0.0. Please use " "`results.rmsd` instead.") warnings.warn(wmsg, DeprecationWarning) return self.results.rmsd
[docs]class RMSF(AnalysisBase): r"""Calculate RMSF of given atoms across a trajectory. Note ---- No RMSD-superposition is performed; it is assumed that the user is providing a trajectory where the protein of interest has been structurally aligned to a reference structure (see the Examples section below). The protein also has be whole because periodic boundaries are not taken into account. Run the analysis with :meth:``, which stores the results in the array :attr:`RMSF.results.rmsf`. """ def __init__(self, atomgroup, **kwargs): r"""Parameters ---------- atomgroup : AtomGroup Atoms for which RMSF is calculated verbose : bool (optional) Show detailed progress of the calculation if set to ``True``; the default is ``False``. Raises ------ ValueError raised if negative values are calculated, which indicates that a numerical overflow or underflow occured Notes ----- The root mean square fluctuation of an atom :math:`i` is computed as the time average .. math:: \rho_i = \sqrt{\left\langle (\mathbf{x}_i - \langle\mathbf{x}_i\rangle)^2 \right\rangle} No mass weighting is performed. This method implements an algorithm for computing sums of squares while avoiding overflows and underflows [Welford1962]_. Examples -------- In this example we calculate the residue RMSF fluctuations by analyzing the :math:`\text{C}_\alpha` atoms. First we need to fit the trajectory to the average structure as a reference. That requires calculating the average structure first. Because we need to analyze and manipulate the same trajectory multiple times, we are going to load it into memory using the :mod:`~MDAnalysis.coordinates.MemoryReader`. (If your trajectory does not fit into memory, you will need to :ref:`write out intermediate trajectories <writing-trajectories>` to disk or :ref:`generate an in-memory universe <creating-in-memory-trajectory-label>` that only contains, say, the protein):: import MDAnalysis as mda from MDAnalysis.analysis import align from MDAnalysis.tests.datafiles import TPR, XTC u = mda.Universe(TPR, XTC, in_memory=True) protein = u.select_atoms("protein") # 1) the current trajectory contains a protein split across # periodic boundaries, so we first make the protein whole and # center it in the box using on-the-fly transformations import MDAnalysis.transformations as trans not_protein = u.select_atoms('not protein') transforms = [trans.unwrap(protein), trans.center_in_box(protein, wrap=True), trans.wrap(not_protein)] u.trajectory.add_transformations(*transforms) # 2) fit to the initial frame to get a better average structure # (the trajectory is changed in memory) prealigner = align.AlignTraj(u, u, select="protein and name CA", in_memory=True).run() # 3) reference = average structure ref_coordinates = u.trajectory.timeseries(asel=protein).mean(axis=1) # make a reference structure (need to reshape into a 1-frame # "trajectory") reference = mda.Merge(protein).load_new(ref_coordinates[:, None, :], order="afc") We created a new universe ``reference`` that contains a single frame with the averaged coordinates of the protein. Now we need to fit the whole trajectory to the reference by minimizing the RMSD. We use :class:`MDAnalysis.analysis.align.AlignTraj`:: aligner = align.AlignTraj(u, reference, select="protein and name CA", in_memory=True).run() The trajectory is now fitted to the reference (the RMSD is stored as `aligner.results.rmsd` for further inspection). Now we can calculate the RMSF:: from MDAnalysis.analysis.rms import RMSF calphas = protein.select_atoms("name CA") rmsfer = RMSF(calphas, verbose=True).run() and plot:: import matplotlib.pyplot as plt plt.plot(calphas.resnums, rmsfer.results.rmsf) References ---------- .. [Welford1962] B. P. Welford (1962). "Note on a Method for Calculating Corrected Sums of Squares and Products." Technometrics 4(3):419-420. .. versionadded:: 0.11.0 .. versionchanged:: 0.16.0 refactored to fit with AnalysisBase API .. deprecated:: 0.16.0 the keyword argument `quiet` is deprecated in favor of `verbose`. .. versionchanged:: 0.17.0 removed unused keyword `weights` .. versionchanged:: 1.0.0 Support for the ``start``, ``stop``, and ``step`` keywords has been removed. These should instead be passed to :meth:``. """ super(RMSF, self).__init__(atomgroup.universe.trajectory, **kwargs) self.atomgroup = atomgroup def _prepare(self): self.sumsquares = np.zeros((self.atomgroup.n_atoms, 3)) self.mean = self.sumsquares.copy() def _single_frame(self): k = self._frame_index self.sumsquares += (k / (k+1.0)) * (self.atomgroup.positions - self.mean) ** 2 self.mean = (k * self.mean + self.atomgroup.positions) / (k + 1) def _conclude(self): k = self._frame_index self.results.rmsf = np.sqrt(self.sumsquares.sum(axis=1) / (k + 1)) if not (self.results.rmsf >= 0).all(): raise ValueError("Some RMSF values negative; overflow " + "or underflow occurred") @property def rmsf(self): wmsg = ("The `rmsf` attribute was deprecated in MDAnalysis 2.0.0 and " "will be removed in MDAnalysis 3.0.0. Please use " "`results.rmsd` instead.") warnings.warn(wmsg, DeprecationWarning) return self.results.rmsf