Utility functions and modules for manipulating molecules for molecular dynamics simulations.
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coord_util is a library for reading, manipulating and writing molecule geometries. coord_util can read and write geometries in several common formats.


coord_util depends on my tempfile_util module.

coord_util also depends on numpy (tested with 1.6.1).

coord_util contains a module for storing geometries in a useful database format, trajdb. In order to make use of this module, coord_util depends on the presence of sqlite3 and h5py.

Scripts for building all of the above dependencies in the user's home directory (and thus not requring root permissions) are available in my build_scripts repository.



Clone coord_util into your python site-packages directory.

      cd your_python_site-packages
      git clone git://github.com/plediii/coord_util

Configure and Build

This step is optional but recommended; all of the coord_util functions are pure python. However, some functions have been implemented more efficiently in Fortran. To access the more efficient versions configure and build the moules via:



The coord_util package includes several test suites with names in the form test_foo.py. To execute all of them:




The coord_math module exposes several geometric functions which interpret one-dimensional numpy arrays holding contiguous sequence of atoms. The arguments to the coord_math functions assume that the numpy arrays are in the order:

      x = np.array([x1, y1, z1, x2, y2, z2, ..., xn, yn, zn]),

where x1 is the x coordinate of the first atom, y2 is the y coordinate of the second atom, and zn is the z coordinate of the nth atom. The atom index in the following functions is 1-based, so that the first atom has index 1.


get_atom_coords returns a 1x3 array containing the coordinates of the ith atom.

		get_atom_coords(np.array([1, 2, 3, 4, 5, 6]), 2) == np.array([4, 5, 6])

center_of_geometry returns a 1x3 array containing the coordinates at the average of the atom coordinates in the system.

For instance, the center of geometry of atoms arranged at the corners of a square is the center of the square:

    center_of_geometry(np.array([0., 0., 0., 
                                 1., 0., 0.,
	                         0., 1., 0.,
	                         1., 1., 0.,]))
	                         == np.array([0.5, 0.5, 0.0])

rmsd is a slight misnomer, since rmsd is the least root mean square distance between two geometries. However, the convention is to simply refer to this measure as rmsd. The non least root mean square distance between two geometries is simply the l2-norm of the difference vector, divided by the square root of the number of atoms:

       flat_rmsd(v1, v2) == sqrt(dot(v1-v2, v1-v2)/(len(v1)/3)).

The least root mean square is the least root mean square between the two vectors, amongst all possible rotations and translations of the geometry. The intuition for using the least rmsd is that, for example, changes in a water molecule geometry by rotation or translation are irrelevant to its dynamics.

Since rmsd gives the least root mean square distance, we have, for example, that

	rmsd(v1, v2) == rmsd(v1, translate(v2, anything))


	rmsd(v1, v2) == rmsd(v1, rotate(v2, anyangle)).

align transforms a geometry to minimize the flat_rmsd to another target geometry. Explicitly:

       rmsd(v1, v2) == flat_rmsd(v1, align(v1, v2)).

Geometries can be aligned considering only subset of the coordinates using the subalign function and the topology module described below.


translate displaces all atoms in a geometry by a uniform vector; for example, if we translate a geometry via,

	    translate(geom, delta_vector).

Then, for any atom in the geometry

    get_atom_coords(geom, idx) - get_atom_coords(translate(geom, delta_vector)), idx) == delta_vector.

dihedral calculates the dihedral angle between involving for atoms. Example:

	   dihedral(geom, idx, jdx, kdx, ldx)

atom_dist calculates the distance between the idxth and jdxth atoms. Example:

	    atom_dist(geom, idx, jdx).

rotate_euler rotates the geometry about the origin according to the euler angles. Example:

	       rotate_euler(geom, alpha, beta, gamma).

transform applies a 3x3 transformation matrix to each atom in the geometry. Example:

	    transform(geom, euler_rotation_matrix(alpha, beta, gamma)).


The topology module exposes classes and functions for selecting and manipulating components of geometries, especially residues of proteins. The inspiration for the design of this module came from the pattern used by jQuery for selecting and manipulating components of the DOM.

The need for this module in my own projects arose in the context of monte-carlo rotamer switching in protein dynamics. Rotamer switching refers to changing the geometry of a single amino acid side chain in a protein between different stable formations separated by energetic frustration. Monte-Carlo rotamer switching allows for rapid exploration of a protein's dynamical space without requiring the time normally required for transitions within the residues.

The usefulness of the topology module goes beyond just rotamer switching. When coupled with coordinates describing a protein geometry, it can be used to extract specific atoms, or switch the atom order in a clear way.

Use of the topology module centers around the Topology class. Instances of the topology classes can be read by reading the topology of a PDB file, or by creating them manually.

Creating topology instances

For example, to obtain the topology describing the model in "protein.pdb":

    import coord_util.pdb as p
    top = p.read_topology('protein.pdb')

Alternatively, specific topologies can be constructed manually. For instance, we can construct a toplogy for a glycine residue, or a water molecule:

	import topology as t

	gly = t.Molecule('GLY', ['N', 'CA', 'C', 'O'])
	water = t.Molecule('HOH', ['H', 'O', 'H'])

Individual molecules can be composed to create polymers. A glycine dipeptide can be constructed via:

	   digly = t.Polymer('GLYGLY', [gly, gly])

Polymers can be composed with water molecules to create a dipeptide solvated in 100 water molecules:

	 system = t.Polymer('solvagted gly', [digly] + [water] * 100)

Alternatively, chains can be constructed by creating a monomer set, and using its sequence method.

	ala = t.Molecule('ALA', ['N', 'CA', 'CB', 'C', 'O'])

	  monomers = t.Monomers([gly, ala])

	  gly_ala_gly = t.Polymer('GLYALAGLY', monomers.sequence(['GLY', 'ALA', 'GLY']))

The coord_util project includes a module aminoacids with the standard set of aminoacids:

    from aminoacids import aminoacids

    gly_ala_gly = aminoacids.sequence(['ALA', 'GLY', 'ALA'])

Selecting and extracting substructures

A Topology instance, when coupled with the coordinates in the PDB file, allows us to select specific components.

Suppose we already have the topology in "protein.pdb", "top" from above. The trajectory of geometries in the PDB file can be obtained via:

	   xs = list(p.read_coords('protein.pdb'))

The coordinates of specific components of the geometry can be extracted by coupling a topology instance with the coordinate described. For instance, to extract only the CA atoms in the protein, we can use the Topolgy.get_atoms method to create a subtopology, and Topology.get_coords to extract the coordinates for those atoms from a numpy array:

      ca_top = top.get_atoms('CA')
      ca_xs = [ca_top.get_coords(x) for x in xs]

After the above, assuming the protein consists of N residues with CA atoms, each of the ca_xs are numpy arrays of length 3N containing the coordinates of the CA atoms.

Alternatively, we could extract just the backbone of the geometry using the Topology.get_atomset method:

	       backbone_top = top.get_atomset(['CA', 'CB', 'C', 'N', 'O'])

Atoms can also be selected with regular expressions (regex). We could have selected the backbone atoms using a regex instead:

	       backbone_top = top.regex_get_atoms('^(CA|CB|C|N|O)$')

We could also select for specific monomers by name using Polymer.get_monomer(resname), Polymer.monomers_slice(idx, jdx) and Polymer.get_monomer_by_index(idx, jdx).

Replacing substructures

Besides extracting substructures from a geometry, replacing those substructures into a geometry is also useful. In the case of the rotamer switching problem, for instance, we want to change the coordinates of specific side chains in the protein. This operation is implemented by the Topology.set_coords method.

For instance, suppose we have the subtopology for the first monomer in a Polymer:

    first = top.get_monomer_by_index(0).

If x is an numpy array of length 3N (corresponding to the N atoms described by top), and x1 is a numpy array of length 3M (corresponding to the M atoms of the first residue), we can change the geometry of the first residue to that of x1 by

	 first.set_coords(x, x1).

Lifting and reordering atoms

When building a geometry from scratch from its constituent substructures, a common operation is to build a new array for the entire geometry, and fill in each of the substructures using subtopologies For instance, suppose we are beginning to reconstruct a geometry from its CA atoms. We can create a numpy array for the entire geometry, with the CA atoms already filled in using Topology.lift_coords like so:

		       lift_ca = top.lift_coords(top.get_atoms('CA'))
		       full_x = lift_ca.lift_coords(ca_x)

Then, assuming ca_x is a numpy array of length 3M corresponding to the M CA atoms, full_x will be a numpy array of length 3N corresponding to the N atoms in the complete geometry, with the CA coordinates fill in, and the coordinates for the remaining atoms set to zero.

A related situation is when we have coordinates for a molecule with atoms in a different order. Suppose top2 contains a topology for the same molecule as top, except that the atom names are in a different order. Additionally, suppose x2 contains a geometry for the atoms in the order of top2. Then we can change order of the atoms to that of top via:

      top_lift_top2 = top.lift_topology(top2, reorder=True)
      x = top_lift_top2.lift_coords(x2)

The extra argument to lift_topology, reorder, is not strictly necessary, but it dictates that lift_topology should raise an exception if the topology to be lifted does not contain the same number of atoms.

Topology lifting considers only the names of atoms in molecules, so it can be used between Polymers with different names as long as the Molecules in the corresponding positions in the Polymer tree can be lifted to one another.

Aligning sub geometries

A common procedure is to align a geometry so that a sub component is optimally aligned with the same component of another geometry. This can be accomplished with the subalign function in the coord_math module. For example, to align protein coordinates y to protein coordinates x along their backbone:

	    backbone_top = top.regex_get_atoms('^(CA|CB|C|N|O)$')
	    aligned_y = subalign(backbone_top, x, y)


The coord_util package includes reader modules for several coordinate formats. These reader modules provide an open function returning an iterator over the geometries in the file.

AMBER Formats


The mdcrd format requires the number of atoms in the geometry.

    import coord_util.mdcrd as mdcrd

    with mdcrd.open('ala.crd', num_atoms=22) as f:
        for geom in f:
	    print center_of_geometry(geom)



	import coord_util.rst as rst

	with rst.open('ala.rst') as f:
	     for geom in f:
	       	 print center_of_geometry(geom)

PDB Format


    import coord_util.pdb as pdb

    with pdb.open('protein.pdb') as f:
        for geom in f:
	    print center_of_geometry(geom)

Gromacs Format


The Gro format specifies coordinates in nanometers, but coord_util converts them to angstroms for consistency with the other formats.


    import coord_util.gro as gro

    with gro.open('ala.gro') as f:
        for geom in f:
	    print center_of_geometry(geom)


coord_util includes a module for storing molecular dynamics trajectories in a convenient database format. The purpose of trajdb is to associate snapshots molecular dynamics trajectories with a collection of all relevant real valued physical properties in an efficient and easily queried pair of files. A trajdb consists of an sqlite and hdf5 database comprising the trajectories and physical properties.

This module requires sqlite3 and h5py python modules.

Opening and creating trajdbs

Opening an existing trajdb

An existing trajdb named mytraj.db can be opened via:

	import coord_util.trajdb as trajdb

	db = trajdb.open_trajectory_database('mytraj.db', create=False)

The optional create argument being set to False will cause an exception to be raised if the file does not exist.

Creating a new trajdb

A new trajdb named mytraj.db for geometries with ndof degrees of freedom (e.g., water molecules have 3*3=6 degrees of freedom) can be created via:

	import coord_util.trajdb as trajdb

	db = trajdb.open_trajectory_database('mytraj.db', ndof=ndof, create=True)

The optional create argument being set to True will cause trajdb to overwrite any previously existing database.

The ndof argument is required when creating a new database.

Adding and retrieving samples

New trajectory insertion

Suppose we have an iterator traj_iter, which yields numpy vectors of molecule geometries representing snapshots from a trajectory with timestep dt (with initial time 0). This sequence can be inserted into the trajdb stored referenced by db via:

  with db.session():
    for x in traj_iter:
      samplekey = db.new_sample(x)


The samplekey returned by new_sample is a unique identifier representing the geometry in the database.

The session ensures that the SQL database is only modified if all of the samles are successfully inserted. The close function ensures that the samples are written to the hdf5 database.

Multiple trajectory insertion

It is possible to denote that samples in the trajdb come from multiple independent trajectories by using the new_trajectory and switch_trajectory functions. Suppose traj_iters is an iterator of trajectory iterators. Then the following will insert multiple trajectories each with initial time 0 and time step dt.

  with db.session():
    for traj_iter in traj_iters:
      trajkey = db.new_trajectory()
      for x in traj_iter:
        samplekey = db.new_sample(x)


The trajkey returned from new_trajectory is a unique identifier representing the trajectory in the database.

Alternatively, a previous inserted trajectory can be "resumed" by calling db.switch_trajectory with an existing trajkey.

Sample retrieval

Geometries inserted into a trajdb can be retrieved as long as the hdf5 database is present, and the samplekey of the desired geometry is known. The geometry associated with a given samplekey can be retrieved via get_sample such as:

  x = db.get_sample(samplekey)

Then x will be a numpy array holding the geometry of the samplekeyth geometry. All the samplekeys for the database can be retrieved by the select function (described in more detail below):

  for samplekey in db.select([db.samplekeys.samplekey]):
    x = db.get_sample(samplekey)
    print 'Sample %s is %s % (samplekey, x)

To simply iterate over the trajectories (in samplekey order), use the db.iter_coordinates function. The previous example is equivalent to the following:

  for samplekey, x in db.iter_coordinates():
    print 'Sample %s is %s % (samplekey, x)

Adding physical properties

In the most common case, molecule samples are described through a smaller set (i.e., smaller than the number of degrees of freedom) of real valued functions of the complete geometry. Examples of such functions are dihedral angles, bond lengths, or rmsd from a specific geometry.

The trajdb module provides functions to associate real valued properties to the samplekeys describing the geometries in the database, and to query the database for based on these properties.

The physical properties for a trajdb can be queried without the presence of the (often much larger) full geometries (in the hdf5 database).

Adding new physical properties

Suppose we want to describe molecules by the dihedral involving atoms 1, 2, 3 and 4; named psi. This attribute can be done in the following way:

  def psis():

    for samplekey, x in db.iter_coordinates():
      yield samplekey, dihedral(x, 1, 2, 3, 4)

  # the above adds the attribute psi to `db`

  db.insert(db.psi, psis())

Retrieving physical properties

By default, time and trajectory keys are associated with each sample. We can select these values, along with any additional properties in the following way:

   for row in db.select([db.trajectories.trajectorykey, db.times.time, db.psis.value]):
     print 'Trajectory %d at time %f had psi=%f' % row

Selecting properties by range

To restrict the samples selected, the select function includes a where keyword:

   for row in db.select([db.trajectories.trajectorykey, db.times.time], 
     print 'Trajectory %d at time %f had psi between 0 and pi/2' % row


The coord_util module includes efficient functions for searching for nearest neighbors in a set of numpy arrays. The code is the Geometric Near-neighbor Access Tree (GNAT), and closely follows the strategy outlined by Brin (infolab.standford.edu/~sergey/near.html).

'Nearest' in this context refers to any metric, but the gnat code defaults to rmsd.

This GNAT library is designed to work with a trajdb, but can easily be freed from this constraint (see test_gnat.py).


Assuming db is a tradb, we can build a GNAT indexing the structures via

  import coord_util.gnat as g

  gnat = g.build_gnat(db)


Given a sample x, the samplekeys of all neighbors closer than r according to the metric can be obtained using the query function.

  for samplekey in gnat.query(x, r):
    print 'Sample %d is closer than %f' % (samplekey, r)

The nearest neighbor can be identified by the neighbor_query function:

  print 'The nearest neighbor is sample %d' % (g.neighbor_query(x)[0])


A GNAT would not be very useful if we had to recreate the structure everytime we wanted to do queries. The GNAT can be saved to the trajdb by using the gnat module's save_gnat function.


The GNAT stored in the trajdb can be restored via the load_gnat function.

  restored_gnat = g.load_gnat(db)