mdio 0.2.2rc1

Basic I/O for MD trajectory files

mdio

A Python library to read, write, and manipulate molecular dynamics (MD) trajectory files.

mdio is designed to provide basic MD file I/O capabilities. It's not supposed to replace great packages like mdtraj and mdanalysis, but is a lighter weight alternative when all you need is basic MD trajectory file I/O and nothing much more.

For example, the following script would read in a Gromacs .xtc format trajectory file, strip it of water molecules, correct molecule coordinates split up by periodic boundary condition artifacts, least-squares fit each snaphot to the first, and then write out the new coordinates in Amber netcdf (.nc) format:

import mdio

topfile = '../test/examples/test.gro'
trajfile = '../test/examples/test.xtc'
outfile = 'protein.nc'

traj = mdio.load(trajfile, top=topfile, selection='not water')
traj = traj.make_whole()
traj = traj.fitted_to(traj[0])
traj.save(outfile)

Installation

Easiest via pip. You need numpy and cython pre-installed:

% pip install numpy cython
% pip install mdio

User Guide

Loading data

mdio can load data from a variety of file formats: Gromacs (.xtc and .gro), NAMD/CHARMM (.dcd), AMBER (.nc, .mdcrd, .rst), and PDB (.pdb). mdio auto-detects file formats, so although it may be useful to use the standard file extensions, you don't have to - mdio will happily load an AMBER netcdf file that has the extension .traj (or even .xtc!).

t = mdio.load('../test/examples/test.nc')
print(t)

mdio.Trajectory with 10 frames, 892 atoms and box info.

Alternative ways of reading files are supported:

f = mdio.open('../test/examples/test.nc', top='../test/examples/test.pdb')
t2 = f.read()
f.close()
print(t2)

mdio.Trajectory with 10 frames, 892 atoms and box info.

Or using a context manager, and in a frame-by-frame way:

with mdio.open('../test/examples/test.dcd') as f:
    frames = []
    frame = f.read_frame()
    while frame is not None:
        frames.append(frame)
        frame = f.read_frame()
t3 = mdio.Trajectory(frames)
print(t3)

mdio.Trajectory with 10 frames, 892 atoms and box info.

You can create an mdio trajectory object from a suitably-shaped numpy array. mdio assumes the numbers in the array are in nanometers.

import numpy as np
xyz = np.random.random((120, 55, 3))
t4 = mdio.Trajectory(xyz)
print(t4)

mdio.Trajectory with 120 frames, and 55 atoms.

Saving data

Trajectory files can also be written in a variety of ways. The required format is inferred from the filename extension, so unlike for file reading, this must be appropriate.

# a) Using the save() method of a trajectory object:
t.save('test.nc')

# b) Using mopen():
with mdio.open('test2.dcd', "w") as f:
    f.write(t)

# c) Frame-by-frame:
f =  mdio.open('test3.xtc', "w")
for frame in t.frames():
    f.write_frame(frame)
f.close()

Manipulating trajectories

a) Frame-wise:

Trajectories can be sliced and concatenated/appended (if they are compatible):

t5 = t[2:9:3] + t2
t5 += t3
print(t5)

mdio.Trajectory with 23 frames, 892 atoms and box info.

b) Atom-wise:

Trajectories with selected subsets of atoms can be created. There are two methods for doing this. One is to specify a list of atom indices:

t6 = t.select([0, 1, 3, 5, 23, 34])
print(t6)

mdio.Trajectory with 10 frames, 6 atoms and box info.

The other is to specify a selection string, which uses a syntax very similar to that used by mdtraj, see here:

t7 = t2.select('name CA')
print(t7)

mdio.Trajectory with 10 frames, 58 atoms and box info.

c) Coordinate-wise:

It is not the purpose of mdio to provide a rich variety of trajectory analysis facilities, but a few common functions are implemented. Firstly coordinates can be least-squares fitted to reference structures and the fitting can be weighted:

# a) Simple fit
t8 = t.fitted_to(t[0])

# b) Mass-weighted fit of t2 to the 6th frame in trajectory t3:
weights = [atom.element.mass for atom in t2.topology.atoms]
t9 = t2.fitted_to(t3[5], weights=weights)

# c) Use just residue 1 for the fit:
weights = np.zeros(t2.n_atoms)
weights[t2.topology.select('residue 1')] = 1.0
t10 = t2.fitted_to(t3[5], weights=weights)

Secondly some PBC-related transformations are supported.

• packed_around() transforms coordinates so they lie within the unit cell whose centre would be the centre of geometry of the selected atoms.
• make_whole() corrects for molecules split by PBC imaging; this uses bond information generated from analysis of the topology file, so this needs to have 'good' geometry.

t11 = t2.packed_around('residue 3')
t12 = t11.make_whole()

Finally RMSDs can be calculated:

r2 = t2.rmsd_from(t3[6])
print(r2)

[0.09093863981724826, 0.07614511939046421, 0.07949020859896917, 0.07497944478603998, 0.06590847615210413, 0.051843638106744715, 5.960464477539063e-08, 0.07091305468398602, 0.07106069413686344, 0.08153553252567688]

NGLViewer compatibility

mdio.Trajectory objects look enough like the ones generated by MDTraj that they can be viewed in Jupyter notebooks with nglview using the show_mdtraj() function.

Author:

Charlie Laughton [e-mail]

License:

BSD 3-clause