.. -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding: utf-8 -*-
.. vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4

Surface of a micelle
********************


Summary
-------
We use the :class:`~pytim.gitim.GITIM` class to investigate the surface composition of a DPC micelle

This tutorial shows how to do some basic statistical analysis and how to visualize
surface atoms / pockets for water.

The DPC micelle
---------------


The micelle example file :file:`MICELLE_PDB` consists of 65 DPC and 6305 water molecules,
for a total of about 20,000 atoms (D. P. Tieleman, D. van der Spoel, H.J.C.
Berendsen, J. Phys. Chem. B 104, pp. 6380-6388, 2000)

.. image:: micelle1.png
   :width: 35%
   :align: center

As this is not a planar interface, we will use the GITIM class to identify the atoms at the
surface of the micelle. Our reference group for the surface analysis includes therefore
all DPC molecules

    >>> import numpy as np
    >>> import MDAnalysis as mda
    >>> import pytim
    >>> from   pytim.datafiles import *
    >>> #
    >>> # this is a DPC micelle in water
    >>> u = mda.Universe(MICELLE_PDB)
    >>> #
    >>> # select all lipid molecues
    >>> g = u.select_atoms('resname DPC')


In order to calculate the surface atoms, we invoke :class:`~pytim.gitim.GITIM`, passing
the group `g` as the `group` option, and set `molecular=False` in order to mark
only the atoms at the surface, and not the whole residues to which these atoms belong.
We override the standard radii (from the gromos 43a1 forcefield) with a simple set that 
will match the atom types.

    >>> # define radii for atom types
    >>> rdict  = {'C':1.5, 'O':1.5, 'P':1.6, 'N':1.8}
    >>> # calculate the atoms at the interface ...
    >>> inter = pytim.GITIM(u, group=g, molecular=False, alpha=2.5, radii_dict=rdict )
    >>> #
    >>> # ... and write a pdb file (default:'layers.pdb') with layer information in the beta field
    >>> inter.writepdb()


The micelle with its surface atoms highlighted in purple looks like this (left: from outside; right: section through the middle)

+------------------------+------------------------+
|.. image:: micelle3.png |.. image:: micelle2.png |
|   :width: 70%          |   :width: 70%          |
|   :align: right        |   :align: left         |
+------------------------+------------------------+



Some statistics
---------------

It's easy to calculate some simple statistical properties. For example, the percentage of atoms of DPC at the surface is

    >>> print "percentage of atoms at the surface: {:.1f}".format(len(inter.layers[0])*100./len(g))
    percentage of atoms at the surface: 47.2

This is a rather high percentage, but is due to the small size of the micelle (large surface/volume ratio)

We can also easily find out which atom is more likely to be found at the surface:

    >>> # we cycle over the names of atoms in the first residue
    >>> for name in g.residues[0].atoms.names :
    ...     total   = np.sum(g.names==name)
    ...     surface = np.sum(inter.layers[0].names == name )
    ...     print('{:>4s} ---> {:>2.0f}%'.format(name, surface*100./total))
      C1 ---> 97%
      C2 ---> 95%
      C3 ---> 100%
      N4 ---> 98%
      C5 ---> 98%
      C6 ---> 92%
      O7 ---> 75%
      P8 --->  3%
      O9 ---> 72%
     O10 ---> 83%
     O11 ---> 48%
     C12 ---> 31%
     C13 ---> 29%
     C14 ---> 14%
     C15 ---> 17%
     C16 ---> 15%
     C17 ---> 18%
     C18 ---> 12%
     C19 ---> 18%
     C20 ---> 12%
     C21 ---> 17%
     C22 ---> 17%
     C23 ---> 22%



One immediately notices that with this choice of radii, P atoms are rarely at the surface: this is because they are always buried wihin their bonded neighbors in the headgroup.  Also, a non negligible part of the fatty tails are also found from time to time at the surface.


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