Mostly complete tutorial - needs rgyr section

doc/source/tutorial.rst

@@ -2,7 +2,8 @@ PyCGTOOL Tutorial
 =================
 
 This tutorial follows the complete process of parametrising a new molecule within the MARTINI forcefield, covering aspects of mapping design, model generation and model validation.
-PyCGTOOL is used at multiple stages, showing its use in several different situations.
+PyCGTOOL is used at multiple stages, showing its use in several different situations
+All files required to follow this tutorial are present in the ``doc/tutorial_files`` directory.
 
 The molecule chosen as a target for this parametrisation is the :math:`\beta_1` antagonist atenolol.
 
@@ -10,12 +11,12 @@ The molecule chosen as a target for this parametrisation is the :math:`\beta_1`
 Atomistic Simulation
 --------------------
 The reference simulation for the parametrisation of atenolol was performed using the GROMOS 54A7 united atom forcefield with a topology from the `ATB database <https://atb.uq.edu.au/molecule.py?molid=23433>`_.
-A single molecule of atenolol was solvated and equilibrated, before collecting a 50 ns trajectory.
-Currently, PyCGTOOL is limited to trajectories in the GROMACS XTC format, with a single frame GRO for residue information.
+A single molecule of atenolol was solvated and equilibrated, before collecting a 50 ns trajectory using the GROMACS molecular dynamics simulator.
+A reduced copy of this trajectory is provided in the tutorial files since the original is prohibitively large.
 
 Mapping Design
 --------------
-Designing a suitable mapping from the atomistic to the coarse-grained representation requires some experience and a degree of `chemical intuition`, but the ease with which the mapping may be modified using PyCGTOOL allows the mapping to be iterated much more quickly.
+Designing a suitable mapping from the atomistic to the coarse-grained representation requires some experience and a degree of `chemical intuition`, but the ease with which the mapping may be modified using PyCGTOOL allows the mapping to be iterated much more quickly than if the construction of the CG model were to be performed manually.
 
 The process of designing a mapping involves splitting the molecule into fragments, each of which contains approximately four heavy atoms.
 Start by finding functional groups such as amides or carboxylates, each of which may become a single bead.
@@ -24,18 +25,110 @@ Finally, divide any remaining parts of the molecule into beads of four heavy ato
 The ideal bead will contain four heavy atoms and be nearly spherical, but this is not always possible.
 If any atoms remain after clusters of four have been allocated, it may be required to use a mapping of three-to-one for some beads.
 
-After the atoms have been allocated to beads, determine which beads should be bonded by copying the bonds between their component atoms.
+After the atoms have been allocated to beads, determine which beads should be bonded by inspection of the atomistic structure; bonds between functional groups in the atomistic structure become bonds between beads in the CG structure.
 It will probably be the case that there is no obviously best mapping and bond topology, which is not at this point a major issue as multiple mappings can be assessed easily.
 
 Once the mapping and bond topology have been determined, they must be put into a format readable by PyCGTOOL.
 This format is as described in the introduction to PyCGTOOL.
+The mapping file must contain a molecule header which is the residue name enclosed in square brackets.
+Each subsequent line represents a single CG bead and is of the structure (where items in square brackets are optional)::
+
+    <bead name> <MARTINI type> [<bead charge>] <component atom 1> [<component atom 2> ...]
+
+The bonding file has a similar structure with a molecule header followed by lines defining each bond in the format::
+
+    <bead 1> <bead 2>
+
+It is not necessary to define angles since PyCGTOOL is able to infer these from the bond network.
+If only a subset of the possible angles require parameters then they may be listed, preventing automatic enumeration of all possible angles.
+
+The mapping used in the paper to describe atenolol (just one of the several possible mappings) is described by the following input files.
+
+``atenolol.map``::
+
+    [36KB]
+    N1  P1  N1  C12 C13 C14
+    O1  P1  O3  C1  C2  C3
+    O2  SP3 O2  C4
+    C1  SC3 C5  C6
+    C2  SC3 C8  C9
+    C3  SC3 C7  C10
+    N2  P5  C11 O1  N2
+
+``atenolol.bnd``::
+
+    [36KB]
+    N1 O1
+    O1 O2
+    O2 C1
+    O2 C2
+    C1 C2
+    C1 C3
+    C2 C3
+    C3 N2
 
 Model Generation
 ----------------
+The process of model generation after having created the mapping and bond definition files is automated by PyCGTOOL.
+In the simplest case, a parameter set may be generated simply by passing the four input files to PyCGTOOL::
+
+    pycgtool.py -g atenolol.gro -x atenolol.xtc -m atenolol.map -b atenolol.bnd
+
+This will create two output files ``out.gro``, the mapped CG coordinates, and ``out.itp``, the calculated CG model parameters.
 
 Running the CG Simulation
 -------------------------
+Note: These instructions assume the use of GROMACS 5.0 or newer.
+
+From this stage the usual preparation of MARTINI/GROMACS simulations applies.
+The output coordinates ``out.gro`` must be solvated using the GROMACS tool `gmx solvate` with the options::
+
+    gmx solvate -cp out.gro -cs ../../data/water.gro -o solv.gro -radius 0.21
+
+Since MARTINI water cannot be automatically added to the `.top` file, this must be done manually.
+Add the line "W 251" to the bottom of the `.top` file, since 251 should be the number of water molecules added by `gmx solvate`.
+
+The three stages of simulation: minimisation, equilibration, and production may then be run::
+
+    gmx grompp -f em.mdp -c solv.gro -p topol.top -o em.tpr
+    gmx mdrun -deffnm em
+
+    gmx grompp -f npt.mdp -c em.gro -p topol.top -o npt.tpr
+    gmx mdrun -deffnm npt -v
+
+    gmx grompp -f md.mdp -c em.gro -p topol.top -o md.tpr
+    gmx mdrun -deffnm md -v
+
+These simulations should take a few minutes on a modern desktop.
 
 Model Validation
 ----------------
+It is recommended to perform validation before using a generated CG model for production simulations, so that we may have confidence in its ability to replicate the behaviour of the molecule being studied.
+The methods of validation applied in the PyCGTOOL paper are a comparison of the distribution of bonded term measurements between the CG test simulation and the atomistic reference simulation, and a comparison of the radius of gyration between these two simulations.
+Additionally, other methods of validation should be applied relevant to the class of molecule being studied; for instance, validation of membrane lipids should compare the membrane thickness and surface area per lipid to the atomistic reference.o
+
+To compare the distribution of bonded terms, we must first rerun PyCGTOOL to generate samples of the bonded measurements.
+For the atomistic reference simulation, this can be done by running::
+
+    pycgtool.py -g atenolol.gro -x atenolol.xtc -m atenolol.map -b atenolol.bnd --advanced
+
+In the menu, set the advanced option `dump_measurements` to `True` by selecting it with the arrow keys and toggling with the enter key.
+Once this option has been set, continue by pressing the q key.
+PyCGTOOL will now output a sample of each measured bond length and angle (but since the reference trajectory is short, the target sample size is not met and all values are collected), in the files ``36KB_length.dat`` and ``36KB_angle.dat``.
+
+Since we will be collecting samples of the same measurements from the CG simulation, these files should be renamed to, for instance, `ref_length.dat` and `ref_angle.dat`.
+Collect the same samples for the CG simulation using::
+
+    pycgtool.py -g md.gro -x md.xtc -b atenolol.bnd
+
+Since we provide a bond file, but not a mapping file, PyCGTOOL will know that this is intended to simply collect bond measurements and will automatically set the `dump_measurements` option to `True`.
+Again, the files created will be called ``36KB_length.dat`` and ``36KB_angle.dat``.
+
+These samples were compared in the paper using an R script to generate a series of boxplots, but a simpler Python script is provided which may be used to compare the mean and standard deviations of the samples::
+
+    average_columns.py ref_length.dat 36KB_length.dat
+    average_columns.py ref_angle.dat 36KB_angle.dat
+
+If the automatically generated parameters provide an accurate representation of the reference structure, the percentage error between the two samples will be small.
 
+Validation of the more general molecular conformation may be performed by comparison of the radius of gyration of the reference and CG models.

doc/tutorial_files/Makefile

+all: pycgtool cgprepare em npt md
+
+pycgtool:
+	../../pycgtool.py -g ref.gro -x ref.xtc -m atenolol.map -b atenolol.bnd
+
+cgprepare:
+	gmx solvate -cp out.gro -cs ../../data/water.gro -o solv.gro -radius 0.21
+	cp template.top topol.top
+	echo "W 251" >> topol.top
+
+em:
+	gmx grompp -f em.mdp -c solv.gro -p topol.top -o em.tpr
+	gmx mdrun -deffnm em
+
+npt:
+	gmx grompp -f npt.mdp -c em.gro -p topol.top -o npt.tpr
+	gmx mdrun -deffnm npt -v
+
+md:
+	gmx grompp -f md.mdp -c npt.gro -p topol.top -o md.tpr
+	gmx mdrun -deffnm md -v
+
+rgyr:
+	gmx gyrate -f ref.xtc -s ref.tpr -o ref-gyr.xvg
+	gmx gyrate -f md.xtc -s md.tpr -o cg-gyr.xvg
+
+clean:
+	git clean -i

doc/tutorial_files/atenolol.bnd

+[36KB]
+N1  O1
+O1  O2
+O2  C1
+O2  C2
+C1  C2
+C1  C3
+C2  C3
+C3  N2

doc/tutorial_files/atenolol.map

+[36KB]
+N1  P1  N1  C12 C13 C14
+O1  P1  O3  C1  C2  C3
+O2  SP3 O2  C4
+C1  SC2 C5  C6
+C2  SC2 C8  C9
+C3  SC3 C7  C10
+N2  P5  C11 O1  N2

doc/tutorial_files/average_columns.py

+#!/usr/bin/env python3
+"""
+Calculate mean and standard deviation of column data using Welford's one pass algorithm
+"""
+
+import sys
+import numpy as np
+
+np.set_printoptions(formatter={"float": lambda x: "{0:10.3f}".format(x)})
+
+def average_columns(filename):
+    with open(filename) as f:
+        mean, sdev = None, None
+        nlines = 0
+
+        for line in f:
+            if not line.strip():
+                continue
+            try:
+                cols = np.array(list(map(float, line.split())))
+            except ValueError:
+                continue
+            if mean is None:
+                mean = np.zeros(len(cols))
+                sdev = np.zeros(len(cols))
+            nlines += 1
+            delta = cols - mean
+            mean += delta / nlines
+            sdev += delta * (cols - mean)
+
+
+        sdev = np.sqrt(sdev / (nlines - 1))
+        print("File: {0}".format(filename))
+        print("Mean: {0}".format(mean))
+        print("Sdev: {0}".format(sdev))
+        print()
+    return (mean, sdev)
+
+def compare_files(filename1, filename2):
+    means = (average_columns(filename1)[0], average_columns(filename2)[0])
+    print("Diff: {0}".format(means[1] - means[0]))
+    print("%Dif: {0}".format(100. * (means[1] - means[0]) / means[0]))
+
+if __name__ == "__main__":
+    if len(sys.argv) == 2:
+        average_columns(sys.argv[1])
+    elif len(sys.argv) == 3:
+        compare_files(sys.argv[1], sys.argv[2])
+    else:
+        print("Give one filename to average columns, or two filenames to compare.")

doc/tutorial_files/em.mdp

+
+; 7.3.2 Preprocessing
+define                  =   ; defines to pass to the preprocessor
+
+; 7.3.3 Run Control
+integrator              = steep        ; steepest descents energy minimization
+nsteps                  = 2000          ; maximum number of steps to integrate
+
+; 7.3.5 Energy Minimization
+emtol                   = 0           ; [kJ/mol/nm] minimization is converged when max force is < emtol
+emstep                  = 0.01          ; [nm] initial step-size
+
+; 7.3.8 Output Control
+nstxout                 = 100           ; [steps] freq to write coordinates to trajectory
+nstvout                 = 100           ; [steps] freq to write velocities to trajectory
+nstfout                 = 100           ; [steps] freq to write forces to trajectory
+nstlog                  = 1             ; [steps] freq to write energies to log file
+nstenergy               = 1             ; [steps] freq to write energies to energy file
+energygrps              = System        ; group(s) to write to energy file
+
+; 7.3.9 Neighbor Searching
+nstlist                 = 1             ; [steps] freq to update neighbor list
+ns_type                 = grid          ; method of updating neighbor list
+pbc                     = xyz           ; periodic boundary conditions in all directions
+rlist                   = 0.8           ; [nm] cut-off distance for the short-range neighbor list
+cutoff-scheme           = verlet
+
+; 7.3.10 Electrostatics
+coulombtype             = cut-off          ; Particle-Mesh Ewald electrostatics
+rcoulomb                = 0.8           ; [nm] distance for Coulomb cut-off
+
+; 7.3.11 VdW
+vdwtype                 = cut-off       ; twin-range cut-off with rlist where rvdw >= rlist
+rvdw                    = 0.8           ; [nm] distance for LJ cut-off

doc/tutorial_files/md.mdp

+;
+; STANDARD MD INPUT OPTIONS FOR MARTINI 2.x
+; Updated 15 Jul 2015 by DdJ
+;
+; for use with GROMACS 5
+; For a thorough comparison of different mdp options in combination with the Martini force field, see:
+; D.H. de Jong et al., Martini straight: boosting performance using a shorter cutoff and GPUs, submitted.
+
+title                    = Martini
+
+; TIMESTEP IN MARTINI 
+; Most simulations are numerically stable with dt=40 fs, 
+; however better energy conservation is achieved using a 
+; 20-30 fs timestep. 
+; Time steps smaller than 20 fs are not required unless specifically stated in the itp file.
+
+integrator               = md
+dt                       = 0.02  
+nsteps                   = 2500000
+nstcomm                  = 100
+comm-grps                = 
+
+nstxout                  = 0
+nstvout                  = 0
+nstfout                  = 0
+nstlog                   = 1000
+nstenergy                = 1000
+nstxout-compressed       = 1000
+compressed-x-precision   = 100
+compressed-x-grps        = System
+energygrps               = System
+imd-group                = Other
+
+; NEIGHBOURLIST and MARTINI 
+; To achieve faster simulations in combination with the Verlet-neighborlist
+; scheme, Martini can be simulated with a straight cutoff. In order to 
+; do so, the cutoff distance is reduced 1.1 nm. 
+; Neighborlist length should be optimized depending on your hardware setup:
+; updating ever 20 steps should be fine for classic systems, while updating
+; every 30-40 steps might be better for GPU based systems.
+; The Verlet neighborlist scheme will automatically choose a proper neighborlist
+; length, based on a energy drift tolerance.
+;
+; Coulomb interactions can alternatively be treated using a reaction-field,
+; giving slightly better properties.
+; Please realize that electrostVatic interactions in the Martini model are 
+; not considered to be very accurate to begin with, especially as the 
+; screening in the system is set to be uniform across the system with 
+; a screening constant of 15. When using PME, please make sure your 
+; system properties are still reasonable.
+;
+; With the polarizable water model, the relative electrostatic screening 
+; (epsilon_r) should have a value of 2.5, representative of a low-dielectric
+; apolar solvent. The polarizable water itself will perform the explicit screening
+; in aqueous environment. In this case, the use of PME is more realistic.
+
+
+cutoff-scheme            = Verlet
+nstlist                  = 20
+ns_type                  = grid
+pbc                      = xyz
+verlet-buffer-tolerance  = 0.005
+
+coulombtype              = reaction-field 
+rcoulomb                 = 1.1
+epsilon_r                = 15   ; 2.5 (with polarizable water)
+epsilon_rf               = 0
+vdw_type                 = cutoff  
+vdw-modifier             = Potential-shift-verlet
+rvdw                     = 1.1
+
+; MARTINI and TEMPERATURE/PRESSURE
+; normal temperature and pressure coupling schemes can be used. 
+; It is recommended to couple individual groups in your system separately.
+; Good temperature control can be achieved with the velocity rescale (V-rescale)
+; thermostat using a coupling constant of the order of 1 ps. Even better 
+; temperature control can be achieved by reducing the temperature coupling 
+; constant to 0.1 ps, although with such tight coupling (approaching 
+; the time step) one can no longer speak of a weak-coupling scheme.
+; We therefore recommend a coupling time constant of at least 0.5 ps.
+; The Berendsen thermostat is less suited since it does not give
+; a well described thermodynamic ensemble.
+; 
+; Pressure can be controlled with the Parrinello-Rahman barostat, 
+; with a coupling constant in the range 4-8 ps and typical compressibility 
+; in the order of 10e-4 - 10e-5 bar-1. Note that, for equilibration purposes, 
+; the Berendsen barostat probably gives better results, as the Parrinello-
+; Rahman is prone to oscillating behaviour. For bilayer systems the pressure 
+; coupling should be done semiisotropic.
+
+tcoupl                   = v-rescale 
+tc-grps                  = System
+tau_t                    = 1.0
+ref_t                    = 310
+pcoupl                   = parrinello-rahman 
+pcoupltype               = isotropic
+tau_p                    = 12.0 ;parrinello-rahman is more stable with larger tau-p, DdJ, 20130422
+compressibility          = 3e-4 3e-4
+ref_p                    = 1.0 1.0
+refcoord_scaling         = all
+
+gen_vel                  = no
+
+; MARTINI and CONSTRAINTS 
+; for ring systems and stiff bonds constraints are defined
+; which are best handled using Lincs. 
+
+constraints              = none 
+constraint_algorithm     = Lincs

doc/tutorial_files/npt.mdp

+;
+; STANDARD MD INPUT OPTIONS FOR MARTINI 2.x
+; Updated 15 Jul 2015 by DdJ
+;
+; for use with GROMACS 5
+; For a thorough comparison of different mdp options in combination with the Martini force field, see:
+; D.H. de Jong et al., Martini straight: boosting performance using a shorter cutoff and GPUs, submitted.
+
+title                    = Martini
+
+; TIMESTEP IN MARTINI 
+; Most simulations are numerically stable with dt=40 fs, 
+; however better energy conservation is achieved using a 
+; 20-30 fs timestep. 
+; Time steps smaller than 20 fs are not required unless specifically stated in the itp file.
+
+integrator               = md
+dt                       = 0.01  
+nsteps                   = 1000000
+nstcomm                  = 100
+comm-grps                = 
+
+nstxout                  = 0
+nstvout                  = 0
+nstfout                  = 0
+nstlog                   = 1000
+nstenergy                = 1000
+nstxout-compressed       = 1000
+compressed-x-precision   = 100
+compressed-x-grps        = 
+energygrps               = System
+
+; NEIGHBOURLIST and MARTINI 
+; To achieve faster simulations in combination with the Verlet-neighborlist
+; scheme, Martini can be simulated with a straight cutoff. In order to 
+; do so, the cutoff distance is reduced 1.1 nm. 
+; Neighborlist length should be optimized depending on your hardware setup:
+; updating ever 20 steps should be fine for classic systems, while updating
+; every 30-40 steps might be better for GPU based systems.
+; The Verlet neighborlist scheme will automatically choose a proper neighborlist
+; length, based on a energy drift tolerance.
+;
+; Coulomb interactions can alternatively be treated using a reaction-field,
+; giving slightly better properties.
+; Please realize that electrostVatic interactions in the Martini model are 
+; not considered to be very accurate to begin with, especially as the 
+; screening in the system is set to be uniform across the system with 
+; a screening constant of 15. When using PME, please make sure your 
+; system properties are still reasonable.
+;
+; With the polarizable water model, the relative electrostatic screening 
+; (epsilon_r) should have a value of 2.5, representative of a low-dielectric
+; apolar solvent. The polarizable water itself will perform the explicit screening
+; in aqueous environment. In this case, the use of PME is more realistic.
+
+
+cutoff-scheme            = Verlet
+nstlist                  = 20
+ns_type                  = grid
+pbc                      = xyz
+verlet-buffer-tolerance  = 0.005
+
+coulombtype              = reaction-field 
+rcoulomb                 = 1.1
+epsilon_r                = 15   ; 2.5 (with polarizable water)
+epsilon_rf               = 0
+vdw_type                 = cutoff  
+vdw-modifier             = Potential-shift-verlet
+rvdw                     = 1.1
+
+; MARTINI and TEMPERATURE/PRESSURE
+; normal temperature and pressure coupling schemes can be used. 
+; It is recommended to couple individual groups in your system separately.
+; Good temperature control can be achieved with the velocity rescale (V-rescale)
+; thermostat using a coupling constant of the order of 1 ps. Even better 
+; temperature control can be achieved by reducing the temperature coupling 
+; constant to 0.1 ps, although with such tight coupling (approaching 
+; the time step) one can no longer speak of a weak-coupling scheme.
+; We therefore recommend a coupling time constant of at least 0.5 ps.
+; The Berendsen thermostat is less suited since it does not give
+; a well described thermodynamic ensemble.
+; 
+; Pressure can be controlled with the Parrinello-Rahman barostat, 
+; with a coupling constant in the range 4-8 ps and typical compressibility 
+; in the order of 10e-4 - 10e-5 bar-1. Note that, for equilibration purposes, 
+; the Berendsen barostat probably gives better results, as the Parrinello-
+; Rahman is prone to oscillating behaviour. For bilayer systems the pressure 
+; coupling should be done semiisotropic.
+
+tcoupl                   = v-rescale 
+tc-grps                  = System
+tau_t                    = 1.0
+ref_t                    = 310
+pcoupl                   = berendsen
+pcoupltype               = isotropic
+tau_p                    = 12.0 ;parrinello-rahman is more stable with larger tau-p, DdJ, 20130422
+compressibility          = 3e-4
+ref_p                    = 1.0
+refcoord_scaling         = all
+
+gen_vel                  = no
+
+; MARTINI and CONSTRAINTS 
+; for ring systems and stiff bonds constraints are defined
+; which are best handled using Lincs. 
+
+constraints              = none 
+constraint_algorithm     = Lincs

doc/tutorial_files/ref.gro

+Giant Rising Ordinary Mutants for A Clerical Setup in water
+ 29
+    136KB   HN2    1   1.183   2.091   3.336  0.4004  3.0041  0.1665
+    136KB    N2    2   1.086   2.097   3.307  0.3140  0.3540 -0.3132
+    136KB   HN1    3   1.030   2.159   3.362  2.2814  1.9625  0.0126
+    136KB   C11    4   1.044   2.011   3.212 -0.3294 -0.2374  0.4940
+    136KB    O1    5   1.123   1.933   3.159  0.1558  0.0691  0.7519
+    136KB   C10    6   0.893   2.015   3.195 -0.2902  0.4953  0.2717
+    136KB    C7    7   0.816   1.942   3.085  0.1506  0.2969  0.0944
+    136KB    C8    8   0.842   1.807   3.057  0.1665  0.4792 -0.8131
+    136KB    H8    9   0.894   1.751   3.134  0.6246  1.2449 -0.5555
+    136KB    C6   10   0.715   2.005   3.012 -0.0948 -0.2399 -0.0337
+    136KB    H6   11   0.689   2.108   3.036 -0.6736 -0.5463  0.6814
+    136KB    C5   12   0.636   1.937   2.920 -0.4231  0.1308 -0.0297
+    136KB    H5   13   0.552   1.987   2.872 -0.3641  1.3976  1.1161
+    136KB    C4   14   0.675   1.806   2.887  0.0542  0.3155 -0.2048
+    136KB    O2   15   0.614   1.751   2.776  0.3195 -0.0247 -0.1830
+    136KB    C9   16   0.778   1.739   2.954 -0.1834 -0.0778 -0.2348
+    136KB    H9   17   0.792   1.632   2.941 -1.4326 -0.0191 -2.4834
+    136KB    C1   18   0.652   1.627   2.715 -0.0574  0.0433 -0.5566
+    136KB    C2   19   0.551   1.526   2.661 -0.3248  0.0834 -0.1427
+    136KB    H2   20   0.504   1.575   2.573  2.1871  2.6350 -0.2157
+    136KB    O3   21   0.453   1.486   2.757 -0.1564  0.0997  0.0375
+    136KB    HO   22   0.393   1.562   2.775 -0.7999 -0.4396  0.1706
+    136KB    C3   23   0.627   1.400   2.615 -0.5193 -0.0460 -0.1100
+    136KB    N1   24   0.731   1.419   2.513 -0.6071 -0.2673 -0.2422
+    136KB    HN   25   0.820   1.457   2.546 -2.0855  3.1942  0.2367
+    136KB   C12   26   0.750   1.318   2.407  0.4805 -0.2676 -0.0564
+    136KB  H121   27   0.831   1.247   2.428  0.2918 -1.0021 -1.6561
+    136KB   C14   28   0.776   1.394   2.277 -0.7249 -0.3819 -0.3773
+    136KB   C13   29   0.623   1.237   2.379  0.0035  0.4471  0.0400
+   3.33938   3.33938   3.33938

doc/tutorial_files/template.top

+; Include forcefield parameters
+#include "../../data/martini_v2.2.itp"
+#include "../../data/w.itp"
+#include "out.itp"
+
+[ system ]
+36KB
+
+[ molecules ]
+36KB		1