Task 10 - Journal (PKU)
Contents
Initial checks
Verify completion, frame number and frame rate: <source lang="bash"> gmxcheck -f _md.xtc </source>
performance statistics: <source lang="bash"> tail _md.log </source>
Create Trajectories
<source lang="bash">
- fix jumps over the periodic boundary
trjconv -f _md.xtc -s _solv.pdb -pbc nojump -o _traj_nojump.xtc<<EOF0\nEOF
- center protein in the box
trjconv -f _traj_nojump.xtc -s _solv.pdb -center -o _traj_center.xtc<<EOF1\n0\nEOF
- eliminate rotations
trjconv -f _traj_center.xtc -s _solv.pdb -fit rot+trans -o _traj_norot.xtc<<EOF1\n0\nEOF
- create pdb file
trjconv -f _traj_norot.xtc -s _solv.pdb -o _traj.pdb<<EOF1\nEOF
- reduce fluctuations and write every fifth frame
g_filter -f _traj.pdb -s _solv.pdb -ol _traj_filtered.pdb -fit -nf 5<<EOF1\nEOF
</source>
Plot Energies
<source lang="bash"> echo 12 0 | g_energy -f _md.edr -o _temperature.xvg echo 13 0 | g_energy -f _md.edr -o _pressure.xvg echo -e "9\n10\n11 0" | g_energy -f _md.edr -o _energy.xvg echo 18 0 | g_energy -f _md.edr -o _volume.xvg echo 19 0 | g_energy -f _md.edr -o _density.xvg echo -e "15\n16\n17 0" | g_energy -f _md.edr -o _box.xvg echo -e "48\n50 0" | g_energy -f _md.edr -o _coulomb-inter.xvg echo -e "49\n51 0" | g_energy -f _md.edr -o _vanderwaals-inter.xvg </source>
Minimal Distance of Interactions
<source lang="bash"> echo 1 0 |g_mindist -f _md.xtc -s _md.tpr -od _minimal_periodic_distance.xvg -pi echo 3 0 |g_mindist -f _md.xtc -s _md.tpr -od _minimal_periodic_c_alpha_distance.xvg -pi </source>
RMSD Calculations
<source lang="bash"> echo 1 1|g_rmsf -f _md.xtc -s _md.tpr -o _rmsf-per-residue.xvg -ox _average.pdb -oq _bfactors.pdb -res echo 1 1 |g_rms -f _traj_norot.xtc -s _md.tpr -o _rmsd-all-atom-vs-start.xvg echo 4 4 |g_rms -f _traj_norot.xtc -s _md.tpr -o _rmsd-backbone-vs-start.xvg echo 1 |trjconv -f _traj_norot.xtc -s _md.tpr -o _protein.xtc echo 1 1 |g_rms -f _protein.xtc -s _average.pdb -o _rmsd-all-atom-vs-average.xvg echo 4 4 |g_rms -f _protein.xtc -s _average.pdb -o _rmsd-backbone-vs-average.xvg </source>
Radius of Gyration
<source lang="bash"> echo 1 0 |g_gyrate -f _traj_norot.xtc -s _md.tpr -o _radius-of-gyration.xvg echo 1 0 |g_gyrate -f _traj_norot.xtc -s _md.tpr -o _moments_of_inertia.xvg -moi </source>
Accessible Surface
<source lang="bash"> echo 1 1 |g_sas -f _traj_norot.xtc -s _md.tpr -o _solvent-accessible-surface.xvg -oa _atomic-sas.xvg -or _residue-sas.xvg </source>
Hydrogen Bonds
<source lang="bash"> echo 1 1 | g_hbond -f _traj_norot.xtc -s _md.tpr -num _hydrogen-bonds-intra-protein.xvg echo 1 12 | g_hbond -f _traj_norot.xtc -s _md.tpr -num _hydrogen-bonds-protein-water.xvg </source>
Secondary Structure
<source lang="bash"> wget http://swift.cmbi.ru.nl/gv/dssp/HTML/dsspcmbi chmod +x dsspcmbi export DSSP=$HOME/dsspcmbi echo 1|do_dssp -f _traj_norot.xtc -s _md.tpr -o _secondary-structure.xpm -sc _secondary-structure.xvg -dt 10 xpm2ps -f _secondary-structure.xpm -o _secondary-structure.eps </source>
Ramachandran Plot
<source lang="bash"> g_rama -f _traj_norot.xtc -s _md.tpr -o _ramachandran.xvg </source>
Somewhat nicer and modified from here:
<source lang="python"> rama_GENERAL = "General" rama_GLYCINE = "Glycine" rama_PROLINE = "Proline" rama_PRE_PRO = "Pre-Pro" ramachandran_types = [rama_GENERAL,rama_GLYCINE,rama_PROLINE,rama_PRE_PRO]
- I have used the same colours as RAMPAGE
- http://raven.bioc.cam.ac.uk/rampage.php
rama_settings = {"General" : ([0, 0.0005, 0.02, 1],
['#FFFFFF','#B3E8FF','#7FD9FF'],
"pct/rama/rama500-general.data"),
# or rama500-general-nosec.data
"Glycine" : ([0, 0.002, 0.02, 1],
['#FFFFFF','#FFE8C5','#FFCC7F'],
"pct/rama/rama500-gly-sym.data"),
# or rama500-gly-sym-nosec.data
"Proline" : ([0, 0.002, 0.02, 1],
['#FFFFFF','#D0FFC5','#7FFF8C'],
"pct/rama/rama500-pro.data"),
"Pre-Pro" : ([0, 0.002, 0.02, 1],
['#FFFFFF','#B3E8FF','#7FD9FF'],
"pct/rama/rama500-prepro.data")}
#P.S. Also rama500-ala-nosec.data
import numpy def load_data_file(filename) :
STEP=2 HALF_STEP=1 STEP = HALF_STEP*2 lower_bounds = range(-180, 180, STEP) mid_points = range(-180+HALF_STEP, 180+HALF_STEP, STEP) upper_bounds = range(-180+STEP, 180+STEP, STEP)
data = numpy.array([[0.0 for x in mid_points] for y in mid_points],
dtype=numpy.float)
"""
# Table name/description: "Top500 General case (not Gly, Pro, or pre-Pro) B<30"
# Number of dimensions: 2
# For each dimension, 1 to 2: lower_bound upper_bound number_of_bins wrapping
# x1: -180.0 180.0 180 true
# x2: -180.0 180.0 180 true
# List of table coordinates and values. (Value is last number on each line.)
-179.0 -179.0 0.0918642445114388
-179.0 -177.0 0.07105717866463215
...
"""
input_file = open(filename,"r")
for line in input_file :
#Strip the newline character(s) from the end of the line
if line[-1]=="\n" : line = line[:-1]
if line[-1]=="\r" : line = line[:-1]
if line[0]=="#" :
#comment
pass
else :
#data
parts = line.split()
assert len(parts)==3
x1 = float(parts[0]) #phi
x2 = float(parts[1]) #psi
value = float(parts[2])
assert x1 == float(int(x1))
assert x2 == float(int(x2))
i1 = mid_points.index(int(x1))
i2 = mid_points.index(int(x2))
data[i1,i2]=value
input_file.close()
return (data, lower_bounds, mid_points, upper_bounds)
- filename = "stat/rama/rama500-general.data"
- data, lower_bounds, mid_points, upper_bounds = load_data_file(filename)
- print sum(sum(data))
from rpy import * r.library("MASS")
print "Creating R function", r(""" ramachandran.plot <- function(x.scatter, y.scatter,
x.grid = seq(0, 1, len = nrow(z)), y.grid = seq(0, 1, len = ncol(z)), z.grid,
xlim = range(x.grid, finite = TRUE), ylim = range(y.grid, finite = TRUE),
zlim = range(z.grid, finite = TRUE), levels = pretty(zlim, nlevels),
nlevels = 20, color.palette = cm.colors, col = color.palette(length(levels) -
1), plot.title="", plot.axes, key.title, key.axes, asp = NA,
xaxs = "i", yaxs = "i", las = 1, axes = TRUE, frame.plot = axes,
...)
{
if (missing(z.grid)) {
stop("no 'z.grid' matrix specified")
}
else if (is.list(x.grid)) {
y.grid <- x.grid$y
x.grid <- x.grid$x
}
if (any(diff(x.grid) <= 0) || any(diff(y.grid) <= 0))
stop("increasing 'x.grid' and 'y.grid' values expected")
plot.new() plot.window(xlim, ylim, "", xaxs = xaxs, yaxs = yaxs, asp = asp)
if (!is.matrix(z.grid) || nrow(z.grid) <= 1 || ncol(z.grid) <= 1)
stop("no proper 'z.grid' matrix specified")
if (!is.double(z.grid))
storage.mode(z.grid) <- "double"
.Internal(filledcontour(as.double(x.grid), as.double(y.grid), z.grid, as.double(levels),
col = col))
if (!(missing(x.scatter)) && !(missing(y.scatter))) {
plot.xy(xy.coords(x.scatter,y.scatter,NULL,NULL,NULL,NULL),
xlim=xlim, ylim=ylim, xlab="", ylab="", asp=asp,
type="p", pch=20, cex=0.1)
}
if (missing(plot.axes)) {
if (axes) {
title(main=plot.title, xlab=expression(phi), ylab=expression(psi))
axis(1, at=c(-180,-90,0,90,180))
axis(2, at=c(-180,-90,0,90,180))
}
}
else plot.axes
if (frame.plot)
box()
if (missing(plot.title))
title(...)
else plot.title
invisible()
} """) print "Done"
import math
def degrees(rad_angle) :
"""Converts and angle in radians to degrees, mapped to the range [-180,180]"""
angle = rad_angle * 180 / math.pi
#Note this assume the radians angle is positive as that's what MMTK does
while angle > 180 :
angle = angle - 360
return angle
def next_residue(residue) :
"""Expects an MMTK residue, returns the next residue in the chain, or None"""
#Proteins go N terminal --> C terminal
#The next reside is bonded to the C of this atom...
for a in residue.peptide.C.bondedTo():
if a.parent.parent != residue:
return a.parent.parent
return None
def residue_amino(residue) :
"""Expects an MMTK residue, returns the three letter amino acid code in upper case"""
if residue :
return residue.name[0:3].upper()
else :
return None
def residue_ramachandran_type(residue) :
"""Expects an MMTK residue, returns ramachandran 'type' (General, Glycine, Proline or Pre-Pro)"""
if residue_amino(residue)=="GLY" :
return rama_GLYCINE
elif residue_amino(residue)=="PRO" :
return rama_PROLINE
elif residue_amino(next_residue(residue))=="PRO" :
#exlcudes those that are Pro or Gly
return rama_PRE_PRO
else :
return rama_GENERAL
scatter_phi = dict() scatter_psi = dict() for ramachandran_type in ramachandran_types :
scatter_phi[ramachandran_type]=[] scatter_psi[ramachandran_type]=[]
pdb=sys.argv[1] pdb_filename = "%s.pdb" % pdb
print "Loading PDB file: " + pdb_filename import MMTK.PDB import MMTK.Proteins
- protein = MMTK.Proteins.Protein("1HMP.pdb", model="no_hydrogens")
- Load the PDB file, ignore the hydrogrens, and then build a model of the peptides:
configuration = MMTK.PDB.PDBConfiguration(pdb_filename) configuration.deleteHydrogens() protein = MMTK.Proteins.Protein(configuration.createPeptideChains(model = "no_hydrogens")) for chain in protein :
print chain.name
for residue in chain :
phi, psi = residue.phiPsi()
#print residue.name, phi, psi
if phi and psi :
ramachandran_type = residue_ramachandran_type(residue)
assert ramachandran_type in ramachandran_types
scatter_phi[ramachandran_type].append(degrees(phi))
scatter_psi[ramachandran_type].append(degrees(psi))
assert len(scatter_phi) == len(scatter_psi)
print "Done"
- pdf_filename = "../%s.pdf" % pdb
- r.pdf(pdf_filename)
png_filename = "%s.png" % pdb r.png(png_filename)
- To get four plots on one page, you could use :
- r.split_screen([2,2]) #split into two by two screen
- Or:
- r.layout(Numeric.array([[1,2],[3,4]]), respect=True)
- But I went for simply:
r.par(mfrow=[2,2])
for (i,ramachandran_type) in enumerate(ramachandran_types) :
#pdf_filename = "../%s_%s.pdf" % (pdb, ramachandran_type) (rama_levels, rama_colors, rama_filename) = rama_settings[ramachandran_type] print "Loading data file: " + rama_filename, data, lower_bounds, mid_points, upper_bounds = load_data_file(rama_filename) print "Done"
#print "Creating PDF output file: " + pdf_filename, #r.pdf(pdf_filename) #r.plot(scatter_phi, scatter_psi)
print "Generating quadrant %i, %s" % (i+1, ramachandran_type) #r.screen(i+1)
#Use small margins to make the plots nice and big, #and specify a SQUARE plot area (to go with aspect ratio, asp=1) r.par(mar = [2, 2, 2, 2], pty="s")
#This function will do a Ramachandran plot in the next quadrant
#which we setup using par(mfrow-...)
r.ramachandran_plot(x_scatter=scatter_phi[ramachandran_type],
y_scatter=scatter_psi[ramachandran_type],
x_grid=mid_points, y_grid=mid_points, z_grid=data,
xlim=[-180,180], ylim=[-180,180], asp=1.0,
plot_title=ramachandran_type, drawlabels=False,
levels=rama_levels, col=rama_colors)
print ramachandran_type + " Done"
r.dev_off() print "Done" </source>
more RMSDs
<source lang="bash"> echo -e "1\n1\n\n"|g_rms -s _md.tpr -f _traj_norot.xtc -f2 _traj_norot.xtc -m _rmsd-matrix.xpm -dt 10 echo -e "6\n6\n\n"|g_rms -s _md.tpr -f _traj_norot.xtc -f2 _traj_norot.xtc -m _rmsd-matrix_chain-c_b.xpm -dt 10 xpm2ps -f _rmsd-matrix.xpm -o _rmsd-matrix.eps -rainbow blue xpm2ps -f _rmsd-matrix_chain-c_b.xpm -o _rmsd-matrix_chain-c_b.eps -rainbow blue </source>
Cluster Analysis
<source lang="bash"> echo 6 6 | g_cluster -s _md.tpr -f _traj_norot.xtc -dm _rmsd-matrix_chain-c_b.xpm -dist _rmsd-distribution.xvg -o _clusters.xpm -sz _cluster-sizes.xvg -tr _cluster-transitions.xpm -ntr _cluster-transitions.xvg -clid _cluster-id-over-time.xvg -cl _clusters.pdb -cutoff 0.1 -method gromos -dt 10 </source>
Distance RMSD
<source lang="bash"> echo 1|g_rmsdist -s _md.tpr -f _traj_norot.xtc -o _distance-rmsd.xvg </source>
