Computational Drug Discovery and Design

getline;centind=index($0,"#Centroid");

}

numcent=0; print $2,$1,NR-b0;

c=$2;

getline; endrec = index("End",$0);

while( endrec != 1 ){

FIL = sprintf("centroid%2.2d.pdb",c);

print>FIL;

getline; endrec = index($0,"#End");

}

b0=NR+2;

}

However, since the centroid structures have no physical mean-

ing, we shall search for best cluster members, i.e., those of the

clustered structures that are the closest to the centroids in the

RMSD sense. Here is the fifth step of the procedure:

#!/bin/csh

set list=’framelist’

rm -f best_members.out

foreach file (‘ls *.member.dat‘)

set i=$file:r

set j=$i:r

set num=‘sort -nk2 $file | head -1 | cut -f1 -d’ ’‘

set rms=‘sort -nk2 $file | head -1 | cut -f2 -d’ ’‘

set i=1

foreach name (‘cat $list‘)

if ($i == $num) then

set m=$name:e

echo ’Best member in’ $j’:’ ID = $m ’(rmsd = ’$rms’)’

echo ’Best member in’ $j’:\

’ ID = $m ’(rmsd = ’$rms’)’>>best_members.out

cp $name ${j}_best_member.pdb

endif

set i=‘expr $i + 1‘

end

end

Please note that at the beginning of the script we specify

explicitly the name of the file with the frame list, defined at the

clustering step. The output is saved in the text file best_members.

out. It lists the identifiers of the best structures in each cluster along

with their corresponding RMSD values (seeNote 7).

As a result, we now have a relatively short list, containing

representative structures of the receptor (one per each cluster

found), which will be used in subsequent docking.

160 Gre ́gory Menchon et al.