/**
* $outfile will be overwritten with a Scheme script for loading in Coot.
* $clash is the data structure from loadClashlist()
* $rama is the data structure from loadRamachandran()
* $rota is the data structure from loadRotamer()
* $cbdev is the data structure from loadCbetaDev()
* $pperp is the data structure from loadBasePhosPerp()
* Any of them can be set to null if the data is unavailable.
*/
function makeCootClusteredChart($infile, $outfile, $outfile_py, $clash, $rama, $rota, $cbdev, $pperp)
{
//$startTime1 = time();
//{{{ 0. A lovely Scheme script written for us by Paul Emsley
$schemeScript = <<<HEREDOC
; -*-scheme-*-
;;(molprobity-fascinating-clusters-things-gui
;; dialog-name
;; sorting-options
;; list-of-clusters)
;;
;; where a cluster is:
;; (list
;; cluster-name-string
;; cluster-center-go-button-label-string
;; ccgb-x ccgb-y ccgb-z
;; ; a list of specific items:
;; (list
;; (list specific-button-label-string button-red button-green button-blue
;; specific-x specific-y specific-z))))
;;
;;
;;(molprobity-fascinating-clusters-things-gui
;; gui-name-string
;; (list
;; (list "Active Site" (list 0 1 2 3 4))
;; (list "Worst First" (list 3 4 2 1 0)))
;; ; now a list of clusters:
;; (list
;; (list cluster-name-string
;; cluster-center-go-button-label-string
;; ccgb-x ccgb-y ccgb-z
;; ; now a list of specific items
;; (list
;; (list specific-button-label-string button-red button-green button-blue
;; specific-x specific-y specific-z)
;; (list specific-button-label-string button-red button-green button-blue
;; specific-x specific-y specific-z)))
;; (list cluster-name-string
;; cluster-center-go-button-label-string
;; ccgb-x ccgb-y ccgb-z
;; ; now a list of specific items
;; (list
;; (list specific-button-label-string button-red button-green button-blue
;; specific-x specific-y specific-z)
;; (list specific-button-label-string button-red button-green button-blue
;; specific-x specific-y specific-z)))))
;;
(define (molprobity-fascinating-clusters-things-gui window-name sorting-options cluster-list)
(define ncluster-max 75)
;; utility function
(define (add-feature-buttons feature-list cluster-vbox)
(let ((frame (gtk-frame-new "Cluster Features"))
\t (vbox (gtk-vbox-new #f 0)))
(gtk-box-pack-start cluster-vbox frame #f #f 2)
(gtk-container-add frame vbox)
;; add buttons to vbox for each feature
;;
(map (lambda (feature)
\t ; (format #t "feature: ~s~%" feature)
\t (let ((button (gtk-button-new-with-label (car feature))))
\t (gtk-signal-connect button "clicked"
\t\t\t\t (lambda ()
\t\t\t\t (set-rotation-centre
\t\t\t\t (list-ref feature 4)
\t\t\t\t (list-ref feature 5)
\t\t\t\t (list-ref feature 6))))
\t (gtk-box-pack-start vbox button #f #f 1)))
\t feature-list)))
;; main body
(let* ((window (gtk-window-new 'toplevel))
\t (scrolled-win (gtk-scrolled-window-new))
\t (outside-vbox (gtk-vbox-new #f 2))
\t (inside-vbox (gtk-vbox-new #f 0)))
(format #t "Maxiumum number of clusters displayed: ~s~%" ncluster-max)
(gtk-window-set-default-size window 300 200)
(gtk-window-set-title window window-name)
(gtk-container-border-width inside-vbox 2)
(gtk-container-add window outside-vbox)
(gtk-box-pack-start outside-vbox scrolled-win #t #t 0) ; expand fill padding
(gtk-scrolled-window-add-with-viewport scrolled-win inside-vbox)
(gtk-scrolled-window-set-policy scrolled-win 'automatic 'always)
(let loop ((cluster-list cluster-list)
\t (count 0))
(cond
((null? cluster-list) 'done)
((= ncluster-max count) 'done)
(else
\t(let ((cluster-info (car cluster-list)))
\t (let* ((frame (gtk-frame-new #f))
\t\t (vbox (gtk-vbox-new #f 2)))
\t (gtk-container-border-width frame 6)
\t (gtk-container-add frame vbox)
\t (gtk-box-pack-start inside-vbox frame #f #f 10)
\t (let ((go-to-cluster-button (gtk-button-new-with-label
\t\t\t\t\t (car cluster-info))))
\t (gtk-signal-connect go-to-cluster-button "clicked"
\t\t\t\t (lambda ()
\t\t\t\t (set-rotation-centre
\t\t\t\t (list-ref cluster-info 1)
\t\t\t\t (list-ref cluster-info 2)
\t\t\t\t (list-ref cluster-info 3))))
\t (gtk-box-pack-start vbox go-to-cluster-button #f #f 2)
\t ;; now we have a list of individual features:
\t (let ((features (list-ref cluster-info 4)))
\t\t (if (> (length features) 0)
\t\t (add-feature-buttons features vbox)))
\t (loop (cdr cluster-list) (+ count 1))))))))
(gtk-container-border-width outside-vbox 2)
(let ((ok-button (gtk-button-new-with-label " Close ")))
(gtk-box-pack-end outside-vbox ok-button #f #f 0)
(gtk-signal-connect ok-button "clicked"
\t\t\t (lambda args
\t\t\t (gtk-widget-destroy window))))
(gtk-widget-show-all window)))
;;
;;(molprobity-fascinating-clusters-things-gui
;; "Testing the GUI"
;; (list
;; (list "Active Site" (list 0 1 2 3 4))
;; (list "Worst First" (list 3 4 1 2 0)))
;; (list
;; (list "The first cluster"
;;\t11 12 15
;;\t(list
;;\t (list "A bad thing" 0.4 0.6 0.7 10 13 16)
;;\t (list "Another bad thing" 0.4 0.6 0.7 12 15 16)))
;; (list "Another cluster of baddies"
;;\t-11 12 15
;;\t(list
;;\t (list "A quite bad thing" 0.4 0.6 0.7 -10 -13 16)
;;\t (list "A not so bad thing" 0.4 0.6 0.7 -12 -15 16)))
;; (list "A third cluster of baddies"
;;\t11 12 -15
;;\t(list
;;\t (list "A quite bad rotamer" 0.4 0.6 0.7 10 13 -16)
;;\t (list "A hydrogen clash" 0.4 0.6 0.7 12 15 -16)
;;\t (list "A not so bad H-H clash" 0.4 0.6 0.7 12 15 -16)))))
HEREDOC;
$schemeScript_py = <<<HEREDOC
def molprobity_fascinating_clusters_things_gui(window_name, sorting_option, cluster_list):
ncluster_max = 75
# a callback function
def callback_recentre(widget, x, y, z):
set_rotation_centre(x, y, z)
# utility function
def add_feature_buttons(feature_list, cluster_vbox):
frame = gtk.Frame("Cluster Features")
vbox = gtk.VBox(False, 0)
cluster_vbox.pack_start(frame, False, False, 2)
frame.add(vbox)
# add buttons to vbox for each feature
#
for feature in feature_list:
# print "feature: ", feature
button = gtk.Button(feature[0])
button.connect("clicked",
callback_recentre,
feature[4],
feature[5],
feature[6])
vbox.pack_start(button, False, False, 1)
# main body
window = gtk.Window(gtk.WINDOW_TOPLEVEL)
scrolled_win = gtk.ScrolledWindow()
outside_vbox = gtk.VBox(False, 2)
inside_vbox = gtk.VBox(False, 0)
print "Maximum number of clusters displayed: ", ncluster_max
window.set_default_size(300, 200)
window.set_title(window_name)
inside_vbox.set_border_width(2)
window.add(outside_vbox)
outside_vbox.pack_start(scrolled_win, True, True, 0) # expand fill padding
scrolled_win.add_with_viewport(inside_vbox)
scrolled_win.set_policy(gtk.POLICY_AUTOMATIC, gtk.POLICY_ALWAYS)
count = 0
for cluster_info in cluster_list:
if (count == ncluster_max):
break
else:
frame = gtk.Frame()
vbox = gtk.VBox(False, 2)
frame.set_border_width(6)
frame.add(vbox)
inside_vbox.pack_start(frame, False, False, 10)
go_to_cluster_button = gtk.Button(cluster_info[0])
go_to_cluster_button.connect("clicked",
callback_recentre,
cluster_info[1],
cluster_info[2],
cluster_info[3])
vbox.pack_start(go_to_cluster_button, False, False, 2)
# now we have a list of individual features:
features = cluster_info[4]
if (len(features) > 0):
add_feature_buttons(features, vbox)
outside_vbox.set_border_width(2)
ok_button = gtk.Button(" Close ")
outside_vbox.pack_end(ok_button, False, False, 0)
ok_button.connect("clicked", lambda x: window.destroy())
window.show_all()
HEREDOC;
//}}} 0. A lovely Scheme script written for us by Paul Emsley
//{{{ 1. For each outlier, create an array(cnit, description, r, g, b, x, y, z)
//$res_xyz = computeResCenters($infile, true);
$res_xyz = computeResCenters($infile);
$self_bads = array();
if (is_array($clash)) {
foreach ($clash['clashes'] as $cnit => $worst) {
$ctr = $res_xyz[$cnit];
$self_bads[] = array($cnit, "Clash at {$cnit} ({$worst} A)", 1, 0, 0.5, $ctr['x'], $ctr['y'], $ctr['z']);
}
}
if (is_array($rama)) {
foreach ($rama as $item) {
if ($item['eval'] == "OUTLIER") {
$cnit = $item['resName'];
$ctr = $res_xyz[$cnit];
$self_bads[] = array($cnit, "Ramachandran outlier {$cnit} ({$item['type']} {$item['scorePct']}%)", 0, 1, 0, $ctr['x'], $ctr['y'], $ctr['z']);
}
}
}
if (is_array($rota)) {
foreach ($rota as $item) {
if ($item['scorePct'] <= 1.0) {
$cnit = $item['resName'];
$ctr = $res_xyz[$cnit];
$self_bads[] = array($cnit, "Bad rotamer {$cnit} ({$item['scorePct']}%)", 1, 0.7, 0, $ctr['x'], $ctr['y'], $ctr['z']);
}
}
}
if (is_array($cbdev)) {
foreach ($cbdev as $item) {
if ($item['dev'] >= 0.25) {
$cnit = $item['resName'];
$ctr = $res_xyz[$cnit];
$self_bads[] = array($cnit, "C-beta deviation {$cnit} ({$item['dev']} A)", 0.5, 0, 1, $ctr['x'], $ctr['y'], $ctr['z']);
}
}
}
if (is_array($pperp)) {
foreach ($pperp as $item) {
if ($item['outlier']) {
$cnit = $item['resName'];
$ctr = $res_xyz[$cnit];
$reasons = array();
if ($item['deltaOut']) {
$reasons[] = "base-phosphate distance";
}
if ($item['epsilonOut']) {
$reasons[] = "bad epsilon angle";
}
$self_bads[] = array($cnit, "Wrong sugar pucker {$cnit} (" . implode(', ', $reasons) . ")", 0.5, 0, 1, $ctr['x'], $ctr['y'], $ctr['z']);
}
}
}
//}}} 1. For each outlier, create an array(cnit, description, r, g, b, x, y, z)
//{{{ 2. Cluster the outliers, somehow
//echo "self_bads has ".count($self_bads)." elements\n";
$range = 12;
// a fairly arbitrary value, in Angstroms.
$range2 = $range * $range;
$worst_res = array();
// cnit => array( bad1, bad2, ... )
$local_bads = array();
$startTime = time();
echo "starting cootclusteredchart\n";
foreach ($res_xyz as $cnit => $xyz) {
#$local_bads[$cnit] = array();
foreach ($self_bads as $idx => $a_bad) {
if (preg_match('/' . $a_bad[0] . '/', $cnit)) {
$res_bads[$cnit]++;
}
}
foreach ($self_bads as $idx => $a_bad) {
$cnit2 = $a_bad[0];
$dx = $xyz['x'] - $a_bad[5];
$dy = $xyz['y'] - $a_bad[6];
$dz = $xyz['z'] - $a_bad[7];
if ($dx * $dx + $dy * $dy + $dz * $dz <= $range2 && $res_bads[$cnit] != 0) {
if (preg_match('/(HOH|DOD|H20|D20|WAT|SOL|TIP|TP3|MTO|HOD|DOH)/', $cnit)) {
if (preg_match('/(HOH|DOD|H20|D20|WAT|SOL|TIP|TP3|MTO|HOD|DOH)/', $a_bad[0])) {
$local_bads[$cnit][$idx] = $a_bad;
}
} else {
$local_bads[$cnit][$idx] = $a_bad;
}
}
}
}
echo "first foreach loop took " . (time() - $startTime) . " seconds\nstarting whiletrue loop\ncycles:";
while (true) {
// Get worst residue from list and its count of bads
//$startTime = time();
uasort($local_bads, 'makeCootClusteredChart_cmp');
// put worst residue last
//echo "Iteration number ".$cycles."\n";
//foreach($self_bads as $key => $value)
//{
// echo $self_bads[$key][0]."\n";
//}
#foreach($local_bads as $key => $value)
#{
# echo $key." ".count($local_bads[$key])."\n";
#}
//echo "size of self_bads = ".count($self_bads)."\n";
#var_export($local_bads); echo "\n==========\n";
end($local_bads);
// go to last element
list($worst_cnit, $worst_bads) = each($local_bads);
// get last element
$bad_count = count($worst_bads);
// Only singletons left (for efficiency)
// Also ensures that singletons are listed under their "owner"
//if($bad_count <= 1)
//{
// foreach($self_bads as $idx => $a_bad)
// $worst_res[$a_bad[0]][$idx] = $a_bad;
// break;
//}
// else ...
#var_export($local_bads);
#echo "\nRemoving $worst_cnit with $bad_count bads...\n==========\n";
$worst_res[$worst_cnit] = $worst_bads;
// record it as the worst one this pass
// Discard all bads that went to making the worst, the worst;
// then re-run the algorithm to find the next worst, until no bads left.
foreach ($res_xyz as $cnit2 => $xyz) {
foreach ($worst_bads as $idx => $a_bad) {
unset($local_bads[$cnit2][$idx]);
//assure that once used, a residue can't be a new center
foreach ($self_bads as $idx2 => $a_bad2) {
unset($local_bads[$a_bad[0]][$idx2]);
}
unset($self_bads[$idx]);
}
}
if (count($self_bads) == 0) {
break;
}
$cycles++;
echo $cycles . " ";
//echo "end of while loop took ".(time() - $startTime)." seconds\n";
#if($cycles > 100) break;
}
//echo "number of cycles: ".$cycles."\n";
#var_export($worst_res); echo "\n==========\n";
//}}}
$out = fopen($outfile, 'wb');
$out_py = fopen($outfile_py, 'wb');
//scheme file
fwrite($out, ";\n; Multicriterion chart for " . basename($infile) . ", generated by MolProbity.\n");
fwrite($out, "; Open this in Coot using Calculate | Run Script...\n;\n");
fwrite($out, "\n\n" . $schemeScript . "\n\n");
fwrite($out, "(molprobity-fascinating-clusters-things-gui\n \"MolProbity Multi-Chart\"\n (list\n");
// this is where we write possible sort orders
//fwrite($out, " (list \"Worst First\" (list 0 1 2 3 4 5))\n");
fwrite($out, " )\n (list\n");
//python file
fwrite($out_py, "#\n# Multicriterion chart for " . basename($infile) . ", generated by MolProbity.\n");
fwrite($out_py, "# Open this in Coot using Calculate | Run Script...\n#\n");
fwrite($out_py, "\n\n" . $schemeScript_py . "\n\n");
fwrite($out_py, "molprobity_fascinating_clusters_things_gui(\n \"MolProbity Multi-Chart\",\n [],\n [");
// This is where we write clusters of outliers
$outlier_ctr = 0;
$loop_ctr = 0;
foreach ($worst_res as $cnit => $bads) {
$max = 0;
foreach ($bads as $b) {
//identify which residue has the most outliers in this group, make header name
if ($res_bads[$b[0]] > $max && !preg_match('/(HOH|DOD|H20|D20|WAT|SOL|TIP|TP3|MTO|HOD|DOH)/', $b[0])) {
$max = $res_bads[$b[0]];
$max_header = $b[0];
}
}
$xyz = $res_xyz[$cnit];
if (count($bads) > 1) {
fwrite($out, " (list\n \"problems near {$max_header}\"\n {$xyz['x']} {$xyz['y']} {$xyz['z']}\n (list\n");
fwrite($out_py, "[\"problems near {$max_header}\",\n {$xyz['x']}, {$xyz['y']}, {$xyz['z']},\n [\n");
foreach ($bads as $b) {
fwrite($out, " (list \"{$b['1']}\" {$b['2']} {$b['3']} {$b['4']} {$b['5']} {$b['6']} {$b['7']})\n");
fwrite($out_py, " [\"{$b['1']}\", {$b['2']}, {$b['3']}, {$b['4']}, {$b['5']}, {$b['6']}, {$b['7']}]");
#if($loop_ctr < count($worst_res)-1){
fwrite($out_py, ",\n");
#}
#else{
# fwrite($out_py, "\n");
#}
$outlier_ctr++;
}
fwrite($out, " )\n )\n");
fwrite($out_py, " ]\n ],\n ");
} else {
$b = reset($bads);
fwrite($out, " (list\n \"{$b['1']}\"\n {$xyz['x']} {$xyz['y']} {$xyz['z']}\n (list\n");
fwrite($out, " )\n )\n");
fwrite($out_py, "[\"{$b['1']}\", {$xyz['x']}, {$xyz['y']}, {$xyz['z']}, []]");
if ($loop_ctr < count($worst_res) - 1) {
fwrite($out_py, ",\n ");
} else {
fwrite($out_py, "\n ");
}
$outlier_ctr++;
}
$loop_ctr++;
}
fwrite($out, " )\n)\n");
fwrite($out_py, "])");
fclose($out);
fclose($out_py);
//echo "Making coot clustered chart took ".(time() - $startTime1)." seconds\n";
//echo "printed out ".$outlier_ctr." elements\n";
}
/**
* $outfile will be overwritten.
* $cnit is an array of CNIT codes for the residues that were processed.
*/
function makeSswingKin($pdb1, $pdb2, $outfile, $cnit)
{
if (file_exists($outfile)) {
unlink($outfile);
}
$stats = describePdbStats(pdbstat($pdb1), false);
$h = fopen($outfile, 'a');
fwrite($h, "@text\n");
fwrite($h, "Sidechains have been refit by SSWING. Details of the input file:\n\n");
foreach ($stats as $stat) {
fwrite($h, "[+] {$stat}\n");
}
fwrite($h, "@kinemage 1\n");
// Calculate views for each residue in CNIT
$ctr = computeResCenters($pdb1);
foreach ($cnit as $res) {
$i++;
$c = $ctr[$res];
fwrite($h, "@{$i}viewid {{$res}}\n@{$i}span 12\n@{$i}zslab 100\n@{$i}center {$c['x']} {$c['y']} {$c['z']}\n");
}
fclose($h);
exec("prekin -quiet -append -animate -onegroup -show 'mc,sc(peach),ca,hy,ht,wa' {$pdb1} >> {$outfile}");
exec("phenix.probe -quiet -noticks -nogroup -self 'alta' {$pdb1} >> {$outfile}");
//exec("probe -quiet -noticks -nogroup -self 'alta' $pdb1 >> $outfile");
exec("prekin -quiet -append -animate -onegroup -show 'mc,sc(sky),ca,hy,ht,wa' {$pdb2} >> {$outfile}");
exec("phenix.probe -quiet -noticks -nogroup -self 'alta' {$pdb2} >> {$outfile}");
//exec("probe -quiet -noticks -nogroup -self 'alta' $pdb2 >> $outfile");
}
/**
* Count the number of outliers that occur for this residue and other
* residues whose centroid is within some number of Anstroms of this one.
*
* $range is the max distance between two residue centroids
* $clash is the data structure from loadClashlist()
* $rama is the data structure from loadRamachandran()
* $rota is the data structure from loadRotamer()
* $cbdev is the data structure from loadCbetaDev()
* $pperp is the data structure from loadBasePhosPerp()
* Any of them can be set to null if the data is unavailable.
*/
function calcLocalBadness($infile, $range, $clash, $rama, $rota, $cbdev, $pperp)
{
$res_xyz = computeResCenters($infile);
$self_bads = findAllOutliers($clash, $rama, $rota, $cbdev, $pperp);
#var_export($self_bads); echo "\n==========\n";
$range2 = $range * $range;
$worst_res = array();
//calculate all distances and build association matrix
foreach ($res_xyz as $cnit => $xyz) {
foreach ($self_bads as $cnit2 => $bads2) {
$xyz2 = $res_xyz[$cnit2];
$dx = $xyz['x'] - $xyz2['x'];
$dy = $xyz['y'] - $xyz2['y'];
$dz = $xyz['z'] - $xyz2['z'];
if ($dx * $dx + $dy * $dy + $dz * $dz <= $range2 && $self_bads[$cnit] != 0) {
$local_mat[$cnit][$cnit2] = 1;
}
}
}
while (true) {
//at each iteration count of how bad each case is
$local_bads = array();
foreach ($res_xyz as $cnit => $xyz) {
foreach ($self_bads as $cnit2 => $bads2) {
if ($local_mat[$cnit][$cnit2] == 1 && !preg_match('/(HOH|DOD|H20|D20|WAT|SOL|TIP|TP3|MTO|HOD|DOH)/', $cnit)) {
$local_bads[$cnit] += $bads2;
}
}
}
// Get worst residue from list and its count of bads
asort($local_bads);
// put worst residue last
#var_export($local_bads); echo "\n==========\n";
end($local_bads);
// go to last element
list($worst_cnit, $bad_count) = each($local_bads);
// get last element
// Only singletons left (for efficiency)
// Also ensures that singletons are listed under their "owner"
if ($bad_count <= 1) {
foreach ($self_bads as $cnit => $bads) {
if ($bads > 0) {
$worst_res[$cnit] = $bads;
}
}
break;
}
// else ...
#var_export($local_bads);
#echo "\nRemoving $worst_cnit with $bad_count bads...\n==========\n";
$worst_res[$worst_cnit] = $bad_count;
// record it as the worst one this pass
// Discard all bads that went to making the worst, the worst;
// then re-run the algorithm to find the next worst, until no bads left.
$cnit = $worst_cnit;
#$xyz = $res_xyz[$cnit];
$leftover_bads = 0;
foreach ($self_bads as $cnit2 => $bads) {
if ($local_mat[$cnit][$cnit2] == 1) {
unset($self_bads[$cnit2]);
// faster than 0 -- won't traverse again
unset($local_bads[$cnit2]);
}
}
if (count($self_bads) == 0) {
break;
}
//limit the number of cycles to 25
$cycles++;
if ($cycles > 25) {
break;
}
}
#var_export($worst_res); echo "\n==========\n";
return $worst_res;
}
