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hyginn 2019-11-14 22:44:07 -05:00
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RPR-SX-PDB.R
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@ -3,12 +3,13 @@
# Purpose: A Bioinformatics Course: # Purpose: A Bioinformatics Course:
# R code accompanying the RPR-SX-PDB unit. # R code accompanying the RPR-SX-PDB unit.
# #
# Version: 1.1 # Version: 1.2
# #
# Date: 2017 10 - 2019 01 # Date: 2017 10 - 2019 11
# Author: Boris Steipe (boris.steipe@utoronto.ca) # Author: Boris Steipe (boris.steipe@utoronto.ca)
# #
# Versions: # Versions:
# 1.2 Maintenance
# 1.1 Change from require() to requireNamespace(), # 1.1 Change from require() to requireNamespace(),
# use <package>::<function>() idiom throughout # use <package>::<function>() idiom throughout
# 1.0 First live version, completely refactores 2016 code # 1.0 First live version, completely refactores 2016 code
@ -30,22 +31,22 @@
#TOC> ========================================================================== #TOC> ==========================================================================
#TOC> #TOC>
#TOC> Section Title Line #TOC> Section Title Line
#TOC> ---------------------------------------------------------- #TOC> ----------------------------------------------------------
#TOC> 1 Introduction to the bio3D package 61 #TOC> 1 Introduction to the bio3D package 62
#TOC> 2 A Ramachandran plot 152 #TOC> 2 A Ramachandran plot 153
#TOC> 3 Density plots 228 #TOC> 3 Density plots 229
#TOC> 3.1 Density-based colours 242 #TOC> 3.1 Density-based colours 243
#TOC> 3.2 Plotting with smoothScatter() 261 #TOC> 3.2 Plotting with smoothScatter() 262
#TOC> 3.3 Plotting hexbins 276 #TOC> 3.3 Plotting hexbins 277
#TOC> 3.4 Plotting density contours 304 #TOC> 3.4 Plotting density contours 305
#TOC> 3.4.1 ... as overlay on a colored grid 337 #TOC> 3.4.1 ... as overlay on a coloured grid 338
#TOC> 3.4.2 ... as filled countour 354 #TOC> 3.4.2 ... as filled countour 355
#TOC> 3.4.3 ... as a perspective plot 385 #TOC> 3.4.3 ... as a perspective plot 386
#TOC> 4 cis-peptide bonds 403 #TOC> 4 cis-peptide bonds 404
#TOC> 5 H-bond lengths 418 #TOC> 5 H-bond lengths 419
#TOC> #TOC>
#TOC> ========================================================================== #TOC> ==========================================================================
@ -166,7 +167,7 @@ abline(v = 0, lwd = 0.5, col = "#00000044")
# quadrant of the plot. This combination of phi-psi angles defines # quadrant of the plot. This combination of phi-psi angles defines
# the conformation of a left-handed alpha helix and is generally # the conformation of a left-handed alpha helix and is generally
# only observed for glycine residues. Let's replot the data, but # only observed for glycine residues. Let's replot the data, but
# color the points for glycine residues differently. First, we # colour the points for glycine residues differently. First, we
# get a vector of glycine residue indices in the structure: # get a vector of glycine residue indices in the structure:
mySeq <- bio3d::pdbseq(apses) mySeq <- bio3d::pdbseq(apses)
@ -242,7 +243,7 @@ for (i in 1:nrow(dat)) {
# == 3.1 Density-based colours ============================================= # == 3.1 Density-based colours =============================================
# A first approximation to scatterplots that visualize the density of the # A first approximation to scatterplots that visualize the density of the
# underlying distribution is coloring via the densCols() function. # underlying distribution is colouring via the densCols() function.
?densCols ?densCols
iNA <- c(which(is.na(tor$phi)), which(is.na(tor$psi))) iNA <- c(which(is.na(tor$phi)), which(is.na(tor$psi)))
phi <- tor$phi[-iNA] phi <- tor$phi[-iNA]
@ -334,7 +335,7 @@ str(dPhiPsi)
contour(dPhiPsi) contour(dPhiPsi)
# === 3.4.1 ... as overlay on a colored grid # === 3.4.1 ... as overlay on a coloured grid
image(dPhiPsi, image(dPhiPsi,
col = myColorRamp(100), col = myColorRamp(100),
@ -351,7 +352,7 @@ abline(h = 0, lwd = 0.5, col = "#00000044")
abline(v = 0, lwd = 0.5, col = "#00000044") abline(v = 0, lwd = 0.5, col = "#00000044")
# === 3.4.2 ... as filled countour # === 3.4.2 ... as filled countour
filled.contour(dPhiPsi, filled.contour(dPhiPsi,
xlim = c(-180, 180), ylim = c(-180, 180), xlim = c(-180, 180), ylim = c(-180, 180),
@ -382,7 +383,7 @@ filled.contour(dPhiPsi,
abline(v = 0, lwd = 0.5, col = "#00000044") abline(v = 0, lwd = 0.5, col = "#00000044")
}) })
# === 3.4.3 ... as a perspective plot # === 3.4.3 ... as a perspective plot
persp(dPhiPsi, persp(dPhiPsi,
xlab = "phi", xlab = "phi",
@ -633,7 +634,7 @@ hist(dH)
hist(dE) hist(dE)
# add color: # add colour:
hist(dH, col="#DD0055") hist(dH, col="#DD0055")
hist(dE, col="#00AA70") hist(dE, col="#00AA70")
@ -653,7 +654,7 @@ hist(dH, col="#DD0055")
hist(dE, col="#00AA70", add=TRUE) hist(dE, col="#00AA70", add=TRUE)
# We see that the leftmost column of the sheet bonds hides the helix bonds in # We see that the leftmost column of the sheet bonds hides the helix bonds in
# that column. Not good. But we can make the colors transparent! We just need to # that column. Not good. But we can make the colours transparent! We just need to
# add a fourth set of two hexadecimal-numbers to the #RRGGBB triplet. Lets use # add a fourth set of two hexadecimal-numbers to the #RRGGBB triplet. Lets use
# 2/3 transparent, in hexadecimal, 1/3 of 256 is x55 - i.e. an RGB triplet # 2/3 transparent, in hexadecimal, 1/3 of 256 is x55 - i.e. an RGB triplet
# specied as #RRGGBB55 is only 33% opaque: # specied as #RRGGBB55 is only 33% opaque:
@ -712,7 +713,7 @@ legend("topright",
# it is easy to try this with a larger protein. # it is easy to try this with a larger protein.
# 3ugj for example is VERY large. # 3ugj for example is VERY large.
pdb <- read.pdb("3ugj") pdb <- bio3d::read.pdb("3ugj")
# helices... # helices...
iN <- ssSelect(pdb, ssType = c("helix"), myElety = "N") iN <- ssSelect(pdb, ssType = c("helix"), myElety = "N")
@ -769,7 +770,7 @@ dH <- c() # collect all helix H-bonds here
dE <- c() # collect all sheet H-bonds here dE <- c() # collect all sheet H-bonds here
for (i in seq_along(myPDBs)) { for (i in seq_along(myPDBs)) {
pdb <- read.pdb(myPDBs[i]) pdb <- bio3d::read.pdb(myPDBs[i])
# helices... # helices...
iN <- ssSelect(pdb, ssType = c("helix"), myElety = "N") iN <- ssSelect(pdb, ssType = c("helix"), myElety = "N")
@ -786,7 +787,7 @@ for (i in seq_along(myPDBs)) {
# Inspect the results # Inspect the results
length(dH) # 4415 (your numbers are different, but it should be a lot) length(dH) # 4415 (your numbers are different, but there should be many)
length(dE) # 262 length(dE) # 262
brk=seq(2.0, 4.0, 0.1) brk=seq(2.0, 4.0, 0.1)