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