stylisitc updates
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RPR-SX-PDB.R
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RPR-SX-PDB.R
@ -9,13 +9,14 @@
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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: 1.2
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# Version: 1.3
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#
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# Date: 2017 10 - 2019 11
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# Date: 2017-10 - 2020-09
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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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# 1.3 2020 Maintenance
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# 1.2 2019 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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@ -57,9 +58,9 @@
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# In this example of protein structure interpretation, we ...
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# - load the library "bio3D" which supports work with
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# - download the package bio3D:: which supports work with
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# protein structure files,
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# - explore some elementary functions of the library
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# - explore some elementary functions of the package
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# - explore plotting of density values with scatterplots
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# - assemble a script to see whether H-bond lengths systematically differ
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# between alpha-helical and beta-sheet structures.
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@ -77,7 +78,7 @@ if (! requireNamespace("bio3d", quietly = TRUE)) {
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# data(package = "bio3d") # available datasets
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# bio3d can load molecules directly from the PDB servers, you don't _have_ to
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# bio3d:: can load molecules directly from the PDB servers, you don't _have_ to
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# store them locally, but you could.
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#
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# But before you _load_ a file, have a look what such a file contains. I have
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@ -87,15 +88,15 @@ file.show("./data/1BM8.pdb")
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# Have a look at the header section, the atom records, the coordinate records
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# etc.
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#
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# Task: Answer the following questions:
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#
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# What is the resolution of the structure?
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# Is the first residue in the SEQRES section also the first residue
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# with an ATOM record?
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# How many water molecules does the structure contain?
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# Can you explain REMARK 525 regarding HOH 459 and HOH 473?
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# Are all atoms of the N-terminal residue present?
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# Are all atoms of the C-terminal residue present?
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# Task:
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# Answer the following questions:
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# What is the resolution of the structure?
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# Is the first residue in the SEQRES section also the first residue
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# with an ATOM record?
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# How many water molecules does the structure contain?
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# Can you explain REMARK 525 regarding HOH 459 and HOH 473?
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# Are all atoms of the N-terminal residue present?
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# Are all atoms of the C-terminal residue present?
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apses <- bio3d::read.pdb("1bm8") # load a molecule directly from the PDB via
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# the Internet.
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@ -119,7 +120,7 @@ str(apses)
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i <- 5
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apses$atom[i,]
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apses$atom[i, c("x", "y", "z")] # here we are selecting with column names!
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apses$xyz[c(i*3-2, i*3-1, i*3)] # here we are selcting with row numbers
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apses$xyz[c(i*3-2, i*3-1, i*3)] # here we are selecting with row numbers
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# all atoms of a residue ...
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i <- 48
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@ -140,11 +141,12 @@ length(apses$seqres)
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length(apses$atom$resid[apses$calpha])
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# Are the sequences the same?
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sum(apses$seqres == apses$atom$resid[apses$calpha])
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any(apses$seqres != apses$atom$resid[apses$calpha])
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# get a list of all CA atoms of arginine residues
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cols <- c("eleno", "elety", "resid", "chain", "resno", "insert")
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sel <- apses$atom$resid == "ARG" & apses$atom$elety == "CA"
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apses$atom[sel, c("eleno", "elety", "resid", "chain", "resno", "insert")]
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apses$atom[sel, cols]
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# The introduction to bio3d tutorial at
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# http://thegrantlab.org/bio3d/tutorials/structure-analysis
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@ -647,33 +649,34 @@ hist(dE, col="#00AA70")
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# For better comparison, plot both in the
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# same window:
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# same window (use the "add = TRUE" parameter in hist() ):
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hist(dH, col="#DD0055")
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hist(dE, col="#00AA70", add=TRUE)
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hist(dE, col="#00AA70", add = TRUE)
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# ... oops, we dind't reset the graphics parameters. You can either close the
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# window, a new window will open with default parameters, or ...
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# ... oops, we dind't reset the graphics parameters. You can either use
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# the broom icon of the Plot tab to clear asll plots, or ...
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par(opar) # ... reset the graphics parameters
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hist(dH, col="#DD0055")
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hist(dE, col="#00AA70", add=TRUE)
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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 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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# add a fourth set of two hexadecimal-numbers to the #RRGGBB triplet. (This
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# sets the transparency of the colour, it is called the "alpha channel"). Lets
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# use 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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hist(dH, col="#DD005555")
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hist(dE, col="#00AA7055", add=TRUE)
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hist(dH, col = "#DD005555")
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hist(dE, col = "#00AA7055", add = TRUE)
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# To finalize the plots, let's do two more things: Explicitly define the breaks,
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# to make sure they match up - otherwise they would not need to... like in this
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# example:
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hist(dH, col="#DD005555")
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hist(dE[dE < 3], col="#00AA7055", add=TRUE)
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hist(dH, col ="#DD005555")
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hist(dE[dE < 3], col = "#00AA7055", add = TRUE)
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# Breaks are a parameter in hist() that can either be a scalar, to define how
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# many columns you want, or a vector, that defines the actual breakpoints.
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@ -687,9 +690,10 @@ hist(dE, col="#00AA7055", breaks=brk, add=TRUE)
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# impression of the relative frequency, but it is of course skewed by observing
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# relatively more or less of one type of secondary structure in a protein. As
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# part of the hist() function we can rescale the values so that the sum over all
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# is one: set the prameter freq=FALSE.
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# is one: set the parameter freq=FALSE. And we explicitly set y-axis limits
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# to accommodate bot distributions.
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hist(dH, col="#DD005555", breaks=brk, freq=FALSE)
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hist(dH, col="#DD005555", breaks=brk, freq=FALSE, ylim = c(0, 4))
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hist(dE, col="#00AA7055", breaks=brk, freq=FALSE, add=TRUE)
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# Adding labels and legend ...
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@ -757,7 +761,7 @@ legend('topright',
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border = NA,
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inset = 0.1)
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# It looks more and more that the distribution is indeed different. Our sample
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# It's apparent that the distributions are indeed different. Our sample
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# is large, but derives from a single protein. To do database scale statistics,
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# we should look at many more proteins. To give you a sense of how, let's do
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# this for just ten proteins, randomly selected from non-homologous,
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