BIO Assignment Week 5

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Assignment for Week 5
Sequence alignment

Note! This assignment is currently inactive. Major and minor unannounced changes may be made at any time.

 
 

Concepts and activities (and reading, if applicable) for this assignment will be topics on next week's quiz.


From A1:

  • install the molecular graphics viewer UCSF Chimera[1] on your own computer, work through a tutorial on its use and begin practicing the skill of viewing split-screen stereographic scenes without aids;

Molecular graphics

A molecular viewer is a program that takes protein structure data and allows you to display and explore it. For a number of reasons, I chose to use the UCSF Chimera viewer for this course.


UCSF Chimera

Task:

  • Access the Chimera page.
  • Install the program as per the instructions in the section: "Installing Chimera".
  • Access the Chimera User's Guide tutorial section. The "Getting Started" tutorial is offered in two versions: one for work with the graphical user interface (GUI), i.e. the usual system of windows and drop-down menu selections. The other is a command-line version for the same. What is the difference? In general, GUI interfaces (Menu version) are well suited for beginners who are not yet familiar with all the options. Having the commands and alternatives presented on a menu makes first steps very easy through simple selection of keywords. On the other hand, work from command line interfaces is much faster and more flexible if you know what you are doing and thus much better suited for the experienced user. It is also quite straightforward to execute series of commands in stored scripts, allowing you to automate tasks. For now, we will stay with the menu version but we will use commands later in the course and you are of course welcome to explore.
  • Work through the Chimera tutorial Getting Started - Menu version, Part 1.

Stereo vision

Task:

Access the Stereo Vision tutorial and practice viewing molecular structures in stereo.

Practice at least ...

  • two times daily,
  • for 3-5 minutes each session,

Keep up your practice throughout the course. Stereo viewing will be required in the final exam, but more importantly, it is a wonderful skill that will greatly support any activity of yours related to structural molecular biology. Practice with different molecules and try out different colours and renderings.

Note: do not go through your practice sessions mechanically. If you are not making any progress with stereo vision, contact me so we can help you on the right track.







 

Introduction

In this assignment we will perform an optimal global and local sequence alignment, and use R to plot the alignment quality as a colored bar-graph.


Optimal sequence alignments

Online programs for optimal sequence alignment are part of the EMBOSS tools. The programs take FASTA files as input.

Local optimal SEQUENCE alignment "water"

Task:

  1. Retrieve the FASTA file for the YFO Mbp1 protein and for Saccharomyces cerevisiae.
  2. Save the files as text files to your computer, (if you haven't done so already). You could give them an extension of .fa.
  3. Access the EMBOSS Explorer site (if you haven't done so yet, you might want to bookmark it.)
  4. Look for ALIGNMENT LOCAL, click on water, paste your FASTA sequences and run the program with default parameters.
  5. Study the results. You will probably find that the alignment extends over most of the protein, but does not include the termini.
  6. Considering the sequence identy cutoff we discussed in class (25% over the length of a domain), do you believe that the APSES domains are homologous?
  7. Change the Gap opening and Gap extension parameters to high values (e.g. 30 and 5). Then run the alignment again.
  8. Note what is different.
  9. You could try getting only an alignment for the ankyrin domains that you have found in the last assignment, by deleting the approximate region of the APSES domains from your input.


Global optimal SEQUENCE alignment "needle"

Task:

  1. Look for ALIGNMENT GLOBAL, click on needle, paste your FASTA sequences and run the program with default parameters.
  2. Study the results. You will find that the alignment extends over the entire protein, likely with long indels at the termini.
  3. Change the Output alignment format to FASTA pairwise simple, to retrieve the aligned FASTA files with indels.
  4. Copy the aligned sequences (with indels) and save them to your computer. You could give them an extension of .fal to remind you that they are aligned FASTA sequences.


 

The Mutation Data Matrix

The NCBI makes its alignment matrices available by ftp. They are located at ftp://ftp.ncbi.nih.gov/blast/matrices - for example here is a link to the BLOSUM62 matrix[2]. Access that site and download the BLOSUM62 matrix to your computer. You could give it a filename of BLOSUM62.mdm.

It should look like this. 

#  Matrix made by matblas from blosum62.iij
#  * column uses minimum score
#  BLOSUM Clustered Scoring Matrix in 1/2 Bit Units
#  Blocks Database = /data/blocks_5.0/blocks.dat
#  Cluster Percentage: >= 62
#  Entropy =   0.6979, Expected =  -0.5209
   A  R  N  D  C  Q  E  G  H  I  L  K  M  F  P  S  T  W  Y  V  B  Z  X  *
A  4 -1 -2 -2  0 -1 -1  0 -2 -1 -1 -1 -1 -2 -1  1  0 -3 -2  0 -2 -1  0 -4 
R -1  5  0 -2 -3  1  0 -2  0 -3 -2  2 -1 -3 -2 -1 -1 -3 -2 -3 -1  0 -1 -4 
N -2  0  6  1 -3  0  0  0  1 -3 -3  0 -2 -3 -2  1  0 -4 -2 -3  3  0 -1 -4 
D -2 -2  1  6 -3  0  2 -1 -1 -3 -4 -1 -3 -3 -1  0 -1 -4 -3 -3  4  1 -1 -4 
C  0 -3 -3 -3  9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 
Q -1  1  0  0 -3  5  2 -2  0 -3 -2  1  0 -3 -1  0 -1 -2 -1 -2  0  3 -1 -4 
E -1  0  0  2 -4  2  5 -2  0 -3 -3  1 -2 -3 -1  0 -1 -3 -2 -2  1  4 -1 -4 
G  0 -2  0 -1 -3 -2 -2  6 -2 -4 -4 -2 -3 -3 -2  0 -2 -2 -3 -3 -1 -2 -1 -4 
H -2  0  1 -1 -3  0  0 -2  8 -3 -3 -1 -2 -1 -2 -1 -2 -2  2 -3  0  0 -1 -4 
I -1 -3 -3 -3 -1 -3 -3 -4 -3  4  2 -3  1  0 -3 -2 -1 -3 -1  3 -3 -3 -1 -4 
L -1 -2 -3 -4 -1 -2 -3 -4 -3  2  4 -2  2  0 -3 -2 -1 -2 -1  1 -4 -3 -1 -4 
K -1  2  0 -1 -3  1  1 -2 -1 -3 -2  5 -1 -3 -1  0 -1 -3 -2 -2  0  1 -1 -4 
M -1 -1 -2 -3 -1  0 -2 -3 -2  1  2 -1  5  0 -2 -1 -1 -1 -1  1 -3 -1 -1 -4 
F -2 -3 -3 -3 -2 -3 -3 -3 -1  0  0 -3  0  6 -4 -2 -2  1  3 -1 -3 -3 -1 -4 
P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4  7 -1 -1 -4 -3 -2 -2 -1 -2 -4 
S  1 -1  1  0 -1  0  0  0 -1 -2 -2  0 -1 -2 -1  4  1 -3 -2 -2  0  0  0 -4 
T  0 -1  0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1  1  5 -2 -2  0 -1 -1  0 -4 
W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1  1 -4 -3 -2 11  2 -3 -4 -3 -2 -4 
Y -2 -2 -2 -3 -2 -1 -2 -3  2 -1 -1 -2 -1  3 -3 -2 -2  2  7 -1 -3 -2 -1 -4 
V  0 -3 -3 -3 -1 -2 -2 -3 -3  3  1 -2  1 -1 -2 -2  0 -3 -1  4 -3 -2 -1 -4 
B -2 -1  3  4 -3  0  1 -1  0 -3 -4  0 -3 -3 -2  0 -1 -4 -3 -3  4  1 -1 -4 
Z -1  0  0  1 -3  3  4 -2  0 -3 -3  1 -1 -3 -1  0 -1 -3 -2 -2  1  4 -1 -4 
X  0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2  0  0 -2 -1 -1 -1 -1 -1 -4 
* -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4  1


Task:

  • Study this and make sure you understand what this table is, how it can be used, and what a reasonable range of values for identities and pairscores for non-identical, similar and dissimilar residues is. Ask on the mailing list in case you have questions.


 

The DNA binding site

Now, that you know how YFO Mbp1 aligns with yeast Mbp1, you can evaluate functional conservation in these homologous proteins. You probably already downloaded the two Biochemistry papers by Taylor et al. (2000) and by Deleeuw et al. (2008) that we encountered in Assignment 2. These discuss the residues involved in DNA binding[3]. In particular the residues between 50-74 have been proposed to comprise the DNA recognition domain.

Task:

  1. Using the APSES domain alignment you have just constructed, find the YFO Mbp1 residues that correspond to the range 50-74 in yeast.
  2. Note whether the sequences are especially highly conserved in this region.
  3. Using Chimera, look at the region. Use the sequence window to make sure that the sequence numbering between the paper and the PDB file are the same (they are often not identical!). Then select the residues - the proposed recognition domain - and color them differently for emphasis. Study this in stereo to get a sense of the spatial relationships. Check where the conserved residues are.
  4. A good representation is stick - but other representations that include sidechains will also serve well.
  5. Calculate a solvent accessible surface of the protein in a separate representation and make it transparent.
  6. You could combine three representations: (1) the backbone (in ribbon view), (2) the sidechains of residues that presumably contact DNA, distinctly colored, and (3) a transparent surface of the entire protein. This image should show whether residues annotated as DNA binding form a contiguous binding interface.


DNA binding interfaces are expected to comprise a number of positively charged amino acids, that might form salt-bridges with the phosphate backbone.


Task:

  • Study and consider whether this is the case here and which residues might be included.


 

R code: coloring the alignment by quality

Task:

  • Study this code carefully, execute it, section by section and make sure you understand all of it. Ask on the list if anything is not clear.
# BiostringsExample.R
# Short tutorial on sequence alignment with the Biostrings package.
# Boris Steipe for BCH441, 2013 - 2014
#
setwd("~/path/to/your/R_files/")
setwd("~/Documents/07.TEACHING/37-BCH441 Bioinformatics 2014/05-Materials/Assignment_5 data")

# Biostrings is a package within the bioconductor project.
# bioconducter packages have their own installation system,
# they are normally not installed via CRAN.

# First, you load the BioConductor installer...
source("http://bioconductor.org/biocLite.R")

# Then you can install the Biostrings package and all of its dependencies.
biocLite("Biostrings")

# ... and load the library.
library(Biostrings)

# Some basic (technical) information is available ...
library(help=Biostrings)

# ... but for more in depth documentation, use the
# so called "vignettes" that are provided with every R package.
browseVignettes("Biostrings")

# In this code, we mostly use functions that are discussed in the 
# pairwise alignement vignette.

# Read in two fasta files - you will need to edit this for YFO
sacce <- readAAStringSet("mbp1-sacce.fa", format="fasta")

# "USTMA" is used only as an example here - modify for YFO  :-)
ustma <- readAAStringSet("mbp1-ustma.fa", format="fasta")

sacce
names(sacce) 
names(sacce) <- "Mbp1 SACCE"
names(ustma) <- "Mbp1 USTMA" # Example only ... modify for YFO

width(sacce)
as.character(sacce)

# Biostrings takes a sophisticated approach to sequence alignment ...
?pairwiseAlignment

# ... but the use in practice is quite simple:
ali <- pairwiseAlignment(sacce, ustma, substitutionMatrix = "BLOSUM50")
ali

pattern(ali)
subject(ali)

writePairwiseAlignments(ali)

p <- aligned(pattern(ali))
names(p) <- "Mbp1 SACCE aligned"
s <- aligned(subject(ali))
names(s) <- "Mbp1 USTMA aligned"

# don't overwrite your EMBOSS .fal files
writeXStringSet(p, "mbp1-sacce.R.fal", append=FALSE, format="fasta")
writeXStringSet(s, "mbp1-ustma.R.fal", append=FALSE, format="fasta")

# Done.
  • Compare the alignments you received from the EMBOSS server, and that you co puted using R. Are they aproximately the same? Exactly? You did use different matrices and gap aameters, so minor differences are to be expected. But by and large you should get the same alignments.

We will now use the aligned sequences to compute a graphical display of alignment quality.


Task:

  • Study this code carefully, execute it, section by section and make sure you understand all of it. Ask on the list if anything is not clear.
# aliScore.R
# Evaluating an alignment with a sliding window score
# Boris Steipe, October 2012. Update October 2013
setwd("~/path/to/your/R_files/")

# Scoring matrices can be found at the NCBI. 
# ftp://ftp.ncbi.nih.gov/blast/matrices/BLOSUM62

# It is good practice to set variables you might want to change
# in a header block so you don't need to hunt all over the code
# for strings you need to update.
#
fa1      <- "mbp1-sacce.R.fal"
fa2      <- "mbp1-ustma.R.fal"
code1    <- "SACCE"
code2    <- "USTMA"
mdmFile  <- "BLOSUM62.mdm"
window   <- 9   # window-size (should be an odd integer)

# ================================================
#    Read data files
# ================================================

# read fasta datafiles using seqinr function read.fasta()
install.packages("seqinr")
library(seqinr)
tmp  <- unlist(read.fasta(fa1, seqtype="AA", as.string=FALSE, seqonly=TRUE))
seq1 <- unlist(strsplit(as.character(tmp), split=""))

tmp  <- unlist(read.fasta(fa2, seqtype="AA", as.string=FALSE, seqonly=TRUE))
seq2 <- unlist(strsplit(as.character(tmp), split=""))

if (length(seq1) != length(seq2)) {
	print("Error: Sequences have unequal length!")
	}
	
lSeq <- length(seq1)

# ================================================
#    Read scoring matrix
# ================================================

MDM <- read.table(mdmFile, skip=6)

# This is a dataframe. Study how it can be accessed:

MDM
MDM[1,]
MDM[,1]
MDM[5,5]   # Cys-Cys
MDM[20,20] # Val-Val
MDM[,"W"]  # the tryptophan column
MDM["R","W"]  # Arg-Trp pairscore
MDM["W","R"]  # Trp-Arg pairscore: pairscores are symmetric

colnames(MDM)  # names of columns
rownames(MDM)  # names of rows
colnames(MDM)[3]   # third column
rownames(MDM)[12]  # twelfth row

# change the two "*" names to "-" so we can use them to score
# indels of the alignment. This is a bit of a hack, since this
# does not reflect the actual indel penalties (which is, as you)
# remember from your lectures, calculated as a gap opening
# + gap extension penalty; it can't be calculated in a pairwise
# manner) EMBOSS defaults for BLODSUM62 are opening -10 and
# extension -0.5 i.e. a gap of size 3 (-11.5) has approximately
# the same penalty as a 3-character score of "-" matches (-12)
# so a pairscore of -4 is not entirely unreasonable.

colnames(MDM)[24] 
rownames(MDM)[24]
colnames(MDM)[24] <- "-"
rownames(MDM)[24] <- "-"
colnames(MDM)[24] 
rownames(MDM)[24]
MDM["Q", "-"]
MDM["-", "D"]
# so far so good.

# ================================================
#    Tabulate pairscores for alignment
# ================================================


# It is trivial to create a pairscore vector along the
# length of the aligned sequences.

PS <- vector()
for (i in 1:lSeq) {
   aa1 <- seq1[i] 
   aa2 <- seq2[i] 
   PS[i] = MDM[aa1, aa2]
}

PS


# The same vector could be created - albeit perhaps not so
# easy to understand - with the expression ...
MDM[cbind(seq1,seq2)]



# ================================================
#    Calculate moving averages
# ================================================

# In order to evaluate the alignment, we will calculate a 
# sliding window average over the pairscores. Somewhat surprisingly
# R doesn't (yet) have a native function for moving averages: options
# that are quoted are:
#   - rollmean() in the "zoo" package http://rss.acs.unt.edu/Rdoc/library/zoo/html/rollmean.html
#   - MovingAverages() in "TTR"  http://rss.acs.unt.edu/Rdoc/library/TTR/html/MovingAverages.html
#   - ma() in "forecast"  http://robjhyndman.com/software/forecast/
# But since this is easy to code, we shall implement it ourselves.

PSma <- vector()           # will hold the averages
winS <- floor(window/2)    # span of elements above/below the centre
winC <- winS+1             # centre of the window

# extend the vector PS with zeros (virtual observations) above and below
PS <- c(rep(0, winS), PS , rep(0, winS))

# initialize the window score for the first position
winScore <- sum(PS[1:window])

# write the first score to PSma
PSma[1] <- winScore

# Slide the window along the sequence, and recalculate sum()
# Loop from the next position, to the last position that does not exceed the vector...
for (i in (winC + 1):(lSeq + winS)) { 
   # subtract the value that has just dropped out of the window
   winScore <- winScore - PS[(i-winS-1)] 
   # add the value that has just entered the window
   winScore <- winScore + PS[(i+winS)]  
   # put score into PSma
   PSma[i-winS] <- winScore
}

# convert the sums to averages
PSma <- PSma / window

# have a quick look at the score distributions

boxplot(PSma)
hist(PSma)

# ================================================
#    Plot the alignment scores
# ================================================

# normalize the scores 
PSma <- (PSma-min(PSma))/(max(PSma) - min(PSma) + 0.0001)
# spread the normalized values to a desired range, n
nCol <- 10
PSma <- floor(PSma * nCol) + 1

# Assign a colorspectrum to a vector (with a bit of colormagic,
# don't worry about that for now). Dark colors are poor scores,
# "hot" colors are high scores
spect <- colorRampPalette(c("black", "red", "yellow", "white"), bias=0.4)(nCol)

# Color is an often abused aspect of plotting. One can use color to label
# *quantities* or *qualities*. For the most part, our pairscores measure amino
# acid similarity. That is a quantity and with the spectrum that we just defined
# we associte the measured quantities with the color of a glowing piece
# of metal: we start with black #000000, then first we ramp up the red
# (i.e. low-energy) part of the visible spectrum to red #FF0000, then we
# add and ramp up the green spectrum giving us yellow #FFFF00 and finally we 
# add blue, giving us white #FFFFFF. Let's have a look at the spectrum:

s <- rep(1, nCol)
barplot(s, col=spect, axes=F, main="Color spectrum")

# But one aspect of our data is not quantitatively different: indels.
# We valued indels with pairscores of -4. But indels are not simply poor alignment, 
# rather they are non-alignment. This means stretches of -4 values are really 
# *qualitatively* different. Let's color them differently by changing the lowest 
# level of the spectrum to grey.

spect[1] <- "#CCCCCC"
barplot(s, col=spect, axes=F, main="Color spectrum")

# Now we can display our alignment score vector with colored rectangles.

# Convert the integers in PSma to color values from spect
PScol <- vector()
for (i in 1:length(PSma)) {
	PScol[i] <- spect[ PSma[i] ]  # this is how a value from PSma is used as an index of spect
}

# Plot the scores. The code is similar to the last assignment.
# Create an empty plot window of appropriate size
plot(1,1, xlim=c(-100, lSeq), ylim=c(0, 2) , type="n", yaxt="n", bty="n", xlab="position in alignment", ylab="")

# Add a label to the left
text (-30, 1, adj=1, labels=c(paste("Mbp1:\n", code1, "\nvs.\n", code2)), cex=0.9 )

# Loop over the vector and draw boxes  without border, filled with color.
for (i in 1:lSeq) {
   rect(i, 0.9, i+1, 1.1, border=NA, col=PScol[i])
}

# Note that the numbers along the X-axis are not sequence numbers, but numbers
# of the alignment, i.e. sequence number + indel length. That is important to
# realize: if you would like to add the annotations from the last assignment 
# which I will leave as an exercise, you need to map your sequence numbering
# into alignment numbering. Let me know in case you try that but need some help.


That is all.


 

Links and resources

 

 


Footnotes and references

  1. ↑ * Previous versions of this course have used the VMD molecular viewer. Material on this is still available at the VMD page.
  2. ↑ The directory also contains sourcecode to generte the PAM matrices. This may be of interest for you if you ever want to produce scoring matrices from your own datasets.
  3. ↑ (Taylor et al. (2000) Biochemistry 39: 3943-3954 and Deleeuw et al. (2008) Biochemistry. 47:6378-6385)


 

Ask, if things don't work for you!

If anything about the assignment is not clear to you, please ask on the mailing list. You can be certain that others will have had similar problems. Success comes from joining the conversation.




 

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