This is a small R code for cleavage site specificity analysis
If you have any problems running the code you can check the following:
- pay attention to the conflicts between the packages used here and those you have appended.
- of course, check the names of your files psm.tsv, peptide.tsv and protein.fas. Sometimes people change names.
- never ever overwrite the original tsv files with modifications. Save as a new file instead.
⚠️ when importing your psm.tsv files, make sure you are using theclean_names()function fromjanitorpackage.
R packages you need to have installed and loaded:
library(tidyverse)
library(here)
library(janitor)
library(ComplexHeatmap)Functions you need to follow the code in this repository:
# to calculate the frequency of each amino acid in each column in percentage
aa_freq <- function(x) {
table(x) / length(x) * 100
}
# to impute missing amino acids with zero and reorder if necessary
complete_and_reorder_amino_acids <- function(element) {
# List of 20 amino acids
twenty_amino_acids <- c('A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y')
# Complete missing amino acids with zero
for (amino_acid in twenty_amino_acids) {
if (!amino_acid %in% names(element)) {
element[[amino_acid]] <- 0
}
}
# Reorder amino acids
element <- element[match(twenty_amino_acids, names(element))]
return(element)
}The simplest way to spilt the extended_peptide sequence keeping the 4 amino acids before and after the first and second dot into two separate columns. The first will be fingerprint_Nterm and the second fingerprint_Cterm. Once you imported the psm.tsv file you can create a fingerprint for the N-term and C-term with 4 amino acids on each side of the cleavage site. If you are using the latest version of FragPipe, you don't need to map the peptides to proteins in fasta file anymore, because you have the column extended peptide.
❗Remember that the psm.tsv file contains all the PSM scored. Only a fraction of these PSM will be transfered to the peptide.tsv file. So, you may want to apply a filter in hyperscore column to select the best PSMs.
psm_file <- read_tsv("psm.tsv") %>%
janitor::clean_names() %>%
dplyr::mutate(
fingerprint_Nterm = case_when(
str_detect(extended_peptide, "^\\.") ~ "NA",
TRUE ~ substr(extended_peptide, 2, 16)
),
fingerprint_Cterm = substr(extended_peptide, nchar(extended_peptide) - 15, nchar(extended_peptide) - 2),
fingerprint_Nterm = str_extract(fingerprint_Nterm, ".{4}\\..{4}"),
fingerprint_Nterm = str_remove_all(fingerprint_Nterm, "\\."),
fingerprint_Cterm = str_extract(fingerprint_Cterm, ".{4}\\..{4}"),
fingerprint_Cterm = str_remove_all(fingerprint_Cterm, "\\.")
) %>%
relocate(extended_peptide, .before = fingerprint_Nterm)Extract the matrix of amino acids and calculate the frequency of each residue in each position (from P4 to P4').
# create a list of peptide sequences including the N-term and C-term fingerprints
fingerprint_protease <- c(psm_file$fingerprint_Nterm,
psm_file$fingerprint_Cterm) %>%
na.omit() %>%
strsplit("")
# create a matrix with the list of peptide sequences
mat_aa <- matrix(unlist(fingerprint_protease),
ncol = 8, byrow = TRUE)
# remove the rows containing "B" or any other unwanted amino acids in the matrix
# mat_aa <- mat_aa[!apply(mat_aa, 1,
function(x) any(x == "B")), ]
# calculate the frequency of each amino acid in each column and plot a heatmap
mat_aa_freq <- apply(mat_aa, 2, aa_freq)
new_list <- lapply(mat_aa_freq, complete_and_reorder_amino_acids)
final_matrix <- matrix(unlist(new_list,), ncol = 8, byrow = FALSE)
colnames(final_matrix) <- c("P4", "P3", "P2", "P1", "P1'", "P2'", "P3'", "P4'")
row.names(final_matrix) <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y")Plotting the result and saving the heatmap. After running the code below, the png file should be saved in your working directory.
png("pics_plot_heatmap.png",
width = 6, height = 6,
units = "in", res = 300)
final_matrix %>%
ComplexHeatmap::pheatmap(
cluster_rows = FALSE,
cluster_cols = FALSE,
fontsize = 10,
show_rownames = TRUE,
show_colnames = TRUE,
color = colorRampPalette(c("grey95", "steelblue"))(20),
main = "Cleavage Site specificity",
gaps_col = 4,
angle_col = "0"
)
dev.off()
Alternatively, you can plot using ggplot2. This gives you more control over the plot. Better for customization.
pics_plot <- final_matrix %>%
as.data.frame() %>%
rownames_to_column(var = "residue") %>%
pivot_longer(cols = -residue, names_to = "position",
values_to = "frequency") %>%
dplyr::mutate(
position = factor(position, c("P4", "P3", "P2", "P1", "P1'", "P2'", "P3'", "P4'")),
residue = factor(residue, c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y"))
) %>%
ggplot(aes(x = position, y = residue, fill = frequency)) +
geom_tile(color = "black") +
scale_fill_gradient(low = "grey90", high = "dodgerblue4") +
theme_void() +
labs(
title = "Cleavage Site Specificity",
x = "Position",
y = "Amino acid residue",
fill = "Frequency (%)"
) +
theme(text = element_text(size = 15, color = "black"),
axis.text.x = element_text(hjust = 0.5),
axis.text.y = element_text(),
legend.title = element_text(hjust = 0.5),
plot.title = element_text(hjust = 0.5),
legend.position = "bottom",
legend.key.width = unit(1.5, "cm"),
legend.title.position = "top")
# save the plot
ggsave("PICS_plot_ggplot.png",
plot = pics_plot,
width = 6, height = 6, bg = "white",
units = "in", dpi = 300)
Additional packages you will need:
library(ggseqlogo) # to create the seqLogos
library(patchwork) # to merge the figures in a nice panelMake the seqlogo for the N-termini and C-termini.
Nterm_seqLogo_plot <- psm_file$fingerprint_Nterm %>%
na.omit() %>%
ggseqlogo::ggseqlogo(
method = "bits",
seq_type = "AA"
) +
geom_hline(yintercept = 0,
color = "black", linetype = "dashed") +
geom_vline(xintercept = 4.5,
color = "black", linetype = "dashed") +
theme_bw() +
theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
text = element_text(size = 15, color = "black"),
legend.position = "bottom",
legend.title.position = "top",
legend.title = element_text(size = 12, hjust = 0.5)
) +
labs(title = "SeqLogo of the N-termini fingerprint",
x = "Amino acid position",
y = "Bits")
ggsave("Nterm_seqLogo_plot.png",
plot = Nterm_seqLogo_plot,
width = 4, height = 5, bg = "white",
units = "in", dpi = 300)
Cterm_seqLogo_plot <- psm_file$fingerprint_Cterm %>%
na.omit() %>%
ggseqlogo::ggseqlogo(
method = "bits",
seq_type = "AA"
) +
geom_hline(yintercept = 0,
color = "black", linetype = "dashed") +
geom_vline(xintercept = 4.5,
color = "black", linetype = "dashed") +
theme_bw() +
theme(plot.title = element_text(size = 12, face = "bold", hjust = 0.5),
text = element_text(size = 15, color = "black"),
legend.position = "bottom",
legend.title.position = "top",
legend.title = element_text(size = 12, hjust = 0.5)
) +
labs(title = "SeqLogo of the C-termini fingerprint",
x = "Amino acid position",
y = "Bits")
ggsave("Cterm_seqLogo_plot.png",
plot = Cterm_seqLogo_plot,
width = 4, height = 5, bg = "white",
units = "in", dpi = 300)This panel plot will show the N-termini and the C-termini seqlogos separately and the composite of the N- and C-termini as a heatmap.
panel_plot <- (
(
(Nterm_seqLogo_plot | Cterm_seqLogo_plot) + plot_layout(guides = 'collect') & theme(legend.position = "bottom")
) / pics_plot) +
plot_annotation(tag_levels = "A", theme = theme(plot.tag = element_text(size = 40, face = "bold"))) +
plot_layout(heights = c(1,4))
ggsave("panel_plot.png",
panel_plot,
width = 20, height = 25,
dpi = 300)
