library(knitr) opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE) knitr::opts_chunk$set(comment = "#>", collapse = TRUE)
The goal of this document is to get you up and running with BaMORC as quickly as possible. BaMORC stands for the Bayesian Model Optimized Reference Correction (BaMORC) Method for Assigned and Unassigned Protein NMR Spectra. For a detailed explanation of the algorithm, please refer to "Automatic 13C chemical shift reference correction for unassigned protein NMR spectra".
There are two important parts to BaMORC: the
bamorc() reference correction function for assigned 13C protein NMR spectra, and the
unassigned_bamorc() reference correction function for unassigned 13C protein NMR spectra. In the first section, you'll learn about the basics of running both of these functions. In the second section, you will dive into the basics of input data process, and in third section, you'll learn a little more about the functions used behind-the-scenes by the BaMORC algorithm.
To make a correction, first load BaMORC, then call
unassigned_bamorc() with the correct arguments:
Here we will using the built-in data to demonstrate the arguments that will be passed into
## Arguments: sequence = paste(RefDB_data$carbonDat[]$AA, collapse = "") secondary_structure = paste(RefDB_data$carbonDat[]$SS, collapse = "") chemical_shifts_input = RefDB_data$carbonDat[][, c(4,5)] from= -5 to = 5 ## Running bamorc() function: bamorc(sequence = sequence, secondary_structure = secondary_structure, chemical_shifts_input = chemical_shifts_input, from = -5, to = 5)
chemical_shifts_input: the carbon 13 chemical shift of the protein.
frommust be lower than
The length of the
secondary_structure should be the same, if not please assign
secondary_structure = NULL, however, the peaklist groups number could be more or less than the length of the sequence. Often peak lists will have fewer CA/CB pairs than the length of the protein sequence.
Printing an argument object gives you some useful information: the actual format, the size, the object type, and if it's a string.
Next, we will use the built-in data to demonstrate the arguments that will be passed in the
unassigned_bamorc(). The output will be slightly different each time it runs, due to the randomness of the optimization.
## Arguments:enerate a temperary sample NMR spectra file and later will be ## removed. sequence = "RPAFCLEPPYAGPGKARIIRYFYNAAAGAAQAFVYGGVRAKRNNFASAADALAACAAA" sample_file_path = system.file("extdata", "bpti_HNcoCACB.txt", package = "BaMORC") ## Running unassigned_bamorc() function: # unassigned_bamorc will take a while to run unassigned_bamorc(peakList_file_loc = sample_file_path, sequence = sequence, secondary_structure = NULL, from = -5, to = 5)
The data passed in both above functions should be pre-processed. Luckly, we provide a variety of helper functions. See the following on how to process the data.
There are three file parsing functions within the BaMORC package:
read_db_file(). These functions handle a wide range of the input files.
Delimiter of white space: ```r ## Arguments: sample_file_path = system.file("extdata", "sample_input_ws.txt", package = "BaMORC")
head(read_raw_file(sample_file_path, delim = "ws")) ```
Delimiter of comma: ```r ## Arguments: sample_file_path = system.file("extdata", "sample_input.csv", package = "BaMORC")
head(read_raw_file(sample_file_path, delim = "comma")) ```
Delimiter of semicolon: ```r ## Arguments: sample_file_path = system.file("extdata", "sample_input_sc.txt", package = "BaMORC")
head(read_raw_file(sample_file_path, delim = "semicolon")) ```
read_raw_file(): parses user-provided file in customed format as show above examples.
read_nmrstar_file(): parses user-provided file in BMRB Star 2/3 format.
## Download a BMRB file library(BMRBr) bmrb_download(4020, output_dir = "./") ## Read in BMRB file and procec file_path = "bmr4020.str" head(read_nmrstar_file(file_path = file_path)) ## Delete downloaded BMRB file unlink("./bmr4020.str")
read_db_file(): parses file from BMRB by a given entry ID.
id = 4022 output <- read_db_file(id = id) head(output[]) head(output[])
Secondary structure information predicted based on the protein sequence is required for the optimization. This is predicted through the JPred and jpredapi.
protein_sequence <- "MQVWPIEGIKKFETLSYLPPLTVEDLLKQI" secondary_structure <- jpred_fetcher(protein_sequence = protein_sequence) secondary_structure
The BaMORC algorithm minimizes the difference between the actual relative cummulative frequency (RCF) of the protein sequence and the estimated RCF predicted from the chemical shifts information. The statistical functions used by the algorithm are shown below with examples. For detailed function descriptions, please see the reference:
calculate_aa_prob(): returns the probability (density) for a certain type of amino acid based on a chi-squared statistics wtih 2 degrees of freedom.
calculate_aa_prob(chi_squared_stat = c(0.05, 0.1, 0.5), df = 2)
calculate_chi_squared_stat(): given a pair of alpha and beta carbons chemical shifts, this function will return a list of calculated chi-squared statistics based on the combination of amino acid typings and secondary structures. Here we illustrate with a pair of chemical shifts from alpha and beta carbon.
calculate_chi_squared_stat(cacb_pair = c(54,45))
calculate_rcf(): calculates the relative cummulative freqeucny of amino acid and secondary structure combination.
## Arguments: sequence = paste(RefDB_data$carbonDat[]$AA, collapse = "") secondary_structure = paste(RefDB_data$carbonDat[]$SS, collapse = "") ## Function: calculate_rcf(sequence = sequence, secondary_structure = secondary_structure)
calculate_mse(): calculates mean squared error for each correction value (step).
## chemicalShifts and aaFreq are predefined sample variables for demo purpose ## within the BaMORC Package. calculate_mse(step_ca = 1, step_cb = 1, dat_cacb = chemicalShifts[, c(3,4)], aa_Freq = aaFreq)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.