Type:	Package
Title:	A Toolkit for Using Peptide Sequences in Machine Learning
Version:	0.0.1
Description:	This toolkit is designed for manipulation and analysis of peptides. It provides functionalities to assist researchers in peptide engineering and proteomics. Users can manipulate peptides by adding amino acids at every position, count occurrences of each amino acid at each position, and transform amino acid counts based on probabilities. The package offers functionalities to select the best versus the worst peptides and analyze these peptides, which includes counting specific residues, reducing peptide sequences, extracting features through One Hot Encoding (OHE), and utilizing Quantitative Structure-Activity Relationship (QSAR) properties (based in the package 'Peptides' by Osorio et al. (2015) <doi:10.32614/RJ-2015-001>). This package is intended for both researchers and bioinformatics enthusiasts working on peptide-based projects, especially for their use with machine learning.
Depends:	R (≥ 4.3.0)
License:	GPL (≥ 3)
Encoding:	UTF-8
Imports:	Peptides, stats, dplyr, stringr, caret
RoxygenNote:	7.2.3
URL:	https://github.com/jrcodina/peptoolkit
BugReports:	https://github.com/jrcodina/peptoolkit/issues
NeedsCompilation:	no
Packaged:	2023-07-13 14:18:54 UTC; Josep
Author:	Josep-Ramon Codina [aut, cre]
Maintainer:	Josep-Ramon Codina <jrc356@miami.edu>
Repository:	CRAN
Date/Publication:	2023-07-13 15:30:06 UTC

Transform Amino Acid Appearance Probability into -1, 0, or 1

Description

This function transforms the counts of amino acids to a -1, 0, 1 matrix based on a probability of appearance of each peptide in each position.

Usage

appearance_to_binary(x, threshold = 1.65, group = "Best", percentage = 0.05)

Arguments

Value

A matrix with the same dimensions as the input where each cell has been transformed to -1, 0, or 1 based on the probability threshold.

Examples

# Generate a mock data frame
peptide_data <- data.frame(Sequence = c("ACGT", "TGCA", "GATC", "CGAT"))

# Apply the function to the mock data
appearance_to_binary(peptide_data, group = "Best", percentage = 0.5)

Count Amino Acids

Description

This function counts the occurrence of each of the 20 amino acids at each of the first 'n' positions across a vector of peptide sequences.

Usage

count_aa(peptides, n = 4)

Arguments

Value

A data frame with 'n' rows and 20 columns where each row represents a position in the peptide sequence and each column represents an amino acid. Each cell in the data frame contains the count of a particular amino acid at a particular position.

Examples

count_aa(c("ACDF", "BCDE", "ABCD"), n = 2)

Extract One-Hot Encoded (OHE) Features from Peptide Sequences

Description

This function takes a data frame or a vector of peptide sequences and generates a one-hot encoded data frame representing each amino acid in the sequences. It can also include additional data (such as docking information), if provided.

Usage

extract_features_OHE(df, sequence_col = "Sequence", docking_col = NULL)

Arguments

Value

A data frame containing one-hot encoded peptide sequences and, if provided, docking information.

Examples

# Load required library caret
library(caret)
# Generate a mock data frame of peptide sequences
df <- data.frame(Sequence = c("AVILG", "VILGA", "ILGAV", "LGAVI"), X = c(1,1,2,3))
# Apply the function to the mock data
extract_features_OHE(df)

Extract QSAR Features from Peptide Sequences

Description

This function extracts various Quantitative Structure-Activity Relationship (QSAR) features from peptide sequences. The extraction is based on a variety of amino acid properties and functions from the "Peptides" package (https://github.com/dosorio/Peptides/).

Usage

extract_features_QSAR(
  n,
  pH = 7.4,
  custom.list = FALSE,
  PeList = NULL,
  rem.cys = FALSE,
  rem.met = FALSE,
  rem.sali = FALSE,
  norm = FALSE
)

Arguments

Value

A dataframe with the calculated peptide properties.

Examples

extract_features_QSAR(n = 3, custom.list = TRUE, PeList = c('ACA', 'ADE'))

Filter Peptides by Residue Counts

Description

This function counts the number of specified residues in each peptide sequence and filters out the ones with more than the specified limit. It's defaults is for filtering out small alliphatic residues.

Usage

filter_residues(
  df,
  sequence_col = "Sequence",
  residues = c("A", "V", "I", "L", "G"),
  max_residues = 2
)

Arguments

Value

A filtered data frame.

Examples

# Generate a mock data frame
peptide_data <- data.frame(Sequence = c("AVILG", "VILGA", "ILGAV", "LGAVI"))
# Apply the function to the mock data
filter_residues(peptide_data, residues = c("A", "V", "I", "L", "G"), max_residues = 2)

Increment Peptide Sequences

Description

This function generates new peptide sequences by adding each of the 20 amino acids to each position of the input peptide or peptides.

Usage

increment(peptides, num_added = 1)

Arguments

Value

A character vector of new peptide sequences.

Examples

increment(c("AC", "DE"))
increment("ACDE", num_added = 2)

Reduce Peptide Sequences by One Residue

Description

This function takes a vector of peptide sequences and generates all possible sequences by removing one amino acid residue at a time. It can also associate each sequence with an ID, if provided.

Usage

reduce_sequences(peptides, id = NULL)

Arguments

Value

A list of data frames, each containing all possible sequences resulting from removing one amino acid from the original sequence.

Examples

# Generate a mock vector of peptide sequences
peptides <- c("AVILG", "VILGA", "ILGAV", "LGAVI")
# Apply the function to the mock data
reduce_sequences(peptides)

Select Best vs Worst Peptides

Description

This function identifies the peptides from the function *appearance_to_binary* that are 1 in one group and 0 or -1 in another group, and expands the grid to all possible combinations.

Usage

select_best_vs_worst(appearance_best, appearance_worst)

Arguments

Value

A data frame with combinations of 'best' peptides.

Examples

# Generate some mock data
appearance_best <- matrix(c(1, -1, 0, 1, -1), nrow = 5, ncol = 4)
appearance_worst <- matrix(c(-1, 1, 0, -1, 1), nrow = 5, ncol = 4)
# Call the function
select_best_vs_worst(appearance_best, appearance_worst)