PepToolkit

The peptoolkit R package is designed for the manipulation and analysis of peptides sequences. 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.

Installation

You can install the released version of peptoolkit (0.0.1) from CRAN with:

install.packages("peptoolkit")

You can also install the development version (0.0.2) from GitHub with:

# install.packages("devtools") # Uncomment and run if you don't have the devtools package yet
devtools::install_github("jrcodina/peptoolkit")

Example

This is a basic example which shows you how to use the main function:

# Default usage
result <- peptoolkit::extract_features_QSAR(n = 3)

# Providing a custom peptide list
result <- peptoolkit::extract_features_QSAR(n = 3, custom.list = TRUE, PeList = c('ACA', 'ADE'))

Please refer to function documentation for more details on parameters and their usage.

Citation

If you use peptoolkit in your research, please cite:

Codina J (2023). peptoolkit: A Toolkit for Using Peptide Sequences in Machine Learning and Accelerate Virtual Screening. R package version 0.0.1.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {peptoolkit: A Toolkit for Using Peptide Sequences in Machine Learning and Accelerate Virtual Screening},
    author = {Josep-Ramon Codina},
    year = {2023},
    note = {R package version 0.0.1},
  }