Comprehensive toolkit for generating various numerical features of protein sequences described in Xiao et al. (2015) <DOI:10.1093/bioinformatics/btv042> (PDF).

Paper Citation

Formatted citation:

Nan Xiao, Dong-Sheng Cao, Min-Feng Zhu, and Qing-Song Xu. (2015). protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics 31 (11), 1857-1859.

BibTeX entry:

  author = {Xiao, Nan and Cao, Dong-Sheng and Zhu, Min-Feng and Xu, Qing-Song.},
  title = {{protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences}},
  journal = {Bioinformatics},
  year = {2015},
  volume = {31},
  number = {11},
  pages = {1857--1859},
  doi = {10.1093/bioinformatics/btv042},
  issn = {1367-4803},
  url = {}


To install protr from CRAN:

Or try the latest version on GitHub:

Browse the package vignette for a quick-start.

Shiny App

ProtrWeb, the Shiny web application built on protr, can be accessed from

ProtrWeb is a user-friendly web application for computing the protein sequence descriptors (features) presented in the protr package.

List of Supported Descriptors

Commonly used descriptors

  • Amino acid composition descriptors

    • Amino acid composition
    • Dipeptide composition
    • Tripeptide composition
  • Autocorrelation descriptors

    • Normalized Moreau-Broto autocorrelation
    • Moran autocorrelation
    • Geary autocorrelation
  • CTD descriptors

    • Composition
    • Transition
    • Distribution
  • Conjoint Triad descriptors

  • Quasi-sequence-order descriptors

    • Sequence-order-coupling number
    • Quasi-sequence-order descriptors
  • Pseudo amino acid composition (PseAAC)

    • Pseudo amino acid composition
    • Amphiphilic pseudo amino acid composition
  • Profile-based descriptors

    • Profile-based descriptors derived by PSSM (Position-Specific Scoring Matrix)

Proteochemometric (PCM) modeling descriptors

  • Scales-based descriptors derived by principal components analysis
    • Scales-based descriptors derived by amino acid properties (AAindex)
    • Scales-based descriptors derived by 20+ classes of 2D and 3D molecular descriptors (Topological, WHIM, VHSE, etc.)
    • Scales-based descriptors derived by factor analysis
    • Scales-based descriptors derived by multidimensional scaling
    • BLOSUM and PAM matrix-derived descriptors

Similarity computation

Local and global pairwise sequence alignment for protein sequences:

  • Between two protein sequences
  • Parallelized pairwise similarity calculation with a list of protein sequences

GO semantic similarity measures:

  • Between two groups of GO terms / two Entrez Gene IDs
  • Parallelized pairwise similarity calculation with a list of GO terms / Entrez Gene IDs

Miscellaneous tools and datasets

  • Retrieve protein sequences from UniProt
  • Read protein sequences in FASTA format
  • Read protein sequences in PDB format
  • Sanity check of the amino acid types appeared in the protein sequences
  • Protein sequence segmentation
  • Auto cross covariance (ACC) for generating scales-based descriptors of the same length
  • 20+ pre-computed 2D and 3D descriptor sets for the 20 amino acids to use with the scales-based descriptors
  • BLOSUM and PAM matrices for the 20 amino acids
  • Meta information of the 20 amino acids


To contribute to this project, please take a look at the Contributing Guidelines first. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.