peptides.py 
#
Physicochemical properties, indices and descriptors for amino-acid sequences.
Overview#
peptides.py is a pure-Python package to compute common descriptors for
protein sequences. It started as a port of Peptides,
the R package written by Daniel Osorio
for the same purpose, but now also provides some more features from
EMBOSS,
ExPASy Protein Identification and Analysis Tools,
and Rcpi. This library has no external dependency and is
available for all modern Python versions (3.6+).
A non-exhaustive list of available features:
Amino-acid Statistics:
Number of occurrences in the peptide sequence
Frequency in the peptide sequence
QSAR descriptors:
Sequence profiles:
Hydrophobicity profile using one of 39 proposed scales.
Hydrophobic moment profile based on Eisenberg, Weiss and Terwilliger (1984).
Membrane position based on Eisenberg (1984).
Physical-chemical properties:
Aliphatic index proposed in Ikai (1980).
Instability index proposed in Boman (2003).
Theoretical net charge based on the Henderson-Hasselbach equation.
Isoelectric point using one of 8 pKa scales.
Molecular weight, taking into account isotope labelling, using one of 3 average weight tables.
Biological properties:
Structural class using methods and reference data from either Nakashima, Nishikawa & Ooi (1985), Chou (1989), Chou & Zhang (1992), or Chou, Liu, Maggiora & Zhang (1998).
Setup#
peptides is a pure Python package available for all modern Python (3.6+).
Run pip install peptides in a shell to download the latest release, or have
a look at the Installation page to find other ways to install
peptides.py.
Library#
License#
This library is provided under the GNU General Public License v3.0.
The original R Peptides package was written by Daniel Osorio,
Paola Rondón-Villarreal and
Rodrigo Torres, and is licensed under
the terms of the GNU General Public License v2.0.
The EMBOSS applications are
released under the GNU General Public License v1.0.
This project is in no way not affiliated, sponsored, or otherwise endorsed by the original Peptides authors. It was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.