peptides 0.3.4

Physicochemical properties, indices and descriptors for amino-acid sequences.

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, but now also provides some additional 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+).

📋 Features

A non-exhaustive list of available features:

• Peptide statistics: amino acid counts and frequencies.
• QSAR descriptors: BLOSUM indices, Cruciani properties, FASGAI vectors, Kidera factors, Atchley factors, MS-WHIM scores, PCP descriptors, ProtFP descriptors, Sneath vectors, ST-scales, T-scales, VHSE-scales, Z-scales.
• Sequence profiles: hydrophobicity, hydrophobic moment, membrane position.
• Physicochemical properties: aliphatic index, instability index, theoretical net charge, isoelectric point, molecular weight (with isotope labelling support).
• Biological properties: structural class prediction.

If this library is missing a useful statistic or descriptor, feel free to reach out and open a feature request on the issue tracker of the project repository.

🧊 Vectorization

Most of the descriptors for a protein sequence are simple averages of values taken in a lookup table, so computing them can be done in a vectorized manner. If numpy can be imported, relevant functions (like numpy.sum or numpy.take) will be used, otherwise a fallback implementation using array.array from the standard library is available.

🔧 Installing

Install the peptides package directly from PyPi which hosts universal wheels that can be installed with pip:

$ pip install peptides

Otherwise, peptides.py is also available as a Bioconda package:

$ conda install -c bioconda peptides

📖 Documentation

A complete API reference can be found in the online documentation, or directly from the command line using pydoc:

$ pydoc peptides.Peptide

💡 Example

Start by creating a Peptide object from a protein sequence:

>>> import peptides
>>> peptide = peptides.Peptide("MLKKRFLGALAVATLLTLSFGTPVMAQSGSAVFTNEGVTPFAISYPGGGT")

Then use the appropriate methods to compute the descriptors you want:

>>> peptide.aliphatic_index()
89.8...
>>> peptide.boman()
-0.2097...
>>> peptide.charge(pH=7.4)
1.99199...
>>> peptide.isoelectric_point()
10.2436...

Methods that return more than one scalar value (for instance, Peptide.blosum_indices) will return a dedicated named tuple:

>>> peptide.ms_whim_scores()
MSWHIMScores(mswhim1=-0.436399..., mswhim2=0.4916..., mswhim3=-0.49200...)

Use the Peptide.descriptors method to get a dictionary with every available descriptor. This makes it very easy to create a pandas.DataFrame with descriptors for several protein sequences:

>>> seqs = ["SDKEVDEVDAALSDLEITLE", "ARQQNLFINFCLILIFLLLI", "EGVNDNECEGFFSAR"]
>>> df = pandas.DataFrame([ peptides.Peptide(s).descriptors() for s in seqs ])
>>> df
    BLOSUM1   BLOSUM2  BLOSUM3   BLOSUM4  ...        Z2        Z3        Z4        Z5
0  0.367000 -0.436000   -0.239  0.014500  ... -0.711000 -0.104500 -1.486500  0.429500
1 -0.697500 -0.372500   -0.493  0.157000  ... -0.307500 -0.627500 -0.450500  0.362000
2  0.479333 -0.001333    0.138  0.228667  ... -0.299333  0.465333 -0.976667  0.023333

[3 rows x 66 columns]

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

⚖️ 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.