peptides


Namepeptides JSON
Version 0.3.2 PyPI version JSON
download
home_pagehttps://github.com/althonos/peptides.py
SummaryPhysicochemical properties, indices and descriptors for amino-acid sequences.
upload_time2023-04-12 18:38:18
maintainer
docs_urlNone
authorMartin Larralde
requires_python>=3.6
licenseMIT
keywords bioinformatics protein sequence peptide qsar
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # `peptides.py` [![Stars](https://img.shields.io/github/stars/althonos/peptides.py.svg?style=social&maxAge=3600&label=Star)](https://github.com/althonos/peptides.py/stargazers)

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

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## πŸ—ΊοΈ Overview

`peptides.py` is a pure-Python package to compute common descriptors for
protein sequences. It started as a port of [`Peptides`](https://cran.r-project.org/web/packages/Peptides/index.html), the R package written by
[Daniel Osorio](https://orcid.org/0000-0003-4424-8422), but now also provides
some additional features from [EMBOSS](http://emboss.bioinformatics.nl/cgi-bin/emboss/),
[ExPASy Protein Identification and Analysis Tools](https://web.expasy.org/protparam/), and [Rcpi](https://bioconductor.org/packages/release/bioc/html/Rcpi.html).

This library has no external dependency and is available for all modern Python
versions (3.6+).

### πŸ“‹ 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, 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](https://github.com/althonos/peptides.py/issues)
of the [project repository](https://github.com/althonos/peptides.py).*

### 🧊 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`](https://numpy.org/) can be imported, relevant functions
(like `numpy.sum` or `numpy.take`) will be used, otherwise a fallback
implementation using [`array.array`](https://docs.python.org/3/library/array.html#array.array)
from the standard library is available.

## πŸ”§ Installing

Install the `peptides` package directly from [PyPi](https://pypi.org/project/peptides)
which hosts universal wheels that can be installed with `pip`:
```console
$ pip install peptides
```

<!--
Otherwise, `peptides.py` is also available as a [Bioconda](https://bioconda.github.io/)
package:
```console
$ conda install -c bioconda peptides-py
``` -->

## πŸ“– Documentation

A complete [API reference](https://peptides.readthedocs.io/en/stable/api.html)
can be found in the [online documentation](https://peptides.readthedocs.io/),
or directly from the command line using
[`pydoc`](https://docs.python.org/3/library/pydoc.html):
```console
$ pydoc peptides.Peptide
```

## πŸ’‘ Example

Start by creating a `Peptide` object from a protein sequence:
```python
>>> import peptides
>>> peptide = peptides.Peptide("MLKKRFLGALAVATLLTLSFGTPVMAQSGSAVFTNEGVTPFAISYPGGGT")
```

Then use the appropriate methods to compute the descriptors you want:
```python
>>> 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:
```python
>>> 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:
```python
>>> 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]
```

## πŸ’­ Feedback

### ⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the [GitHub issue
tracker](https://github.com/althonos/peptides.py/issues) 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.

### πŸ—οΈ Contributing

Contributions are more than welcome! See
[`CONTRIBUTING.md`](https://github.com/althonos/peptides.py/blob/main/CONTRIBUTING.md)
for more details.

## βš–οΈ License

This library is provided under the [GNU General Public License v3.0](https://choosealicense.com/licenses/gpl-3.0/).
The original R `Peptides` package was written by [Daniel Osorio](https://orcid.org/0000-0003-4424-8422),
[Paola RondΓ³n-Villarreal](https://orcid.org/0000-0001-8209-3885) and
[Rodrigo Torres](https://orcid.org/0000-0003-1113-3020), and is licensed under
the terms of the [GNU General Public License v2.0](https://choosealicense.com/licenses/gpl-2.0/).
The [EMBOSS](http://emboss.bioinformatics.nl/cgi-bin/emboss/) applications are
released under the [GNU General Public License v1.0](https://www.gnu.org/licenses/old-licenses/gpl-1.0.html).

*This project is in no way not affiliated, sponsored, or otherwise endorsed
by the [original `Peptides` authors](https://github.com/dosorio). It was developed
by [Martin Larralde](https://github.com/althonos/) during his PhD project
at the [European Molecular Biology Laboratory](https://www.embl.de/) in
the [Zeller team](https://github.com/zellerlab).*

            

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It started as a port of [`Peptides`](https://cran.r-project.org/web/packages/Peptides/index.html), the R package written by\n[Daniel Osorio](https://orcid.org/0000-0003-4424-8422), but now also provides\nsome additional features from [EMBOSS](http://emboss.bioinformatics.nl/cgi-bin/emboss/),\n[ExPASy Protein Identification and Analysis Tools](https://web.expasy.org/protparam/), and [Rcpi](https://bioconductor.org/packages/release/bioc/html/Rcpi.html).\n\nThis library has no external dependency and is available for all modern Python\nversions (3.6+).\n\n### \ud83d\udccb Features\n\nA non-exhaustive list of available features:\n\n- Peptide statistics: amino acid counts and frequencies.\n- **QSAR** descriptors: BLOSUM indices, Cruciani properties, FASGAI vectors, Kidera factors, MS-WHIM scores, PCP descriptors, ProtFP descriptors, Sneath vectors, ST-scales, T-scales, VHSE-scales, Z-scales.\n- Sequence profiles: hydrophobicity, hydrophobic moment, membrane position.\n- Physicochemical properties: aliphatic index, instability index, theoretical net charge, isoelectric point, molecular weight (with isotope labelling support).\n- Biological properties: structural class prediction.\n\n*If this library is missing a useful statistic or descriptor, feel free to\nreach out and open a feature request on the [issue tracker](https://github.com/althonos/peptides.py/issues)\nof the [project repository](https://github.com/althonos/peptides.py).*\n\n### \ud83e\uddca Vectorization\n\nMost of the descriptors for a protein sequence are simple averages of values\ntaken in a lookup table, so computing them can be done in a vectorized manner.\nIf [`numpy`](https://numpy.org/) can be imported, relevant functions\n(like `numpy.sum` or `numpy.take`) will be used, otherwise a fallback\nimplementation using [`array.array`](https://docs.python.org/3/library/array.html#array.array)\nfrom the standard library is available.\n\n## \ud83d\udd27 Installing\n\nInstall the `peptides` package directly from [PyPi](https://pypi.org/project/peptides)\nwhich hosts universal wheels that can be installed with `pip`:\n```console\n$ pip install peptides\n```\n\n<!--\nOtherwise, `peptides.py` is also available as a [Bioconda](https://bioconda.github.io/)\npackage:\n```console\n$ conda install -c bioconda peptides-py\n``` -->\n\n## \ud83d\udcd6 Documentation\n\nA complete [API reference](https://peptides.readthedocs.io/en/stable/api.html)\ncan be found in the [online documentation](https://peptides.readthedocs.io/),\nor directly from the command line using\n[`pydoc`](https://docs.python.org/3/library/pydoc.html):\n```console\n$ pydoc peptides.Peptide\n```\n\n## \ud83d\udca1 Example\n\nStart by creating a `Peptide` object from a protein sequence:\n```python\n>>> import peptides\n>>> peptide = peptides.Peptide(\"MLKKRFLGALAVATLLTLSFGTPVMAQSGSAVFTNEGVTPFAISYPGGGT\")\n```\n\nThen use the appropriate methods to compute the descriptors you want:\n```python\n>>> peptide.aliphatic_index()\n89.8...\n>>> peptide.boman()\n-0.2097...\n>>> peptide.charge(pH=7.4)\n1.99199...\n>>> peptide.isoelectric_point()\n10.2436...\n```\n\nMethods that return more than one scalar value (for instance, `Peptide.blosum_indices`)\nwill return a dedicated named tuple:\n```python\n>>> peptide.ms_whim_scores()\nMSWHIMScores(mswhim1=-0.436399..., mswhim2=0.4916..., mswhim3=-0.49200...)\n```\n\nUse the `Peptide.descriptors` method to get a dictionary with every available\ndescriptor. This makes it very easy to create a `pandas.DataFrame` with\ndescriptors for several protein sequences:\n```python\n>>> seqs = [\"SDKEVDEVDAALSDLEITLE\", \"ARQQNLFINFCLILIFLLLI\", \"EGVNDNECEGFFSAR\"]\n>>> df = pandas.DataFrame([ peptides.Peptide(s).descriptors() for s in seqs ])\n>>> df\n    BLOSUM1   BLOSUM2  BLOSUM3   BLOSUM4  ...        Z2        Z3        Z4        Z5\n0  0.367000 -0.436000   -0.239  0.014500  ... -0.711000 -0.104500 -1.486500  0.429500\n1 -0.697500 -0.372500   -0.493  0.157000  ... -0.307500 -0.627500 -0.450500  0.362000\n2  0.479333 -0.001333    0.138  0.228667  ... -0.299333  0.465333 -0.976667  0.023333\n\n[3 rows x 66 columns]\n```\n\n## \ud83d\udcad Feedback\n\n### \u26a0\ufe0f Issue Tracker\n\nFound a bug ? Have an enhancement request ? Head over to the [GitHub issue\ntracker](https://github.com/althonos/peptides.py/issues) if you need to report\nor ask something. If you are filing in on a bug, please include as much\ninformation as you can about the issue, and try to recreate the same bug\nin a simple, easily reproducible situation.\n\n### \ud83c\udfd7\ufe0f Contributing\n\nContributions are more than welcome! 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It was developed\nby [Martin Larralde](https://github.com/althonos/) during his PhD project\nat the [European Molecular Biology Laboratory](https://www.embl.de/) in\nthe [Zeller team](https://github.com/zellerlab).*\n",
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