peptides.py is a pure-Python package to compute common descriptors for protein sequences

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Data Analysispeptides.py
Overview

peptides.py Stars

Physicochemical properties and indices for amino-acid sequences.

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🗺️ Overview

peptides.py is a pure-Python package to compute common descriptors for protein sequences. It is a port of Peptides, the R package written by Daniel Osorio for the same purpose. This library has no external dependency and is available for all modern Python versions (3.6+).

🔧 Installing

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

$ pip install peptides

💡 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:

>> 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] "> 
>>> 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 GPLv2.

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.

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Comments
  • Per-residue data

    Per-residue data

    It seems that the API can only output single statistics for the entire peptide chain, but I'm interested in statistics for each residue individually. I'm wondering if it might be possible to output an array/list from some of these functions instead of always averaging them as is done now.

    enhancement 
    opened by multimeric 1
  • Hydrophobic moment is inconsistent with R version

    Hydrophobic moment is inconsistent with R version

    Computed hydrophobic moment is not the same as the one computed by R. More specifically, it seems that peptides.py always outputs 0 for the hydrophobic moment when peptide length is shorter than the set window. The returned value matches the value from R when peptide length is equal to or greater than the set window length.

    Example in python:

    >>> import peptides`
>>> peptides.Peptide("MLK").hydrophobic_moment(window=5, angle=100)
0.0
>>> peptides.Peptide("AACQ").hydrophobic_moment(window=5, angle=100)
0.0
>>> peptides.Peptide("FGGIQ").hydrophobic_moment(window=5, angle=100)
0.31847187610377536

    Example in R:

    > library(Peptides)
> hmoment(seq="MLK", window=5, angle=100)
[1] 0.8099386
> hmoment(seq="AACQ", window=5, angle=100)
[1] 0.3152961
> hmoment(seq="FGGIQ", window=5, angle=100)
[1] 0.3184719

    I think that it can be easily fixed by internally setting the window length to the length of the peptide if the latter is shorter. What I propose:

    --- a/peptides/__init__.py
+++ b/peptides/__init__.py
@@ -657,6 +657,7 @@ class Peptide(typing.Sequence[str]):
               :doi:`10.1073/pnas.81.1.140`. :pmid:`6582470`.

         """
+        window = min(window, len(self))
         scale = tables.HYDROPHOBICITY["Eisenberg"]
         lut = [scale.get(aa, 0.0) for aa in self._CODE1]
         angles = [(angle * i) % 360 for i in range(window)]
    bug 
    opened by eotovic 1
  • RuntimeWarning in auto_correlation function()

    RuntimeWarning in auto_correlation function()

    Hi, thank you for creating peptides.py.

    Some hydrophobicity tables together with certain proteins cause a runtime warning for in the function auto_correlation():

    import peptides

for hydro in peptides.tables.HYDROPHOBICITY.keys():
    print(hydro)
    table = peptides.tables.HYDROPHOBICITY[hydro]
    peptides.Peptide('MANTQNISIWWWAR').auto_correlation(table)

    Warning (s2 == 0):

    RuntimeWarning: invalid value encountered in double_scalars
  return s1 / s2

    The tables concerned are: octanolScale_pH2, interfaceScale_pH2, oiScale_pH2 Some other proteins causing the same warning: ['MSYGGSCAGFGGGFALLIVLFILLIIIGCSCWGGGGYGY', 'MFILLIIIGASCFGGGGGCGYGGYGGYAGGYGGYCC', 'MSFGGSCAGFGGGFALLIVLFILLIIIGCSCWGGGGGF']

    opened by jhahnfeld 0
Releases(v0.3.1)

  • v0.3.1(Sep 1, 2022)

  • v0.3.0(Sep 1, 2022)

    Added

    • Peptide.linker_preference_profile to build a profile like used in the DomCut method from Suyama & Ohara (2002).
    • Peptide.profile to build a generic per-residue profile from a data table (#3).
    Source code(tar.gz)
    Source code(zip)

  • v0.2.0(Oct 25, 2021)

    Added

    • Peptide.counts method to get the number of occurences of each amino acid in the peptide.
    • Peptide.frequencies to get the frequencies of each amino acid in the peptide.
    • Peptide.pcp_descriptors to compute the PCP descriptors from Mathura & Braun (2001).
    • Peptide.sneath_vectors to compute the descriptors from Sneath (1966).
    • Hydrophilicity descriptors from Barley (2018).
    • Peptide.structural_class to predict the structural class of a protein using one of three reference datasets and one of four distance metrics.

    Changed

    • Peptide.aliphatic_index now supports unknown Leu/Ile residue (code J).
    • Swap order of Peptide.hydrophobic_moment arguments for consistency with profile methods.
    • Some Peptide functions now support vectorized code using numpy if available.
    Source code(tar.gz)
    Source code(zip)

  • v0.1.0(Oct 21, 2021)

Owner
Martin Larralde
PhD candidate in Bioinformatics, passionate about programming, Pythonista, Rustacean. I write poems, and sometimes they are executable.
Martin Larralde
GitHub Repository
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