protpy - Package for generating protein physicochemical, biochemical and structural descriptors using their constituent amino acids.
- 🧬 A demo of the software is available here
- 📝 A Medium article about
protPyand its background is available here
- Introduction
- Requirements
- Installation
- Usage
- Documentation
- Directories
- Tests
- Issues
- Contact
- References
protpy is a Python software package for generating a variety of physicochemical, biochemical and structural descriptors for proteins. All of these descriptors are calculated using sequence-derived or physicochemical features of the amino acids that make up the proteins. These descriptors have been highly studied and used in a series of Bioinformatic applications including protein engineering, SAR (sequence-activity-relationships), predicting protein structure & function, subcellular localization, protein-protein interactions, drug-target interactions etc.
This software is aimed at any researcher or developer using protein sequence/structural data, and was mainly created to use in my own project pySAR which uses protein sequence data to identify Sequence Activity Relationships (SAR) using Machine Learning [1]. protpy is built and developed in Python 3.10.
The descriptors available in protpy include:
Composition Descriptors (22)
- Amino Acid Composition (AAComp)
- Dipeptide Composition (DPComp)
- Tripeptide Composition (TPComp)
- Grand Average of Hydropathy (GRAVY)
- Aromaticity
- Instability Index
- Isoelectric Point
- Molecular Weight
- Charge Distribution
- Hydrophobic/Polar/Charged Composition (HPC)
- Secondary Structure Propensity (SSP)
- k-mer Composition
- Reduced Alphabet Composition
- Motif Composition
- Amino Acid Pair Composition
- Aliphatic Index
- Extinction Coefficient
- Boman Index
- Aggregation Propensity
- Hydrophobic Moment
- Shannon Entropy
- Pseudo Amino Acid Composition (PAAComp)
- Amphiphilic Amino Acid Composition (APAAComp)
Autocorrelation Descriptors (3)
- Moreaubroto Autocorrelation (MBAuto)
- Moran Autocorrelation (MAuto)
- Geary Autocorrelation (GAuto)
Conjoint Triad (1)
- Conjoint Triad (CTriad)
CTD Descriptors (4)
- CTD Composition
- CTD Transition
- CTD Distribution
- CTD Combined
Sequence Order Descriptors (5)
- Sequence Order Coupling Number — single (SOCN)
- Sequence Order Coupling Number — series
- Sequence Order Coupling Number — all matrices
- Quasi Sequence Order (QSO)
- Quasi Sequence Order — all matrices
More detail of each descriptor is listed in the markdown file: DESCRIPTORS.md
- Python >= 3.9
- aaindex >= 1.2.0
- numpy >= 2.4.4
- pandas >= 3.0.2
- varname >= 0.15.1
- biopython >= 1.87 (only required for testing)
Install the latest version of protpy using pip:
pip3 install protpy --upgradeInstall by cloning repository:
git clone https://github.com/amckenna41/protpy.git
python3 setup.py installimport protpy as protpyfrom Bio import SeqIO
with open("test_fasta.fasta") as pro:
protein_seq = str(next(SeqIO.parse(pro,'fasta')).seq)Composition Descriptors Usage Examples
Calculate Amino Acid Composition:
amino_acid_composition = protpy.amino_acid_composition(protein_seq)
# A C D E F ...
# 6.693 3.108 5.817 3.347 6.614 ...Calculate Dipeptide Composition:
dipeptide_composition = protpy.dipeptide_composition(protein_seq)
# AA AC AD AE AF ...
# 0.72 0.16 0.48 0.4 0.24 ...Calculate Tripeptide Composition:
tripeptide_composition = protpy.tripeptide_composition(protein_seq)
# AAA AAC AAD AAE AAF ...
# 1 0 0 2 0 ...Calculate GRAVY (Grand Average of Hydropathy):
gravy = protpy.gravy(protein_seq)
# GRAVY
# -0.045Calculate Aromaticity:
aromaticity = protpy.aromaticity(protein_seq)
# Aromaticity
# 0.118Calculate Instability Index:
instability = protpy.instability_index(protein_seq)
# InstabilityIndex
# 31.836Calculate Isoelectric Point:
pi = protpy.isoelectric_point(protein_seq)
# IsoelectricPoint
# 5.412Calculate Molecular Weight:
mw = protpy.molecular_weight(protein_seq)
# MolecularWeight (Da)
# 139122.355Calculate Charge Distribution:
charge = protpy.charge_distribution(protein_seq)
#using default parameters: ph=7.4
# PositiveCharge NegativeCharge NetCharge
# 99.526 114.956 -15.43Calculate Hydrophobic/Polar/Charged Composition:
hpc = protpy.hydrophobic_polar_charged_composition(protein_seq)
# Hydrophobic Polar Charged
# 44.542 32.669 18.247Calculate Secondary Structure Propensity:
ssp = protpy.secondary_structure_propensity(protein_seq)
# Helix Sheet Coil
# 0.983 1.05 1.043Calculate k-mer Composition:
kmer = protpy.kmer_composition(protein_seq)
#using default parameters: k=2
# AA AC AD ...
# 0.797 0.159 ... ...Calculate Reduced Alphabet Composition:
reduced = protpy.reduced_alphabet_composition(protein_seq)
#using default parameters: alphabet_size=6
# Group_1 Group_2 Group_3 Group_4 Group_5 Group_6
# 25.339 34.741 9.163 9.084 10.837 10.837Calculate Motif Composition:
motif = protpy.motif_composition(protein_seq)
# NxST_glycosylation RGD_integrin KDEL_retention ...
# 23 0 0 ...Calculate Amino Acid Pair Composition:
aapair = protpy.amino_acid_pair_composition(protein_seq)
# AA_Hydrophobic-Hydrophobic AA_Hydrophobic-Polar ...
# 0.797 0.159 ...Calculate Aliphatic Index:
aliphatic = protpy.aliphatic_index(protein_seq)
# AliphaticIndex
# 82.725Calculate Extinction Coefficient:
extinction = protpy.extinction_coefficient(protein_seq)
# ExtCoeff_Reduced ExtCoeff_Oxidized
# 140960 143335Calculate Boman Index:
boman = protpy.boman_index(protein_seq)
# BomanIndex
# 0.119Calculate Aggregation Propensity:
aggregation = protpy.aggregation_propensity(protein_seq)
# AggregProneRegions AggregProneFraction
# 58 11.793Calculate Hydrophobic Moment:
hm = protpy.hydrophobic_moment(protein_seq)
#using default parameters: window=11, angle=100
# HydrophobicMoment_Mean HydrophobicMoment_Max
# 0.272 0.813Calculate Shannon Entropy:
se = protpy.shannon_entropy(protein_seq)
# ShannonEntropy
# 4.163Calculate Pseudo Composition:
pseudo_composition = protpy.pseudo_amino_acid_composition(protein_seq)
#using default parameters: lamda=30, weight=0.05, properties=[]
# PAAC_1 PAAC_2 PAAC_3 PAAC_4 PAAC_5 ...
# 0.127 0.059 0.111 0.064 0.126 ...Calculate Amphiphilic Composition:
amphiphilic_composition = protpy.amphiphilic_pseudo_amino_acid_composition(protein_seq)
#using default parameters: lamda=30, weight=0.5, properties=[hydrophobicity_, hydrophilicity_]
# APAAC_1 APAAC_2 APAAC_3 APAAC_4 APAAC_5 ...
# 6.624 3.076 5.757 3.032 5.988 ...Autocorrelation Descriptors Usage Examples
Calculate MoreauBroto Autocorrelation:
moreaubroto_autocorrelation = protpy.moreaubroto_autocorrelation(protein_seq)
#using default parameters: lag=30, properties=["CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"], normalize=True
# MBAuto_CIDH920105_1 MBAuto_CIDH920105_2 MBAuto_CIDH920105_3 MBAuto_CIDH920105_4 MBAuto_CIDH920105_5 ...
# -0.052 -0.104 -0.156 -0.208 0.246 ...Calculate Moran Autocorrelation:
moran_autocorrelation = protpy.moran_autocorrelation(protein_seq)
#using default parameters: lag=30, properties=["CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"], normalize=True
# MAuto_CIDH920105_1 MAuto_CIDH920105_2 MAuto_CIDH920105_3 MAuto_CIDH920105_4 MAuto_CIDH920105_5 ...
# -0.07786 -0.07879 -0.07906 -0.08001 0.14911 ...Calculate Geary Autocorrelation:
geary_autocorrelation = protpy.geary_autocorrelation(protein_seq)
#using default parameters: lag=30, properties=["CIDH920105", "BHAR880101", "CHAM820101", "CHAM820102", "CHOC760101", "BIGC670101", "CHAM810101", "DAYM780201"], normalize=True
# GAuto_CIDH920105_1 GAuto_CIDH920105_2 GAuto_CIDH920105_3 GAuto_CIDH920105_4 GAuto_CIDH920105_5 ...
# 1.057 1.077 1.04 1.02 1.013 ...Conjoint Triad Descriptors Usage Examples
Calculate Conjoint Triad:
conjoint_triad = protpy.conjoint_triad(protein_seq)
# 111 112 113 114 115 ...
# 7 17 11 3 6 ...CTD Descriptors Usage Examples
Calculate CTD:
ctd = protpy.ctd(protein_seq)
#using default parameters: property="hydrophobicity", all_ctd=True
# hydrophobicity_CTD_C_01 hydrophobicity_CTD_C_02 hydrophobicity_CTD_C_03 normalized_vdwv_CTD_C_01 ...
# 0.279 0.386 0.335 0.389 ... Sequence Order Descriptors Usage Examples
Calculate Sequence Order Coupling Number (SOCN):
socn = protpy.sequence_order_coupling_number_(protein_seq)
#using default parameters: d=1, distance_matrix="schneider-wrede"
#401.387 Calculate all SOCN's per distance matrix:
#using default parameters: lag=30, distance_matrix="schneider-wrede"
socn_all = protpy.sequence_order_coupling_number(protein_seq)
# SOCN_SW1 SOCN_SW2 SOCN_SW3 SOCN_SW4 SOCN_SW5 ...
# 401.387 409.243 376.946 393.042 396.196 ...
#using custom parameters: lag=10, distance_matrix="grantham"
socn_all = protpy.sequence_order_coupling_number(protein_seq, lag=10, distance_matrix="grantham")
# SOCN_Grant1 SOCN_Grant_2 SOCN_Grant_3 SOCN_Grant_4 SOCN_Grant_5 ...
# 399.125 402.153 387.820 393.111 409.096 ... Calculate Quasi Sequence Order (QSO):
#using default parameters: lag=30, weight=0.1, distance_matrix="schneider-wrede"
qso = protpy.quasi_sequence_order(protein_seq)
# QSO_SW1 QSO_SW2 QSO_SW3 QSO_SW4 QSO_SW5 ...
# 0.005692 0.002643 0.004947 0.002846 0.005625 ...
#using custom parameters: lag=10, weight=0.2, distance_matrix="grantham"
qso = protpy.quasi_sequence_order(protein_seq, lag=10, weight=0.2, distance_matrix="grantham")
# QSO_Grant1 QSO_Grant2 QSO_Grant3 QSO_Grant4 QSO_Grant5 ...
# 0.123287 0.079967 0.04332 0.039983 0.013332 ... The documentation for protpy is hosted on ReadTheDocs and is available here.
/tests- unit and integration tests forprotpypackage./protpy- source code and all required external data files for package./docs-protpydocumentation./examples- example notebook for protpy
To run all tests, from the main protpy folder run:
python3 -m unittest discover tests -v
-v: verbose output flag
If you have any questions or comments, please contact [email protected] or raise an issue on the Issues tab.
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