An open-source, Python implementation of Munoz & Serrano's AGADIR model of α-helix formation. This model uses statistical mechanics and energy parameters trained on a database of over 400 peptides to predict the α-helical tendency (probability) per residue for a given peptide (see references).
This package has been uploaded to the Python Package Index (PyPI (https://pypi.org/project/pyagadir/) and can be installed with:
pip install pyagadirThe most simple way to use this package is to import and invoke predict_alphahelix() where result.helical_propensity is the probability that each residue is the α-helical conformation (list of floats) and result.percent_helix is the mean helical propensity (probability) for the full peptide (float):
>>> from pyagadir import predict_alphahelix
>>> result = predict_alphahelix('ILKSLEEFLKVTLRSTRQT')
>>> print(f'Percent helix: {result.percent_helix}')
>>> print(f'Per-residue helical propensity: {result.helical_propensity}')Percent helix: 0.092
Per-residue helical propensity: [0.00734307 0.01717528 0.03517554 0.13830898 0.16129371 0.17397703
0.17788564 0.17859396 0.17903603 0.17499225 0.14250647 0.12157049
0.10387933 0.07653458 0.02485916 0.01393712 0.00978755 0.00462415
0.00114698]Advanced users may want to modify the partition function to an alternate approximation (e.g. residue, 'r') or inspect the detailed dG predicted values. The model class AGADIR can be directly imported and invoked. The result object is an instance of ModelResult (found in pyagadir.models) with more detailed free energy values saved during calculation (stored values are listed below). Example:
>>> from pyagadir.models import AGADIR
>>> model = AGADIR(method='r')
>>> result = model.predict('ILKSLEEFLKVTLRSTRQT')
>>> print(f'dG_Int array (kcal/mol): {result.int_array}')dG_Int array (kcal/mol): [0.96 0.8 0.76 1.13 0.8 0.95 0.95 1.08 0.8 0.76 1.12 1.18 0.8 0.67
1.13 1.18 0.67 0.93 1.18]> seq :: peptide sequence (str)
# for each residue/index position
> int_array :: dG_Int (np.array of shape(seq,1))
> i1_array :: dG_i,i+1 (np.array of shape(seq,1))
> i3_array :: dG_i,i+3 (np.array of shape(seq,1))
> i4_array :: dG_i,i+4 (np.array of shape(seq,1))
> N_array :: dG_Ncap (np.array of shape(seq,1))
> C_array :: dG_Ccap (np.array of shape(seq,1))
> dG_dict_mat :: dG_dict's in list of lists where indexing corresponds to [j][i] (see Muñoz, V., & Serrano, L. (1994)); dG_dict includes each term used in computing dG_Helix for a given helical segment of length j at position i (Python indexing).
# statistical weights and partition functions
> K_tot :: sum of statistical weights for AGADIR1s (one-sequence) (float)
> K_tot_array :: array of summed statistical weights for AGADIR (residue) (np.array of shape(seq,1))
> Z :: residue parition function for AGADIR1s (one-sequence) (float)
> Z_array :: residue parition function for AGADIR (residue) (np.array of shape(seq,1))
# final predicted values
> helical_propensity :: probability that each residue is in the alpha-helical conformation (np.array of shape(seq,1))
> percent_helix :: mean helical propensity, or probability of peptide is an alpha-helix (float)Build package with build (see https://github.com/pypa/build)
python -m buildMuñoz, V., & Serrano, L. (1994). Elucidating the folding problem of helical peptides using empirical parameters. Nature structural biology, 1(6), 399-409. https://doi.org/10.1038/nsb0694-399
Munoz, V., & Serrano, L. (1995). Elucidating the folding problem of helical peptides using empirical parameters. II†. Helix macrodipole effects and rational modification of the helical content of natural peptides. Journal of molecular biology, 245(3), 275-296. https://doi.org/10.1006/jmbi.1994.0023
Muñoz, V., & Serrano, L. (1995). Elucidating the Folding Problem of Helical Peptides using Empirical Parameters. III> Temperature and pH Dependence. Journal of molecular biology, 245(3), 297-308. https://doi.org/10.1006/jmbi.1994.0024
Lacroix, E., Viguera, A. R., & Serrano, L. (1998). Elucidating the folding problem of α-helices: local motifs, long-range electrostatics, ionic-strength dependence and prediction of NMR parameters. Journal of molecular biology, 284(1), 173-191. https://doi.org/10.1006/jmbi.1998.2145
Munoz, V., & Serrano, L. (1997). Development of the multiple sequence approximation within the AGADIR model of α‐helix formation: Comparison with Zimm‐Bragg and Lifson‐Roig formalisms. Biopolymers: Original Research on Biomolecules, 41(5), 495-509. https://doi.org/10.1002/(SICI)1097-0282(19970415)41:5<495::AID-BIP2>3.0.CO;2-H