Package: pyagadir
α-helix probability model (AGADIR)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).InstallThis package has been uploaded to the Python Package Index (PyPI) and can be installed with:pip install pyagadirUsageThe most simple way to use this package is to import and invokepredict_alphahelix()whereresult.helical_propensityis the probability that each residue is the α-helical conformation (list of floats) andresult.percent_helixis the mean helical propensity (probability) for the full peptide (float):>>>frompyagadirimportpredict_alphahelix>>>result=predict_alphahelix('ILKSLEEFLKVTLRSTRQT')>>>print(f'Percent helix:{result.percent_helix}')>>>print(f'Per-residue helical propensity:{result.helical_propensity}')Percenthelix:0.092Per-residuehelicalpropensity:[0.007343070.017175280.035175540.138308980.161293710.173977030.177885640.178593960.179036030.174992250.142506470.121570490.103879330.076534580.024859160.013937120.009787550.004624150.00114698]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 classAGADIRcan be directly imported and invoked. The result object is an instance ofModelResult(found inpyagadir.models) with more detailed free energy values saved during calculation (stored values are listed below). Example:>>>frompyagadir.modelsimportAGADIR>>>model=AGADIR(method='r')>>>result=model.predict('ILKSLEEFLKVTLRSTRQT')>>>print(f'dG_Int array (kcal/mol):{result.self.int_array}')dG_Intarray(kcal/mol):[0.960.80.761.130.80.950.951.080.80.761.121.180.80.671.131.180.670.931.18]Stored Data in ModelResult> 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)To DoImplement multiple-sequence approximation (Munoz, V., & Serrano, L. (1997))Cythonize the modelpytestsFor developersBuild package with build (seehttps://github.com/pypa/build)python -m buildCitationsMuñoz, 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/nsb0694-399Munoz, 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.1994.0023Muñoz, 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.1994.0024Lacroix, 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.1998.2145Munoz, 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