clophfit.binding#

Fit Cl binding and pH titration.

Functions:

fit_titration(kind, x, y[, y2, residue, ...])

Fit pH or Cl titration using a single-site binding model.

fz_kd_singlesite(k, p, x)

Fit function for Cl titration.

fz_pk_singlesite(k, p, x)

Fit function for pH titration.

kd(kd1, pka, ph)

Infinite cooperativity model.
clophfit.binding.fit_titration(kind, x, y, y2=None, residue=None, residue2=None, tval_conf=0.95)#

Fit pH or Cl titration using a single-site binding model.

Returns confidence interval (default=0.95) for fitting params (cov*tval), rather than standard error of the fit. Use scipy leastsq. Determine 3 fitting parameters: - binding constant K - and 2 plateau SA and SB.

Parameters:

  • kind (str) – Titration type {‘pH’|’Cl’}

  • x (Sequence[float]) – Dataset x-values.

  • y (NDArray[np.float]) – Dataset y-values.

  • y2 (NDArray[np.float], optional) – Optional second dataset y-values (share x with main dataset).

  • residue (NDArray[np.float], optional) – Residues for main dataset.

  • residue2 (NDArray[np.float], optional) – Residues for second dataset.

  • tval_conf (float) – Confidence level (default 0.95) for parameter estimations.

Returns:

Fitting results.

Return type:

pd.DataFrame

Raises:

NameError – When kind is different than “pH” or “Cl”.

Examples

>>> import numpy as np
>>> fit_titration("Cl", np.array([1.0, 10, 30, 100, 200]),           np.array([10, 8, 5, 1, 0.1]))[["K", "sK"]]
           K         sK
0  38.955406  30.201929
clophfit.binding.fz_kd_singlesite(k, p, x)#

Fit function for Cl titration.

Return type:

ndarray[Any, dtype[float64]]

Parameters:

  • k (float) –

  • p (ndarray[Any, dtype[float64]] | Sequence[float]) –

  • x (ndarray[Any, dtype[float64]]) –

clophfit.binding.fz_pk_singlesite(k, p, x)#

Fit function for pH titration.

Return type:

ndarray[Any, dtype[float64]]

Parameters:

  • k (float) –

  • p (ndarray[Any, dtype[float64]] | Sequence[float]) –

  • x (ndarray[Any, dtype[float64]]) –

clophfit.binding.kd(kd1, pka, ph)#

Infinite cooperativity model.

It can describe pH-dependence for chloride dissociation constant.

Parameters:

  • kd1 (float) – Dissociation constant at pH <= 5.0 (fully protonated).

  • pka (float) – Acid dissociation constant.

  • ph (Xtype) – pH value(s).

Returns:

Predicted Kd value(s).

Return type:

Xtype

Examples

>>> kd(10, 8.4, 7.4)
11.0
>>> import numpy as np
>>> kd(10, 8.4, np.array([7.4, 8.4]))
array([11., 20.])