OPLS-MD 0.0.1

PLS and OPLS regressors

OPLS

OPLS and PLS regressors

Installation

Run

pip install .

Usage

Given a structure file and trajectory as struct.pdb and traj.xtc we can e.g. define a function as the distance of two specific C alpha atoms:

import MDAnalysis as mda
import numpy as np

# Make universe and load trajectory to memory
u = mda.Universe("struct.pdb","traj.xtc")
coords = np.array([u.atoms.positions.copy() for ts in u.trajectory])
# Calculate function as distance between res 2 and 150 CA
sel = u.select_atoms("resid 2 150 and name CA")
y = np.linalg.norm(coords[:,sel.indices[0],:]-coords[:,sel.indices[1],:], axis=-1)
# Flatten from N by M by 3 dimensions to N by 3M
X = coords.reshape((coords.shape[0],-1))

Now that we have shape N by 3M array of X values and a one dimensional y, we can start running the OPLS

from OPLS import PLS, OPLS

opls = OPLS(n_components=1).fit(X,y)

This will fit a one component OPLS model on the data. To get a new X array, where we have filtered out the orthogonal components, we can use the transform function

X_filt = opls.transform(X)

Finally to get our model, we can fit the PLS with the filtered data.

pls = PLS(n_components=5).fit(X_filt,y)

If we have a new set of data, called X_new, we can predict the y values for it as

X_new_filt = opls.transform(X_new)
y_new_predicted = pls.predict(X_new_filt)

or if the corresponding y_new are known, we can estimate the coefficient of determination (r2) with

X_new_filt = opls.transform(X_new)
r2 = pls.score(X_new_filt, y_new)

Having to run two separate models one after the other can be tiresome, so the package also includes as OPLS_PLS object, which does the above much easier:

from OPLS import OPLS_PLS

# Fitting:
opls_pls = OPLS_PLS(n_components=1,pls_components=5).fit(X,y)

# Predicting:
y_new_predicted = opls_pls.predict(X_new)

# Scoring:
r2 = opls_pls.score(X_new, y_new)

References

Main references for OPLS:

[1] Wold S, et al. PLS-regression: a basic tool of chemometrics.
    Chemometr Intell Lab Sys 2001, 58, 109–130.
[2] Bylesjo M, et al. Model Based Preprocessing and Background
    Elimination: OSC, OPLS, and O2PLS. in Comprehensive Chemometrics.