Example of input vector for scikit-learn

>seq1
MRQA
HECH
—
Description:

y : labels (for example 0 = H = HELIX ; 1 = E = BETA ; 2 = C = COIL)
X : features (one-hot encoding sliding window)

One-hot encoding:
A -> [1, 0, 0, 0, 0, ...]
M -> [0, 1, 0, 0, 0, ...]
R -> [0, 0, 1, 0, 0, ...]
Q -> [0, 0, 0, 1, 0, ...]
L -> [0, 0, 0, 0, 1, ...]

Example:

Window of 3, second position. Sequence: MRQ

X = [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0]

y = [1]

Second and third position: MRQ, RQA:

X = np.array([ [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0],
               [0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0]])
y = [1, 2]

Note: this example assumes there are only 5 amino acids. Your vectors should be actually longer.

Further reading: https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/