  The biological function of a protein stems from its 3-dimensional structure,
which is thermodynamically determined by the energetics of interatomic forces
between its amino acid building blocks (the order of amino acids, known as the
sequence, defines a protein). Given the costs (time, money, human resources) of
determining protein structures via experimental means such as X-ray
crystallography, can we better describe and compare protein 3D structures in a
robust and efficient manner, so as to gain meaningful biological insights? We
begin by considering a relatively simple problem, limiting ourselves to just
protein secondary structural elements. Historically, many computational methods
have been devised to classify amino acid residues in a protein chain into one
of several discrete secondary structures, of which the most well-characterized
are the geometrically regular $\alpha$-helix and $\beta$-sheet; irregular
structural patterns, such as 'turns' and 'loops', are less understood. Here, we
present a study of Deep Learning techniques to classify the loop-like end cap
structures which delimit $\alpha$-helices. Previous work used highly empirical
and heuristic methods to manually classify helix capping motifs. Instead, we
use structural data directly--including (i) backbone torsion angles computed
from 3D structures, (ii) macromolecular feature sets (e.g., physicochemical
properties), and (iii) helix cap classification data (from CAPS-DB)--as the
ground truth to train a bidirectional long short-term memory (BiLSTM) model to
classify helix cap residues. We tried different network architectures and
scanned hyperparameters in order to train and assess several models; we also
trained a Support Vector Classifier (SVC) to use as a baseline. Ultimately, we
achieved 85% class-balanced accuracy with a deep BiLSTM model.
