In this study, an attempt has been made to develop a method for
predicting weak hydrogen bonding interactions, namely,
C
$^{α}$
-H·O and
C
$^{α}$
-H·π interactions in proteins using
artificial neural network. Both standard feed-forward neural network (FNN) and
recurrent neural networks (RNN) have been trained and tested using five-fold
cross-validation on a non-homologous dataset of 2298 protein chains where no
pair of sequences has more than 25% sequence identity. It has been found that
the prediction accuracy varies with the separation distance between donor and
acceptor residues. The maximum sensitivity achieved with RNN for
C
$^{α}$
-H·O is 51.2% when donor and acceptor
residues are four residues apart (i.e. at Δ
$_{D-A}$
=4)
and for C
$^{α}$
-H·π is 82.1% at
Δ
$_{D-A}$
=3. The performance of RNN is increased by
1–3% for both types of interactions when PSIPRED predicted protein
secondary structure is used. Overall, RNN performs better than feed-forward
networks at all separation distances between donor-acceptor pair for both types
of interactions. Based on the observations, a web server CHpredict (available
at http://www.imtech.res.in/raghava/chpredict/) has been developed for
predicting donor and acceptor residues in
C
$^{α}$
-H·O and
C
$^{α}$
-H·π interactions in proteins.
