TY  - JOUR
ID  - open148
UR  - http://www.bioinfo.de/isb/2006060011/
IS  - 1-2
A1  - Kaur, Harpreet
A1  - Raghava, G.P.S.
N2  - In this study, an attempt has been made to develop a method for predicting weak hydrogen bonding interactions, namely, C alpha-H...O and C alpha-H...pi 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 alpha-H...O is 51.2% when donor and acceptor residues are four residues apart (i.e. at delta D-A = 4) and for C alpha-H...pi is 82.1% at delta 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 alpha-H...O and C alpha-H...pi interactions in proteins.
VL  - 6
TI  - Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network.
AV  - public
EP  - 25
N1  - OPEN ACCESS
Y1  - 2006///
PB  - Bioinformation Systems e.V.
JF  - In silico biology
KW  - weak hydrogen bonds
KW  -  donor
KW  -  acceptor
KW  -  prediction
KW  -  neural network
KW  -  secondary structure 
SN  - 1386-6338
SP  - 111
ER  -