Prediction of C alpha-H...O and C alpha-H...pi interactions in proteins using recurrent neural network.
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.
Kaur
Harpreet
H
Institute of Microbial Technology, Sector 39A, Chandigarh, India.
Raghava
Gajendra Pal Singh
GP
D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D030562:Databases, Protein; D006860:Hydrogen Bonding; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry / Q000235:genetics; D020539:Sequence Analysis, Protein; D012984:Software
D011506:Proteins