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| Titel: |
Prediction of Cα-H·O and Cα-H·π Interactions in Proteins Using Recurrent Neural Network |
| Auteur: |
Kaur, Harpreet Raghava, Gajendra Pal Singh |
| Verschenen in: |
In silico biology |
| Paginering: | Jaargang 6 (2006) nr. 1-2 pagina's 111-125 |
| Jaar: |
2006-07-05 |
| Inhoud: |
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. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 12 van 14 gevonden artikelen
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