Wardah, Wafaa and Dehzangi, Abdollah and Taherzadeh, G. and Rashid, Mahmood and Khan, Mohammad G.M. and Tsunoda, Tatsuhiko and Sharma, Alokanand (2020) Predicting protein-peptide binding sites with a Deep Convolutional Neural Network. Journal of Theoretical Biology, 110278 . TBC. ISSN 0022-5193
Full text not available from this repository. (Request a copy)Abstract
Interactions between proteins and peptides influence biological functions. Predicting such bio-molecular interactions can lead to faster disease prevention and help in drug discovery. Experimental methods for determining protein-peptide binding sites are costly and time-consuming. Therefore, computational methods have become prevalent. However, existing models show extremely low detection rates of actual peptide binding sites in proteins. To address this problem, we employed a two-stage technique - first, we extracted the relevant features from protein sequences and transformed them into images applying a novel method and then, we applied a convolutional neural network to identify the peptide binding sites in proteins. We found that our approach achieves 67% sensitivity or recall (true positive rate) surpassing existing methods by over 35%
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Office of the DVC (ARC) |
| Depositing User: | Fulori Nainoca - Waqairagata |
| Date Deposited: | 20 Apr 2020 04:08 |
| Last Modified: | 20 Apr 2020 04:08 |
| URI: | https://repository.usp.ac.fj/id/eprint/12096 |
Actions (login required)
 |
View Item |