pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou’s General PseAAC
Methods: To counter the prevailing, laborious and time-consuming experimental approaches, a novel computational model is introduced in the present study. Based on data collected from experimentally verified tyrosine nitration sites feature vectors are formed. Later, an adaptive training algorithm is used to train a back propagation neural network for prediction purposes. To objectively measure the accuracy of the proposed model, rigorous verification and validation tests are carried out.
Results: Through verification and validation, a promising accuracy of 88%, a sensitivity of 85%, a specificity of 89.18% and Mathew’s Correlation Coefficient of 0.627 is achieved.
Conclusion: It is concluded that the proposed computational model provides the foundation for further investigation and be used for the identification of nitrotyrosine sites in proteins.
Document Type: Research Article
Publication date: September 1, 2018
This article was made available online on December 27, 2018 as a Fast Track article with title: "pNitro-Tyr-PseAAC: Predict Nitrotyrosine Sites in Proteins by Incorporating Five Features into Chou’s General PseAAC".
- Current Pharmaceutical Design publishes timely in-depth reviews covering all aspects of current research in rational drug design. Each issue is devoted to a single major therapeutic area. A Guest Editor who is an acknowledged authority in a therapeutic field has solicits for each issue comprehensive and timely reviews from leading researchers in the pharmaceutical industry and academia.
Each thematic issue of Current Pharmaceutical Design covers all subject areas of major importance to modern drug design, including: medicinal chemistry, pharmacology, drug targets and disease mechanism.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites