Affinity Prediction of Spinocerebellar Ataxia using Protein-Ligand and Protein-Protein Interactions with Functional Deep Learning
P. R. Asha1, M. S. Vijaya2
1P. R. Asha, Department of Computer Science, Krishnammal College for Women, Coimbatore (Tamil Nadu), India.
2M. S. Vijaya, Department of Computer Science, Krishnammal College for Women, Coimbatore (Tamil Nadu), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 858-865 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7279068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Drug discovery of incomparable hereditary disorder like spinocerebellar ataxia is confronted and an enforce task in biomedical study. There are number of paths available for affinity prediction through scoring functions and ideals in the catalog. Nevertheless there is a need for artistic access in portraying the affinity of spinocerebellar ataxia which will facilitate enhanced prediction for drug discovery. This research work portrays the significance of docking for protein-ligand interaction and protein-protein interaction with modeling through deep learning. Deep Neural Networks is utilized in predicting binding affinity with 3d protein structures and ligand. Predictive models have been built with features related to for protein-ligand interaction and protein-protein interaction. In the first case, 17 protein structures and 18 ligands were used. Each protein structure is docked with ligand to get essential features like energy calculations, properties of protein and ligand for predicting binding affinity. In the next case, repeat mutation is induced manually with 17 protein structures and docked with 18 ligands. To train the model, well-defined descriptors are squeezed from the docked complex. Third case employs protein-protein interaction of total of 626 protein structures and the complexes attained from the protein-protein interaction are 313. Features like energy calculations, physio-chemical properties and interfacial and non-interfacial properties are extracted for learning this model. Deep learning has the property of representation learning from the user defined features, which helps in accurate prediction of binding affinity. The predictive models are developed with functional deep neural network and their performances are compared with sequential deep neural network. Functional deep neural network have more flexibility to define layers, complements sequential deep neural network which results in improved performance.
Keywords: Binding Affinity, Deep Neural Network, Docking, Functional Deep Neural Network, Optimizers, Prediction, Protein Structure, Repeat Mutation
Scope of the Article: Deep Learning