DEEP NEURAL NETWORKS FOR AFFINITY PREDICTION OF SPINOCEREBELLAR ATAXIA USING PROTEIN STRUCTURES

Abstract

Drug identification for exceptional hereditary disorder like spinocerebellar ataxia (SCA) is defy and an obligatory chore in biomedical investigate. There are numeral paths obtainable for affinity prediction through diverse scores and features in a typical computational scaffold. However there is a need for creative approaches to further depict the affinity of spinocerebellar ataxia which will enable better prediction for drug identification. This research work depicts the importance of hotspots identification for protein-protein interaction and also rigid docking. The power of Deep Neural Networks is utilized to predict binding affinity patterns through 3d protein structures and interactive properties of the interacted complex that can be used for predicting binding affinity. The work is carried out with 626 protein structures to perform protein-protein interaction. The complexes attained from the protein-protein interaction are 313 and from these complexes features are extracted. Features like physio-chemical properties, energy calculations, interfacial and non-interfacial properties are extracted from the complexes to model the binding affinity and a dataset with 313 instances is developed. Deep learning architectures learn complicated patterns, by gradually building from simpler ones. Deep learning models has many layers, when it goes deeper the model gets refined to provide better results. Input is given as feature vectors for the reason that of the docking process. Deep neural network works well for prediction by learning the features and the signals between them and the experiments discovered the dominance of deep learning neural network when compared to traditional ensemble learning.