Abstract

To tackle the difficulty of interpreting protein function only from sequences of amino acid, our paper suggests a novel strategy. When it comes to efficiently managing extremely divergent sequences or misaligned sections, traditional methods frequently fall short. In order to resolve the issue, we present a new, deep learning (DL) technique that exploit neural networks to discover complex connections between functional annotations and protein sequences. For a immense spectrum of complex protein sequences, our approach greatly improves the prediction of type of proteins as well as DNAs and RNAs also helps in diagnosis by precisely detecting minor alterations in protein sequences linked to a range of disorders.