Recurrent Neural Network Encoding Decoding Translator based Prediction Protein Function and Functional Annotation

Protein sequences are symbols generally different characters representing the 20 amino acids used in human proteins those sequences can range from the very sort to the very long. There are many proteins database for the sequences are known but the function and functional annotation is not. Protein function prediction (PFP) as well as functional annotation (FA) from its structure or sequence is a major field of bioinformatics at the same time how to judge how well perform these algorithms. We proposed the novel method that converts the protein function problem into a language translation problem by a new proposed protein sequence language encoded to the protein function language decoded and build a recurrent neural machine encoding decoding translator (RNNEDT) based on the recurrent neural networks model. The excellent acting on training, testing datasets exhibits the proposed system as an improving direction for PFP. The proposed system alters the PFP matter to a language translation issue as well as applies a recurrent neural network machine version model for PFP, and visualizes the annotation of biological process (BP), molecular function (MF), as well as cellular component (CP).

I. Introduction

Proteins are the major biological mechanisms that make life possible. There are around 54 million protein sequences and members chose are exemplars of around 1.4 million protein sequences [1]. Protein function prediction is also a multi-label classification problem. The suggested technique uses the Modern Machine Learning (ML) method to better understand and forecast biological protein function and functional annotation in the field of biological process (BP), molecular function (MP), and cellular components (CC). The design space of protein is much larger than what we observe in the real world. To address this challenge, we are interested in computational and experimental work to modify and optimize proteins for a variety of uses in the field of and cellular components, biological processes, molecular functions. Bioinformatics researchers use protein prediction methods to assign biological or biochemical roles. The term "protein function" refers to a protein's molecular functions, including gene regulation, material transport, and biochemical reaction catalytic (enzymes) catalysts. These proteins are typically poorly studied or forecast based on genomic sequence information. These predictions are usually driven by computational processes with extensive data. Information may be derived from the homology of nucleic acid sequence, gene expression profiles, the structures of protein domains, the mining of publications, the profiles of phylogenies, and phénotypes. Protein function is a broad term, and a single protein can play a role in several processes or cell pathways, ranging from catalysts for biochemical reactions to transports and transduction. The function can generally be considered as "anything that occurs with or through a protein".