Encoder-decoder models for protein secondary structure prediction

Abstract

Proteins are arranged in a linear sequence due to peptide bonds. In proteins, a peptide bond combines the amino group of one protein with the carboxyl group of another protein. Protein secondary structure formation results from their bio-physical and biochemical properties, like natural languages which depend on their grammatical rule. So, the proposed model predicts a secondary structure from protein primary sequences using the encoder-decoder based machine translation method. The proposed model uses an encoder-decoder model based on long shortterm memory network. The proposed work uses training and testing performed on available public datasets, namely CullPDB and data1199. The proposed model has better Q3 accuracy of 84.87% and 87.39% for CullPDB and data1199, respectively. Further, the proposed work was evaluated by comparing their performance with other methods which predict secondary structure only from a single sequence. The Encoder-Decoder Model for predicting secondary structure from a single primary sequence is performing better than other single sequence-based methods. © 2023 Scrivener Publishing LLC. All rights reserved.