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\begin{document}

\title{OneHotEncoding and LSTM based Deep Learning Models for Protein Secondary
Structure Prediction}



\author[1]{Vamsidhar Enireddy}%
\author[1]{C Karthikeyan }%
\affil[1]{Koneru Lakshmaiah Education Foundation}%


\vspace{-1em}



  
  \date{May 07, 2024}


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\begin{abstract}
Protein Secondary Structure (PSS) prediction is crucial for examining
and studying the protein structure and its function. PSS helps to
predict the tertiary structure and offers to understand about its
structures, which in turn helps to design various drugs. The existing
PSS prediction techniques are capable of achieving Q3 accuracy of nearly
80\%, and there has not any improvement till now. In this paper, we
propose a novel technique that uses amino acid sequences alone as an
input feature, and the respected feature vector matrix is given through
the deep learning model (DLM) for PSS prediction. Apart from all deep
learning methods, we use OneHotEncoding and LSTM (Long short term
memory) technique to forecast PSS that helps to give more accuracy. The
one hot encoder is used to extract the local contexts of amino-acid
sequences, and BLSTM (Bi-directional LSTM) captures the long-distance
interdependencies among amino-acids. LSTM is one of the new deep
learning models successfully applied in the field of bioinformatics to
solve problems. LSTM is very efficient in mapping the long term
dependencies of sequence information, which is more capable than the
convolutional neural networks (CNN's). The performance of the proposed
system is estimated on the openly available datasets such as CullPDB,
CASP10, and CASP11. Results show that the performance of the proposed
technique achieved superior outcomes than the existing approaches on the
three similar datasets.%
\end{abstract}%



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