Protein Secondary Structure Prediction Using Ensemble Neural Networks With Local And Long-range Amino-acid Features
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Date
2021-10
Authors
Fawaz Hameed Hazzaa Mahyoub
Journal Title
Journal ISSN
Volume Title
Publisher
Universiti Sains Malaysia
Abstract
Predicting protein structures from sequences is a challenging problem. Determining
the secondary structures of the protein is an effective approach to infer the complete
protein structure. The interactions of local and long-range amino-acid residues in proteins
are key contributors in defining the protein secondary structures. Recent works
have focused on capturing local and long-range amino-acid interactions using various
predicted protein structural features via an ensemble of deep learning techniques.
Nevertheless, determining these structural features is always associated with intensive
computing. Moreover, their predictive performance is heavily relied on the quality of
the data features resulting from evolutionarily related proteins. This study proposes
a method for predicting protein secondary structure by incorporating Feed-Forward
Neural Network (FFNN) with bidirectional Long Short-Term Memory (LSTM) networks
to capture local and long-range amino-acid interactions. To further improve the
prediction accuracy of proteins with few evolutionarily related proteins, additional data
features based on the physicochemical properties of amino acids have been proposed.
The empirical outcomes indicate that the proposed method in this study shows competitive
prediction accuracy compared to Sequence-based Prediction Online Tools for
one dimensional structural features (SPOT-1D) and PORTER5. In addition to that, the
method outperformed several well-known cutting-edge methods by 2-3 percentagepoint
improvement.
Description
Keywords
Protein Secondary Structure Prediction , Local And Long-range Amino-acid Features