A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure

Hu, Hailong; Zhong, Li; Elofsson, Arne; Xie, Shangxin

doi:10.3390/app9173538

Cited by 19 publications

(17 citation statements)

References 28 publications

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“…From an abstraction based perspective, protein backbone structure prediction could be viewed as prediction of secondary structures (SSs). Protein secondary structure prediction has obtained significant success over the years through the use of various types of deep neural networks and their ensembles [6][7][8][9][10][11][12] and ab initio methods 13 . For example, SSpro8 14 achieves 79% accuracy on proteins with no homologs in the Protein Data Bank (PDB) and of 92% accuracy on proteins where homologs can be found in the PDB.…”

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confidence: 99%

Enhancing protein backbone angle prediction by using simpler models of deep neural networks

Mataeimoghadam

Newton

Dehzangi

et al. 2020

Sci Rep

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Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6–8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap.

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confidence: 99%

Enhancing protein backbone angle prediction by using simpler models of deep neural networks

Mataeimoghadam

Newton

Dehzangi

et al. 2020

Sci Rep

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“…Compared with RNN, LSTM has designed the controller of the neural unit (Cell), which can judge whether the information is useful. In summary, the long-distance interdependencies [ 11 , 31 ] of amino acids are critical for protein secondary structure prediction. Therefore, local features extracted by the optimized convolutional neural network are sent to BiLSTM to obtain the long-distance dependencies of amino acids.…”

Section: Oclstmmentioning

confidence: 99%

OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

Zhao

Liu

2021

PLoS ONE

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Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.

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“…One is a regular secondary structure and it has three types which are α-Helices (H) and β-sheet (E), Coil (C) (Akkaladevi et al, 2005;Hobbes, 2019) and other is irregular secondary structure and it has many types such as Tight turns, Bulges, etc. According to DSSP, there are 8-class of secondary structures i.e., G (310 Helix) H, I (π-Helix) B (Isolated Bridge), E (Beta-strand), C, S(Bend), T (Tight turns) are converted into 3-class of secondary structure (Hu et al, 2019;Zhang et al, 2018;Yang et al, 2018;Hanson et al, 2019). {B, S, T, G, I, C} are converted into C, {H}> H, {E}>E. Methods of predicting protein secondary structures based on deep learning techniques are the most crucial problems in molecular biology.…”

Section: Introductionmentioning

confidence: 99%

Protein Secondary Structure Prediction using Hybrid Recurrent Neural Networks

Ema¹,

Khatun²,

Hossain³

et al. 2022

Journal of Computer Science

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The most important and challenging problem in bioinformatics is protein secondary structure prediction. The molecules of all protein organisms have three-dimensional (primary, secondary, 3-D) structures which are completely recognized by the sequence of amino acids. Protein secondary structure attributes to the polypeptide backbone of the local configuration of proteins. Most generally, the second-level prediction is indicated such as: If there is an amino acid sequence of the protein, then predict that all amino acid has in the α-Helices (H), β-sheet (E), and other Random Coils (C). In this study, Hybrid Recurrent Neural Networks (HRNN) have been proposed for the prediction of protein secondary structure to improve the prediction performance. The purpose of the work is to predict the protein secondary structure and bring out a highly accurate solution that would be easily solved in computational biology. The proposed method can experimentally perform exceedingly better than other previous work and this study could be easily understandable by researchers for solving the protein structure prediction problems. The five techniques are used for this implementation. These are Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bidirectional, Bidirectional Gated Recurrent Unit (BGRU), and Bidirectional Long Short-Term Memory (BLSTM) neural networks. Especially, the proposed two-dimensional recurrent neural network (2D-RNN) framework consisted of five models: 2D-GRU_RNN, 2D-LSTM_RNN, 2D-Bi_RNN, 2D-BiGRU_RNN, and 2D-BiLSTM_RNN. In this study, firstly the 2D recurrent neural network has been generated and combined the extracted features of protein sequence with Position-Specific Scoring Matrix (PSSM). After that, the model has been trained and tested with those datasets. Finally, the model has been evaluated for prediction. Besides, all prediction accuracy has been compared and improved with existing methods. These achievements are obtained 91% (BiGRU and BiLSTM), 92% (BiGRU), 89% (BiGRU and BiLSTM), 93% (BiGRU and BiLSTM), 88% (BiGRU), 86% (BiGRU), 91% (GRU), 87% (BiLSTM), 88% (BiGRU) and 93% (GRU and BiGRU) for predicting accuracy of Q3.

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A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure

Cited by 19 publications

References 28 publications

Enhancing protein backbone angle prediction by using simpler models of deep neural networks

Enhancing protein backbone angle prediction by using simpler models of deep neural networks

OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

Protein Secondary Structure Prediction using Hybrid Recurrent Neural Networks

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