Prediction of 8-state protein secondary structures by 1D-Inception and BD-LSTM

AbstractProtein secondary structure is crucial to create an information bridge between the primary structure and the tertiary (3D) structure. Precise prediction of 8-state protein secondary structure (PSS) significantly utilized in the structural and functional analysis of proteins in bioinformatics. In this recent period, deep learning techniques have been applied in this research area and raise the Q8 accuracy remarkably. Nevertheless, from a theoretical standpoint, there still lots of room for improvement, specifically in 8-state (Q8) protein secondary structure prediction. In this paper, we presented two deep learning architecture, namely 1D-Inception and BD-LSTM, to improve the performance of 8-classes PSS prediction. The input of these two architectures is a carefully constructed feature matrix from the sequence features and profile features of the proteins. Firstly, 1D-Inception is a Deep convolutional neural network-based approach that was inspired by the InceptionV3 model and containing three inception modules. Secondly, BD-LSTM is a recurrent neural network model which including bidirectional LSTM layers. Our proposed 1D-Inception method achieved 76.65%, 71.18%, 76.86%, and 74.07% Q8 accuracy respectively on benchmark CullPdb6133, CB513, CASP10, and CASP11 datasets. Moreover, BD-LSTM acquired 74.71%, 69.49%, 74.07%, and 72.37% state-8 accuracy after evaluated on CullPdb6133, CB513, CASP10, and CASP11 datasets, respectively. Both these architectures enable the efficient processing of local and global interdependencies between amino acids to make an accurate prediction of each class is very beneficial in the deep neural network. To the best of our knowledge, experiment results of the 1D-Inception model demonstrate that it outperformed all the state-of-art methods on the benchmark CullPdb6133, CB513, and CASP10 datasets.

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A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure

Current Bioinformatics ◽

10.2174/1574893615666200120103050 ◽

2020 ◽

Vol 15 (7) ◽

pp. 767-777

Author(s):

Lin Guo ◽

Qian Jiang ◽

Xin Jin ◽

Lin Liu ◽

Wei Zhou ◽

...

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Convolutional Neural Network ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Deep Convolutional Neural Network ◽

Dimensional Structure ◽

Evolutionary Information ◽

Protein Secondary Structures

Background: Protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics that is helpful for understanding the three-dimensional structure and biological function of proteins. Many neural network-based prediction methods have been developed for protein secondary structures. Deep learning and multiple features are two obvious means to improve prediction accuracy. Objective: To promote the development of PSSP, a deep convolutional neural network-based method is proposed to predict both the eight-state and three-state of protein secondary structure. Methods: In this model, sequence and evolutionary information of proteins are combined as multiple input features after preprocessing. A deep convolutional neural network with no pooling layer and connection layer is then constructed to predict the secondary structure of proteins. L2 regularization, batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better prediction performance, and an improved cross-entropy is used as the loss function. Results: Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%, respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8 prediction results of 74.1%, 70.5%, 74.9%, and 71.3%. Conclusion: We have proposed the DCNN-SS deep convolutional-network-based PSSP method, and experimental results show that DCNN-SS performs competitively with other methods.

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Long sequence feature extraction based on deep learning neural network for protein secondary structure prediction

2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC) ◽

10.1109/itoec.2017.8122472 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yehong Chen

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Sequence Feature ◽

Protein Secondary Structure Prediction ◽

Deep Learning Neural Network

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Protein Secondary Structure Prediction With a Reductive Deep Learning Method

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.687426 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhiliang Lyu ◽

Zhijin Wang ◽

Fangfang Luo ◽

Jianwei Shuai ◽

Yandong Huang

Keyword(s):

Deep Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Secondary Structures ◽

Resonance Spectroscopy ◽

Data Set ◽

X Ray Crystallography ◽

Protein Secondary Structures

Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.

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In Silico Study of Secondary Structure of Hemoglobin Protein

Research Journal of Pharmacy and Technology ◽

10.52711/0974-360x.2021.01080 ◽

2021 ◽

pp. 6245-6249

Author(s):

Roma Chandra

Keyword(s):

Secondary Structure ◽

Protein Sequence ◽

Structure Prediction ◽

Tertiary Structure ◽

Secondary Structure Prediction ◽

Three Dimensional ◽

Protein Secondary Structure ◽

Alpha Helix ◽

Prediction Methods ◽

Protein Secondary Structures

Protein structure prediction is one of the important goals in the area of bioinformatics and biotechnology. Prediction methods include structure prediction of both secondary and tertiary structures of protein. Protein secondary structure prediction infers knowledge related to presence of helixes, sheets and coils in a polypeptide chain whereas protein tertiary structure prediction infers knowledge related to three dimensional structures of proteins. Protein secondary structures represent the possible motifs or regular expressions represented as patterns that are predicted from primary protein sequence in the form of alpha helix, betastr and and coils. The secondary structure prediction is useful as it infers information related to the structure and function of unknown protein sequence. There are various secondary structure prediction methods used to predict about helixes, sheets and coils. Based on these methods there are various prediction tools under study. This study includes prediction of hemoglobin using various tools. The results produced inferred knowledge with reference to percentage of amino acids participating to produce helices, sheets and coils. PHD and DSC produced the best of the results out of all the tools used.

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

PLoS ONE ◽

10.1371/journal.pone.0245982 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0245982

Author(s):

Yawu Zhao ◽

Yihui Liu

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Structure Prediction ◽

Short Term Memory ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Short Term ◽

Protein Secondary Structure Prediction ◽

Term Memory ◽

Long Short Term Memory

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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