Protein secondary structure assignment using pc ‐polyline and convolutional neural network

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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Knowledge-based protein secondary structure assignment

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.340230412 ◽

1995 ◽

Vol 23 (4) ◽

pp. 566-579 ◽

Cited By ~ 1652

Author(s):

Dmitrij Frishman ◽

Patrick Argos

Keyword(s):

Secondary Structure ◽

Protein Secondary Structure ◽

Knowledge Based ◽

Secondary Structure Assignment ◽

Structure Assignment

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Protein Secondary Structure Prediction Using Convolutional Neural Network and Softmax

Computer Science and Application ◽

10.12677/csa.2019.92051 ◽

2019 ◽

Vol 09 (02) ◽

pp. 450-457

Author(s):

蕾蕾王

Keyword(s):

Neural Network ◽

Secondary Structure ◽

Convolutional Neural Network ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Protein Secondary Structure Prediction

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Predicting Secondary Structure of Protein Using Hybrid of Convolutional Neural Network and Support Vector Machine

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2021.0228.23 ◽

2021 ◽

Vol 14 (1) ◽

pp. 232-243

Author(s):

Vincent Sutanto ◽

◽

Zaki Sukma ◽

Afiahayati Afiahayati ◽

◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Secondary Structure ◽

Convolutional Neural Network ◽

Structure Prediction ◽

Secondary Structure Prediction ◽

Protein Secondary Structure ◽

Support Vector ◽

Classification Problems ◽

Protein Secondary Structure Prediction

Protein secondary structure prediction is one of the problems in the Bioinformatics field, which conducted to find the function of proteins. Protein secondary structure prediction is done by classifying each sequence of protein primary structure into the sequence of protein secondary structure, which fall in sequence labelling problems and can be solved with the machine learning. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are 2 methods that often used to solve classification problems. In this research, we proposed a hybrid of 1-Dimensional CNN and SVM to predict the secondary structure of the protein. In this research, we used a novel hybrid 1-Dimensional CNN and SVM for sequence labelling, specifically to predict the secondary structure of the protein. Our hybrid model managed to outperform previous studies in term of Q3 and Q8 accuracy on CB513 dataset.

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