Prediction of Protein Secondary Structure Based on WS-BiLSTM Model
Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model...
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MDPI AG
2022-01-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/1/89 |
| _version_ | 1797490075934654464 |
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| author | Yang Gao Yawu Zhao Yuming Ma Yihui Liu |
| author_facet | Yang Gao Yawu Zhao Yuming Ma Yihui Liu |
| author_sort | Yang Gao |
| collection | DOAJ |
| description | Protein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction. |
| first_indexed | 2024-03-10T00:26:43Z |
| format | Article |
| id | doaj.art-775f0523a9a94e1b8eb8a3957e6cc28a |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T00:26:43Z |
| publishDate | 2022-01-01 |
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| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-775f0523a9a94e1b8eb8a3957e6cc28a2023-11-23T15:33:15ZengMDPI AGSymmetry2073-89942022-01-011418910.3390/sym14010089Prediction of Protein Secondary Structure Based on WS-BiLSTM ModelYang Gao0Yawu Zhao1Yuming Ma2Yihui Liu3School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250316, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250316, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250316, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250316, ChinaProtein secondary structure prediction is an important topic in bioinformatics. This paper proposed a novel model named WS-BiLSTM, which combined the wavelet scattering convolutional network and the long-short-term memory network for the first time to predict protein secondary structure. This model captures nonlocal interactions between amino acid sequences and remembers long-range interactions between amino acids. In our WS-BiLSTM model, the wavelet scattering convolutional network is used to extract protein features from the PSSM sliding window; the extracted features are combined with the original PSSM data as the input features of the long-short-term memory network to predict protein secondary structure. It is worth noting that the wavelet scattering convolutional network is asymmetric as a member of the continuous wavelet family. The Q3 accuracy on the test set CASP9, CASP10, CASP11, CASP12, CB513, and PDB25 reached 85.26%, 85.84%, 84.91%, 85.13%, 86.10%, and 85.52%, which were higher 2.15%, 2.16%, 3.5%, 3.19%, 4.22%, and 2.75%, respectively, than using the long-short-term memory network alone. Comparing our results with the state-of-art methods shows that our proposed model achieved better results on the CB513 and CASP12 data sets. The experimental results show that the features extracted from the wavelet scattering convolutional network can effectively improve the accuracy of protein secondary structure prediction.https://www.mdpi.com/2073-8994/14/1/89proteinprotein secondary structurePSSMwavelet scattering convolution networklong-short-term memory network |
| spellingShingle | Yang Gao Yawu Zhao Yuming Ma Yihui Liu Prediction of Protein Secondary Structure Based on WS-BiLSTM Model Symmetry protein protein secondary structure PSSM wavelet scattering convolution network long-short-term memory network |
| title | Prediction of Protein Secondary Structure Based on WS-BiLSTM Model |
| title_full | Prediction of Protein Secondary Structure Based on WS-BiLSTM Model |
| title_fullStr | Prediction of Protein Secondary Structure Based on WS-BiLSTM Model |
| title_full_unstemmed | Prediction of Protein Secondary Structure Based on WS-BiLSTM Model |
| title_short | Prediction of Protein Secondary Structure Based on WS-BiLSTM Model |
| title_sort | prediction of protein secondary structure based on ws bilstm model |
| topic | protein protein secondary structure PSSM wavelet scattering convolution network long-short-term memory network |
| url | https://www.mdpi.com/2073-8994/14/1/89 |
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