Multi-S3P: Protein Secondary Structure Prediction with Specialized Multi-Network and Self-Attention-based Deep Learning Model
The standalone version of Multi-S3P is available for public use for research purposes.
This study presents Multi-S3P, which employs bidirectional Long-Short-Term-Memory (BILSTM) and Convolutional Neural Networks (CNN) with a self-attention mechanism to improve the secondary structure prediction using an effective training strategy to capture the unique characteristics of each type of secondary structure and combine them more effectively. The ensemble of CNN and BILSTM can learn both contextual information and long-range interactions between the residues. In addition, using a self-attention mechanism allows the model to focus on the most important features for improving performance. We used the SPOT-1D dataset for the training and validation of our model using a set of four input features derived from amino acid sequences. Further, the model was tested on four popular independent test datasets and compared with various state-of-the-art predictors. The presented results show that Multi-S3P outperformed the other methods in terms of Q3, Q8 accuracy and other performance metrics, achieving the highest Q3 accuracy of 87.57% and a Q8 accuracy of 77.56% on the TEST2016 test set. More importantly, Multi-S3P demonstrates high performance in SS boundary regions. Our experiment also demonstrates that the combination of different input features and a multi-network-based training strategy significantly improved the performance.
- Python 3.7
- Anaconda
- TensorFlow v2.0
The Multi-S3P predictor underwent testing on a typical Ubuntu 18 computer with approximately 16 GB of RAM, ensuring sufficient memory capacity to facilitate in-memory operations.
Train, Validation, TEST2016 & TEST2018 are obtained from SPOT-1D and available at: Dataset
Cite: Hanson, J., Paliwal, K., Litfin, T., Yang, Y., & Zhou, Y. (2019). Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks. Bioinformatics, 35(14), 2403-2410.
CASP12 & CASP13: https://predictioncenter.org/
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20PSSM from PSI-BLAST v2.10.0
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30HMM from HHBlits v3.1.0
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7PCP
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19PSP from OPUS-PSP
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Output: Secondary Structure Probabilities (prot_name.csv)
$ git clone https://github.com/mufassirin/Multi-S3P.git$ cd Multi-S3P
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Please download the pretrained model weights from dropbox here
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Place it in the folder Multi-S3P. or
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wget https://www.dropbox.com/scl/fo/pu9omax6s30ttb98ddazt/h?dl=0&rlkey=7gsdy48yiyuuzinsj7oxbva52
MIT LICENSE
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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