DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system

Yuan, Lu; Hu, Xiaopei; Ma, Y.; Liu, Yihui

doi:10.1039/d2ra06433b

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…OPUS-TASS [10] recently proposed an architecture based on Transformer along with CNN and LSTM to capture the interactions between the two residues in order to enhance the SS prediction accuracy. A recent method called DLBLS_SS [40] utilized the BILSTM network and temporal convolutional networks to construct the model. In addition, some deep learning-based methods employed huge datasets like ProteinNet [41] to train the model in order to improve the prediction accuracy [42].…”

Section: Related Workmentioning

confidence: 99%

“…Using the given independent datasets, the performance of the proposed method was tested and compared against various state-of-the-art secondary structure prediction methods, including DeepCNF [37], SPIDER3 [31], RaptorX [11], PSRSM [20], MUFOLD-SS [19], NetSurfP-2.0 [38], Porter 5 [58], SPOT-1D [32], DNSS2 [24], DLBLS-SS [40] and DML_SS [48]. All the approaches were evaluated in terms of Q3, Q8 accuracy and SOV scores on each test dataset.…”

Section: A Datasetmentioning

confidence: 99%

“…The absence of a result is denoted by the symbol ''−''. methods such as DeepCNF [37], SPIDER3 [31], Porter5 [58], MUFOLD-SS [19], SPOT-1D [32], DNSS2 [24], and DLBLS-SS [40], as well as two methods proposed in the paper the Base-model and the Multi-S3P. The table provides a clear representation of the experimental results obtained on the CASP12 and CASP13 datasets, with the Q3, Q8 accuracy and SOV measure for each method.…”

Section: Tablementioning

confidence: 99%

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Multi-S3P: Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model

et al. 2023

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Protein structure prediction (PSP) is a vital challenge in bioinformatics, structural biology and drug discovery. Protein secondary structure (SS) prediction is critical since three-dimensional (3D) structures are primarily made up of secondary structures. With the advancement of deep learning approaches, SS classification accuracy has been significantly improved. Many existing methods use an ensemble of complex neural networks to improve SS prediction. Because of the high dimensionality of the hyperparameter space, deep neural networks with complex architectures are typically challenging to train effectively. Also, predicting secondary structures in the boundary regions between different types of SS is challenging. 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.

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