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protein secondary structure prediction based on Wasserstein generative adversarial networks and temporal convolutional networks with convolutional block attention modules

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 2203-2218页

作者： Yuan, Lu Ma, Yuming Liu, Yihui Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan 250353 Peoples R China

As an important task in bioinformatics, protein secondary structure prediction (PSSP) is not only beneficial to protein function research and tertiary structure prediction, but also to promote the design and development of new drugs. However, current PSSP methods cannot sufficiently extract effective features. In this study, we propose a novel deep learning model WGACSTCN, which combines Wasserstein generative adversarial network with gradient penalty (WGAN-GP), convolutional block attention module (CBAM) and temporal convolutional network (TCN) for 3-state and 8-state PSSP. In the proposed model, the mutual game of generator and discriminator in WGAN-GP module can effectively extract protein features, and our CBAM-TCN local extraction module can capture key deep local interactions in protein sequences segmented by sliding window technique, and the CBAM-TCN long-range extraction module can further capture the key deep long-range interactions in sequences. We evaluate the performance of the proposed model on seven benchmark datasets. Experimental results show that our model exhibits better prediction performance compared to the four state-of-the-art models. The proposed model has strong feature extraction ability, which can extract important information more comprehensively.

关键词： protein secondary structure prediction Wasserstein generative adversarial network convolutional block attention module temporal convolutional network feature extraction

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Enhanced protein secondary structure prediction Through Multi-View Multi-Feature Evolutionary Deep Fusion Method

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Qian, Yining Su, Lijie Xu, Meiling Tang, Lixin Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China Northeastern Univ Minist Educ Key Lab Data Analyt & Optimizat Smart Ind Shenyang 110819 Peoples R China Liaoning Engn Lab Data Analyt & Optimizat Smart In Shenyang 110819 Peoples R China Liaoning Key Lab Mfg Syst & Logist Optimizat Shenyang 110819 Peoples R China Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China

Understanding protein secondary structure is crucial for analyzing their functions and designing drugs. Accurately predicting its eight states is both vital and challenging. Despite numerous proposed methods, research on representing proteins from multiple views and features remains limited. Moreover, effectively selecting and fusing this information remains an unresolved issue, which hampers prediction accuracy. In this paper, the Multi-View Multi-Feature Evolutionary Deep Fusion (MVMF-EDF) method, which consists of two stages, is proposed to address this challenge. First, a multi-view multi-feature (MVMF) strategy is introduced to construct protein representations from various views and features. Notably, MVMF-EDF incorporates semantic information from a pre-trained language model to enhance protein representation. Second, an evolutionary deep fusion (EDF) approach is employed to automatically select and fuse the extracted features. Experimental results on four benchmark datasets show that MVMF-EDF outperforms baseline models in predicting protein secondary structure. This approach leverages deep neural networks for feature extraction and evolutionary algorithms for feature integration, achieving high accuracy without requiring manual feature fusion design.

关键词： proteins Hidden Markov models Feature extraction Vectors Biological system modeling protein engineering Optimization Convolutional neural networks Accuracy Manuals Multi-view multi-feature protein secondary structure prediction evolutionary fusion method feature selection feature fusion differential evolution algorithm

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Post-processing enhances protein secondary structure prediction with second order deep learning and embeddings

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2025年 27卷 243-251页

作者： Chatzimiltis, Sotiris Agathocleous, Michalis Promponas, Vasilis J. Christodoulou, Chris Univ Cyprus Dept Comp Sci Nicosia Cyprus Univ Nicosia Dept Comp Sci Nicosia Cyprus Univ Cyprus Dept Biol Sci Nicosia Cyprus Univ Surrey Inst Commun Syst ICS 5G 6GIC Guildford England

protein secondary structure prediction (PSSP) is regarded as a challenging task in bioinformatics, and numerous approaches to achieve a more accurate prediction have been proposed. Accurate PSSP can be instrumental in inferring protein tertiary structure and their functions. Machine Learning and in particular Deep Learning approaches show promising results for the PSSP problem. In this paper, we deploy a Convolutional Neural Network (CNN) trained with the Subsampled Hessian Newton (SHN) method (a Hessian Free Optimisation variant), with a two- dimensional input representation of embeddings extracted from a language model pretrained with protein sequences. Utilising a CNN trained with the SHN method and the input embeddings, we achieved on average a 79.96% per residue (Q3) accuracy on the CB513 dataset and 81.45% Q3 accuracy on the PISCES dataset (without any post-processing techniques applied). The application of ensembles and filtering techniques to the results of the CNN improved the overall prediction performance. The Q3 accuracy on the CB513 increased to 93.65% and for the PISCES dataset to 87.13%. Moreover, our method was evaluated using the CASP13 dataset where we showed that as the post-processing window size increased, the prediction performance increased as well. In fact, with the biggest post-processing window size (limited by the smallest CASP13 protein), we achieved a Q3 accuracy of 98.12% and a Segment Overlap (SOV) score of 96.98 on the CASP13 dataset when the CNNs were trained with the PISCES dataset. Finally, we showed that input representations from embeddings can perform equally well as representations extracted from multiple sequence alignments.

关键词： Convolutional neural networks Deep learning Embeddings Hessian free optimisation protein secondary structure prediction

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ProAttUnet: Advancing protein secondary structure prediction with deep learning via U-Net dual-pathway feature fusion and ESM2 pretrained protein language model

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2025年 118卷

作者： Cheng, Long Lu, Weizhong Xia, Yiyi Lu, Yiming Shen, Jiyun Hui, Zhiqiang Xu, Yixin Wu, Hongjie Chen, Jing Fu, Qiming Lu, You Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China Tianping Coll Suzhou Univ Sci & Technol Suzhou 215009 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

protein secondary structure prediction remains a pivotal concern within the domain of bioinformatics. In this innovative research, we introduce a novel methodology to further enhance a protein prediction model grounded in single sequences. Our key contribution lies in integrating the state-of-the-art (SOTA) model ESM2, which hails from the field of universal protein language models. By leveraging ESM2, we are able to acquire residual embeddings and contact maps for the protein sequences under study. Regarding the model architecture, we employ a unique dual-way U-Net framework for effective feature fusion. This framework is complemented by the integration of a cross-attention mechanism, enabling the model to capture more comprehensive context information. Furthermore, In accordance with the distinctive characteristics of protein sequences, we incorporate a so-called GCU_SE module into both the encoder and the decoder components of the model. These innovative enhancements enable the ProAttUnet model to outperform the benchmark model SPOT-1D-Single by 1.6%, 3.5%, 1.0%, 4.6%, and 7.2% for ss3, and by 5.5%, 7.8%, 4.1%, 8.1%, and 10.1% for ss8 across five test sets (SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ and TEST2018, respectively). This significant improvement vividly demonstrates the effectiveness and novelty of our proposed model.

关键词： protein secondary structure prediction protein language model Cross attention mechanism U-Net architecture Dynamic sliding window

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A Hybrid Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Unit Architecture for protein secondary structure prediction

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IEEE ACCESS 2024年 12卷 115346-115355页

作者： Bongirwar, Vrushali Mokhade, A. S. Shri Ramdeobaba Coll Engn & Management Nagpur 440013 India Visvesvaraya Natl Inst Technol Nagpur 440010 India

protein secondary structure prediction plays a pivotal role in deciphering protein function and structure, with implications for drug discovery, functional annotation, and molecular biology research. Deep learning techniques, particularly recurrent neural networks (RNNs), have shown promise in capturing sequential dependencies and contextual information from protein sequences. In this study, we propose a novel approach for protein secondary structure prediction by integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) architectures. Leveraging the complementary strengths of BiLSTM and BiGRU networks, our integrated model aims to enhance prediction accuracy and robustness by effectively capturing both short and long-range dependencies within protein sequences. We evaluate the performance of the proposed model on benchmark datasets and compare it with state-of-the-art methods in the field. The model is trained on CB6133 filtered dataset and tested on CB513, CASP13 and CASP14 dataset. Our results demonstrate 87.92% and 78.6% Q3 and Q8 accuracy on CB513 dataset.

关键词： proteins Accuracy Predictive models Recurrent neural networks Long short term memory Feature extraction Convolutional neural networks BiLSTM protein secondary structure prediction bidirectional LSTM bidirectional GRU

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protein secondary structure prediction Using Cascaded Feature Learning Model

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APPLIED SOFT COMPUTING 2023年 140卷

作者： Geethu, S. Vimina, E. R. Amrita Vishwa Vidyapeetham Dept Comp Sci & IT Sch Comp Kochi Campus Kochi India

The protein secondary structure prediction (PSSP) is pivotal for predicting tertiary structure, which is proliferating in demand for drug design and development. Further, it can be used to learn different protein functions. Although there are many computational methods for protein structure prediction, none of them have succeeded 100% in solving the protein structure prediction problem. This paper proposes a deep learning model, namely Cascaded Feature Learning Model (CFLM) that uses a multistage transfer learning approach based on the Residual Dense Network (RDN) for predicting protein secondary structure. The model is trained with different protein datasets at various levels of transfer learning and accepts selected protein features such as solvent accessibility, physicochemical properties, PSSM (position-specific scoring matrix), and PSFM (position-specific frequency matrix) as input. The Q3 and Q8 accuracy obtained on CASP 13 and 14 benchmark datasets with the proposed approach are 91.23% and 91.45%, and 76.83% and 78.04%, respectively. The performance of CFLM is also compared with some of the recent PSSP approaches and it is observed that the proposed approach improves the prediction accuracy by 2.8% in terms of Q3 and 1.81% in terms of Q8 in the CASP 14 dataset.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Transfer learning Residual Dense Network (RDN) Cascaded Feature learning

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MFTrans: A multi-feature transformer network for protein secondary structure prediction

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2024年第Part1期267卷 131311页

作者： Chen, Yifu Chen, Guanxing Chen, Calvin Yu-Chian Sun Yat Sen Univ Artificial Intelligence Med Res Ctr Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Peking Univ Shenzhen Grad Sch AI Sci Preferred Program AI4S Shenzhen 518055 Guangdong Peoples R China Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen 518055 Guangdong Peoples R China China Med Univ Hosp Dept Med Res Taichung 40447 Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung 41354 Taiwan

In the rapidly evolving field of computational biology, accurate prediction of protein secondary structures is crucial for understanding protein functions, facilitating drug discovery, and advancing disease diagnostics. In this paper, we propose MFTrans, a deep learning -based multi -feature fusion network aimed at enhancing the precision and efficiency of protein secondary structure prediction (PSSP). This model employs a Multiple Sequence Alignment (MSA) Transformer in combination with a multi -view deep learning architecture to effectively capture both global and local features of protein sequences. MFTrans integrates diverse features generated by protein sequences, including MSA, sequence information, evolutionary information, and hidden state information, using a multi -feature fusion strategy. The MSA Transformer is utilized to interleave row and column attention across the input MSA, while a Transformer encoder and decoder are introduced to enhance the extracted high-level features. A hybrid network architecture, combining a convolutional neural network with a bidirectional Gated Recurrent Unit (BiGRU) network, is used to further extract high-level features after feature fusion. In independent tests, our experimental results show that MFTrans has superior generalization ability, outperforming other state-of-the-art PSSP models by 3 % on average on public benchmarks including CASP12, CASP13, CASP14, TEST2016, TEST2018, and CB513. Case studies further highlight its advanced performance in predicting mutation sites. MFTrans contributes significantly to the protein science field, opening new avenues for drug discovery, disease diagnosis, and protein.

关键词： protein secondary structure prediction Multiple sequence alignment Feature fusion Deep learning

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protein secondary structure prediction Based on Fusion of Machine Learning Classifiers 21

Protein Secondary Structure Prediction Based on Fusion of Ma...

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36th Annual ACM Symposium on Applied Computing (SAC)

作者： de Oliveira, Gabriel Bianchin Pedrini, Helio Dias, Zanoni Univ Estadual Campinas Inst Comp Campinas SP Brazil

ISBN: (纸本)9781450381048

protein secondary structure prediction plays an important role in protein folding and function classification. Although the works available in the literature present good results, protein secondary structure prediction is still an open problem. In this work, we present and discuss a fusion strategy using four different classifiers. The fusion is composed of bidirectional recurrent networks, random forests, Inception-v4 blocks and Inception recurrent networks. In order to evaluate our model, we used CB6133 dataset as training and testing. The fusion achieved 76.4% of Q8 accuracy using the amino acid sequence and similarity information on CB6133, surpassing state-of-the-art approaches.

关键词： protein secondary structure prediction Machine Learning Neural Networks Fusion Classifiers

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protein secondary structure prediction using a lightweight convolutional network and label distribution aware margin loss

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KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107771-107771页

作者： Yang, Wei Hu, Zhentao Zhou, Lin Jin, Yong Henan Univ Sch Comp & Informat Engn Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Sch Artificial Intelligence Zhengzhou 450046 Peoples R China

protein secondary structure prediction (PSSP) is an important task in computational molecular biology. Recently, deep neural networks have demonstrated great potential in improving the performance of eight-class PSSP. However, the existing deep predictors usually have higher model complexity and ignore the class imbalance of eight-class secondary structure data in training. In addition, the current methods cannot guarantee that the features corresponding to the padded residue positions are always zero during the forward propagation of the network, which will cause the prediction results of the same protein chain to be different under varied zero-padding numbers. To this end, we propose a novel lightweight convolutional network ShuffleNet_SS, which adopts modified 1-dimensional batch normalization to eliminate the impact of padded residue positions on nonpadded residue positions and uses the label distribution aware margin loss to enhance the network's ability to learn rare classes. In particular, in order to enable ShuffleNet_SS to fully achieve cross-group information exchange, we further improve the standard channel shuffle operation. Experimental results on the benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14 and CB513 show that the proposed method achieves state-of-the-art performance with much lower parameters compared to the five existing deep predictors. (C) 2021 Elsevier B.V. All rights reserved.

关键词： protein secondary structure prediction Imbalanced classification Deep convolutional network Lightweight network Channel shuffle

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Deep learning for protein secondary structure prediction: Pre and post-AlphaFold

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 6271-6286页

作者： Ismi, Dewi Pramudi Pulungan, Reza Afiahayatia Univ Gadjah Mada Fac Math & Nat Sci Dept Comp Sci & Elect Yogyakarta Indonesia Univ Ahmad Dahlan Fac Ind Technol Dept Infomat Yogyakarta Indonesia

This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical *** are still rooms for improvement to be made in the field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： protein protein secondary structure prediction Deep neural networks Machine learning prediction accuracy

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