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

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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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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protein secondary structure prediction: A Review of Progress and Directions

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CURRENT BIOINFORMATICS 2020年第2期15卷 90-107页

作者： Smolarczyk, Tomasz Roterman-Konieczna, Irena Stapor, Katarzyna Silesian Tech Univ Inst Informat Akad 16 Gliwice Poland Jagiellonian Univ Dept Bioinformat & Telemed Med Coll Krakow Poland

Background: Over the last few decades, a search for the theory of protein folding has grown into a full-fledged research field at the intersection of biology, chemistry and informatics. Despite enormous effort, there are still open questions and challenges, like understanding the rules by which amino acid sequence determines protein secondary structure. Objective: In this review, we depict the progress of the prediction methods over the years and identify sources of improvement. Methods: The protein secondary structure prediction problem is described followed by the discussion on theoretical limitations, description of the commonly used data sets, features and a review of three generations of methods with the focus on the most recent advances. Additionally, methods with available online servers are assessed on the independent data set. Results: The state-of-the-art methods are currently reaching almost 88% for 3-class prediction and 76.5% for an 8-class prediction. Conclusion: This review summarizes recent advances and outlines further research directions.

关键词： protein secondary structure prediction multiple sequence alignment PSSM HHblits deep neural networks machine learning protein early-stage structure

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protein secondary structure prediction using DWKF based on SVR-NSGAII

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NEUROCOMPUTING 2012年 94卷 87-101页

作者： Zangooei, Mohammad Hossein Jalili, Saeed Tarbiat Modares Univ Elect & Comp Engn Fac Dept Comp Engn SCS LabSch Elect & Comp Engn Tehran Iran

prediction of protein secondary structure is an important step towards elucidating its three dimensional structure and its function. This is a challenging problem in bioinformatics. By introduction of machine learning for protein structure prediction, a solution has brought to this challenge to some extent. In the literature of Machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variable. There are endeavors to use regression methods to solve the classification problem and vice versa. To regard a classification problem as a regression one, we proposed a method which is based on Support Vector Regression (SVR) classification model as one of the powerful methods in the field of machine intelligence. We applied non-dominated Sorting Genetic Algorithm II (NSGAII) to find mapping points (MPs) for rounding a real-value to an integer one. Also NSGAII is used for finding out and tuning SVR kernel parameters optimally to enhance the performance of our model and achieve better results. At the other hand, using a suitable SVR kernel function for a particular problem can improve the prediction results remarkably but there is not a kernel which can predict all protein secondary structure classes with acceptable accuracy. Therefore we use a Dynamic Weighted Kernel Fusion (DWKF) method for fusing of three SVR kernels to achieve a supreme performance. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this research are to regulate SVR parameters and fuse different SVR kernel outputs in order to determine protein secondary structure classes accurately. The obtained classification accuracies of our method are 85.79% and 84.94% on RS126 and CB513 datasets respectively and they are promising with regard to other classification methods in the literature. Moreover, for gauging our method behavior in

关键词： protein secondary structure prediction Machine learning approach Support vector regression Multi-Objective Genetic Algorithm

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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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protein secondary structure prediction using neural networks and deep learning: A review

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2019年 81卷 1-8页

作者： Wardah, Wafaa Khan, M. G. M. Sharma, Alok Rashid, Mahmood A. Univ South Pacific Sch Comp Informat & Math Sci Suva Fiji Univ South Pacific Sch Engn & Phys Suva Fiji RIKEN Ctr Integrat Med Sci Yokohama Kanagawa Japan Griffith Univ Inst Integrated & Intelligent Syst Nathan Qld Australia Victoria Univ Melbourne Inst Sustainable Ind & Liveable Cities Melbourne Vic Australia

Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand.

关键词： protein secondary structure prediction Machine learning Neural network Deep learning Feature selection

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protein secondary structure prediction: A survey of the state of the art

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JOURNAL OF MOLECULAR GRAPHICS & MODELLING 2017年 76卷 379-402页

作者： Jiang, Qian Jin, Xin Lee, Shin-Jye Yao, Shaowen Yunnan Univ Sch Informat Kunming Yunnan Peoples R China Yunnan Univ Sch Software Kunming Yunnan Peoples R China Univ Cambridge Queens Coll Cambridge CB3 9ET England

protein secondary structure prediction (PSSP) is a fundamental task in protein science and computational biology, and it can be used to understand protein 3-dimensional (3-D) structures, further, to learn their biological functions. In the past decade, a large number of methods have been proposed for PSSP. In order to learn the latest progress of PSSP, this paper provides a survey on the development of this field. It first introduces the background and related knowledge of PSSP, including basic concepts, data sets, input data features and prediction accuracy assessment. Then, it reviews the recent algorithmic developments of PSSP, which mainly focus on the latest decade. Finally, it summarizes the corresponding tendencies and challenges in this field. This survey concludes that although various PSSP methods have been proposed, there still exist several further improvements or potential research directions. We hope that the presented guidelines will help nonspecialists and specialists to learn the critical progress in PSSP in recent years. (C) 2017 Elsevier Inc. All rights reserved.

关键词： protein secondary structure prediction Machine learning Neural networks Classification algorithm Feature extraction

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protein secondary structure prediction using local alignments

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JOURNAL OF MOLECULAR BIOLOGY 1997年第1期268卷 31-36页

作者： Salamov, AA Solovyev, VV BAYLOR COLL MED DEPT CELL BIOL HOUSTON TX 77030 USA

The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively. We introduce a variant of the nearest-neighbor approach that can achieve similar accuracy using a single sequence as the query input. We compute the 50 best non-intersecting local alignments of the query sequence with each sequence from a set of proteins with known 3D structures. Each position of the query sequence is aligned with the database amino acids in alpha-helical, beta-strand or coil states. The prediction type of secondary structure is selected as the type of aligned position with the maximal total score. On the dataset of 124 non-membrane non-homologous proteins, used earlier as a benchmark for secondary structure predictions, our method reaches an overall three-state accuracy of 71.2%. The performance accuracy is verified by an additional test on 461 non-homologous proteins giving an accuracy of 71.0%. The main strength of the method is the high level of prediction accuracy for proteins without any known homolog. Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. prediction of secondary structure by the SSPAL method is available via Baylor College of Medicine World Wide Web server. (C) 1997 Academic Press Limited.

关键词： protein secondary structure prediction local alignments 3D-1D environment scoring substitution matrix

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protein secondary structure prediction with Bidirectional Recurrent Neural Nets: Can Weight Updating for Each Residue Enhance Performance?

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6th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Agathocleous, Michalis Christodoulou, Georgia Promponas, Vasilis Christodoulou, Chris Vassiliades, Vassilis Antoniou, Antonis Univ Cyprus Dept Comp Sci POB 20537 CY-1678 Nicosia Cyprus Univ Cyprus Dept Sci Biol POB 20537 CY-1678 Nicosia Cyprus

ISBN: (纸本)9783642162381

Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been proposed, we are far from being accurate. The Bidirectional Recurrent Neural Network (BRNN) architecture of Baldi et al. [1] is currently considered as one of the optimal computational neural network type architectures for addressing the problem. In this paper, we implement the same BRNN architecture, but we use a modified training procedure. More specifically, our aim is to identify the effect of the contribution of local versus global information, by varying the length of the segment on which the Recurrent Neural Networks operate for each residue position considered. For training the network, the backpropagation learning algorithm with an online training procedure is used, where the weight updates occur for every amino acid, as opposed to Baldi et al. [1], where the weight updates are applied after the presentation of the entire protein. Our results with a single BRNN are better than Baldi et al. [1] by three percentage points (Q3) and comparable to results of [1] when they use an ensemble of 6 BRNNs. In addition, our results improve even further when sequence-to-structure output is filtered in a post-processing step, with a novel Hidden Markov Model-based approach.

关键词： protein secondary structure prediction Bidirectional Recurrent Neural Networks Bioinformatics and Computational Biology

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protein secondary structure prediction using Large Margin Methods

Protein Secondary Structure Prediction using Large Margin Me...

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8th IEEE/ACIS International Conference on Computer and Information Science

作者： Tang, Buzhou Wang, Xuan Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China

ISBN: (纸本)9780769536415

protein secondary structure prediction is an important step to understanding protein tertiary structure. Recent studies indicate that the correlation between neighboring secondary structures are beneficial to improve prediction performance. In this paper, we propose a new large margin approach for protein secondary structure prediction, which consider the problem as a sequence labeling problem like probabilistic graphical models. It doesn't only make full use of the correlation between neighboring secondary structures like graphical chain models, but also shares the key advantages of other SVM-based methods, i.e. learming non-linear discriminant via kernel functions. The experimental results on datasets: CB513 and RS126 show that our algorithm outperforms other state-of-the-art methods.

关键词： protein secondary structure prediction large margin approach sequence labeling problem probabilistic graphical models

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