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protein secondary structure prediction: A Review of Progress and Directions

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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Convolution-Bidirectional Temporal Convolutional Network for protein secondary structure prediction

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IEEE ACCESS 2022年 10卷 117469-117476页

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

As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem. Howerover, ordinary temporal convolutional networks can not deal well protein secondary structure prediction because of their one-way analysis. Therefore, we propose an integrated deep learning model called Convolutional-Bidirectional Temporal Convolutional Network. for 3-state and 8-state protein secondary structure predictions based on a convolutional neural network and bidirectional temporal convolutional networks. Because the model combines the advantages of the convolutional neural network and bidirectional temporal convolution network, it can not only capture the local correlation in the amino acid sequence but also analyse the long-distance interaction in the amino acid sequence. Therefore, this model can effectively improve the accuracy of protein secondary structure predictions. The experimental results show that the combination of convolutional neural network and bidirectional temporal convolutional network is effective for predicting protein secondary structure.

关键词： protein secondary structure prediction temporal convolutional network convolutional neural network bidirectional prediction deep learning

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Solving the protein secondary structure prediction Problem With the Hessian Free Optimization Algorithm

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IEEE ACCESS 2022年 10卷 27759-27770页

作者： Charalampous, Konstantinos Agathocleous, Michalis Christodoulou, Chris Promponas, Vasilis Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Nicosia Dept Comp Sci CY-1700 Nicosia Cyprus Univ Cyprus Dept Biol Sci CY-1678 Nicosia Cyprus

Trying to extract features from complex sequential data for classification and prediction problems is an extremely difficult task. This task is even more challenging when both the upstream and downstream information of a time-series is important to process the sequence at a specific time-step. One typical problem which falls in this category is protein secondary structure prediction (PSSP). Recurrent Neural Networks (RNNs) have been successful in handling sequential data. These methods are demanding in terms of time and space efficiency. On the other hand, simple Feed-Forward Neural Networks (FFNNs) can be trained really fast with the Backpropagation algorithm, but in practice they give poor results in this category of problems. The Hessian Free Optimization (HFO) algorithm is one of the latest developments in the field of Artificial Neural Network (ANN) training algorithms which can converge faster and more accurately. In this paper, we present the implementation of simple FFNNs trained with the powerful HFO second-order learning algorithm for the PSSP problem. In our approach, a single FFNN trained with the HFO learning algorithm can achieve an approximately 79.6% per residue (Q(3)) accuracy on the PISCES dataset. Despite the simplicity of our method, the results are comparable to some of the state of the art methods which have been designed for this problem. A majority voting ensemble method and filtering with Support Vector Machines have also been applied, which increase our results to 80.4% per residue (Q(3)) accuracy. Finally, our method has been tested on the CASP13 independent dataset and achieved 78.14% per residue (Q(3)) accuracy. Moreover, the HFO does not require tuning of any parameters which makes training much faster than other state of the art methods, a very important feature with big datasets and facilitates fast training of FFNN ensembles.

关键词： Hessian free optimization neural networks protein secondary structure prediction second order learning algorithms

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Self-attention and asymmetric multi-layer perceptron-gated recurrent unit blocks for protein secondary structure prediction

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APPLIED SOFT COMPUTING 2024年 159卷

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

protein secondary structure prediction (PSSP) is one of the most prominent and widely -conducted tasks in Bioinformatics. Deep neural networks have become the primary methods for building PSSP models in the last decade due to their potential to enhance PSSP performances. However, there is room for improvement in PSSP as previous studies have yet to reach the theoretical limit of PSSP model performance. In this work, we propose a PSSP model called SADGRU-SS, which is built with a novel and unique deep learning architecture that utilizes self -attention, asymmetric multi -layer perceptron (MLP)-gated recurrent unit (GRU) blocks, and a dense block for solving the PSSP problem. Our experiment results show that using self -attention in the SADGRUSS architecture has successfully increased SADGRU-SS performance. Moreover, installing self -attention in the frontmost position of the networks produces better performance than locating it in other positions. Using the asymmetric configuration in the MLP-GRU blocks results in more excellent performance than the symmetric ones. Our model is trained using the standard CB6133-filtered dataset. We evaluate the performance of our model using the standard CB513 test dataset. Our experiment shows that the performance of our model on 8 -state PSSP outstands other PSSP models. The model achieves 70.74% and 82.78% prediction accuracy in the 8 -state and 3 -state PSSP, respectively.

关键词： protein secondary structure prediction Deep learning Self-attention Asymmetric MLP-GRU block prediction accuracy

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IGPRED: Combination of convolutional neural and graph convolutional networks for protein secondary structure prediction

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proteinS-structure FUNCTION AND BIOINFORMATICS 2021年第10期89卷 1277-1288页

作者： Gormez, Yasin Sabzekar, Mostafa Aydin, Zafer Sivas Cumhuriyet Univ Fac Econ & Adm Sci Management Informat Syst Sivas Turkey Birjand Univ Technol Dept Comp Engn Birjand Iran Abdullah Gul Univ Comp Engn Dept Engn Fac Kayseri Turkey

There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.

关键词： Bayesian optimization convolutional neural network deep learning graph convolutional network protein secondary structure prediction

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Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2023年 11卷 1051268页

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

protein secondary structure prediction (PSSP) is a challenging task in computational biology. However, existing models with deep architectures are not sufficient and comprehensive for deep long-range feature extraction of long sequences. This paper proposes a novel deep learning model to improve protein secondary structure prediction. In the model, our proposed bidirectional temporal convolutional network (BTCN) can extract the bidirectional deep local dependencies in protein sequences segmented by the sliding window technique, the bidirectional long short-term memory (BLSTM) network can extract the global interactions between residues, and our proposed multi-scale bidirectional temporal convolutional network (MSBTCN) can further capture the bidirectional multi-scale long-range features of residues while preserving the hidden layer information more comprehensively. In particular, we also propose that fusing the features of 3-state and 8-state protein secondary structure prediction can further improve the prediction accuracy. Moreover, we also propose and compare multiple novel deep models by combining bidirectional long short-term memory with temporal convolutional network (TCN), reverse temporal convolutional network (RTCN), multi-scale temporal convolutional network (multi-scale bidirectional temporal convolutional network), bidirectional temporal convolutional network and multi-scale bidirectional temporal convolutional network, respectively. Furthermore, we demonstrate that the reverse prediction of secondary structure outperforms the forward prediction, suggesting that amino acids at later positions have a greater impact on secondary structure recognition. Experimental results on benchmark datasets including CASP10, CASP11, CASP12, CASP13, CASP14, and CB513 show that our methods achieve better prediction performance compared to five state-of-the-art methods.

关键词： protein secondary structure prediction bidirectional temporal convolutional network bidirectional long short-term memory multi-scale BTCN reverse prediction fusing the features

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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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Lightweight proteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation

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BMC BIOINFORMATICS 2022年第1期23卷 100-100页

作者： Stapor, Katarzyna Kotowski, Krzysztof Smolarczyk, Tomasz Roterman, Irena Silesian Tech Univ Dept Appl Informat Akad 16 PL-44100 Gliwice Poland Jagiellonian Univ Med Coll Dept Bioinformat & Telemed Med 7 PL-30688 Krakow Poland

Background The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings-the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman-Pearson approach is not appropriate. Results We present a lightweight deep network proteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of proteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher-Pitman permutation tests accompanied by practical significance measured by Cohen's effect size. Conclusions Our results suggest that proteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based fe

关键词： protein secondary structure prediction U-Net Deep learning PSSM HHblits

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MMEC: Multi-Modal Ensemble Classifier for protein secondary structure prediction 19th

MMEC: Multi-Modal Ensemble Classifier for Protein Secondary ...

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19th International Conference on Computer Analysis of Images and Patterns (CAIP)

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

ISBN: (纸本)9783030891275;9783030891282

The protein secondary structure prediction is an important task with many applications, such as local folding analysis, tertiary structure prediction, and function classification. Driven by the recent success of multi-modal classifiers, new studies have been conducted using this type of method in other domains, for instance, biology and health care. In this work, we investigate the ensemble of three different classifiers for protein secondary structure prediction. Each classifier of our method deals with a transformation of the original data into a specific domain, such as image classification, natural language processing, and time series tasks. As a result, each classifier achieved competitive results compared to the literature, and the ensemble of the three different classifiers obtained 77.9% and 73.3% of Q8 accuracy on the CB6133 and CB513 datasets, surpassing state-of-the-art approaches in both scenarios.

关键词： Deep learning protein secondary structure prediction Multi-modal classification

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protein secondary structure prediction Based on Generative Confrontation and Convolutional Neural Network

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IEEE ACCESS 2020年 8卷 199171-199178页

作者： Zhao, Yawu Zhang, Hualan Liu, Yihui Qilu Univ Technol Sch Comp Sci & Technol Shandong Acad Sci Jinan 250353 Peoples R China

In the field of bioinformatics, the prediction of protein secondary structure is a challenging task, and it is extremely important for determining the structure and function of proteins. In this paper, the generation of adversarial network and convolutional neural network model are combined for protein secondary structure prediction. Firstly, generate a confrontation network to extract protein features, and then combine the extracted features with the original PSSM data as the input of the convolutional neural network to obtain prediction results. Testsets CASP9, CASP10, CASP11, CASP12, CB513 and PDB25 obtained 87.06%, 87.24%, 87.31%, 87.39%, 88.13% and 88.93%, which are 3.88%, 4.6%, 7.97%,5.85%, 5.78%, 4.25% higher than one using the convolutional neural network alone. The experimental results show that the feature extraction ability of generating adversarial networks is very significant.

关键词： Bioinformatics generative adversarial network convolutional neural network protein secondary structure prediction

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