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

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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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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A Comparative Study on Filtering protein secondary structure prediction

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第3期9卷 731-739页

作者： Kountouris, Petros Agathocleous, Michalis Promponas, Vasilis J. Christodoulou, Georgia Hadjicostas, Simos Vassiliades, Vassilis Christodoulou, Chris Univ Cyprus Dept Comp Sci CY-1678 Nicosia Cyprus Univ Cyprus Dept Biol Sci CY-1678 Nicosia Cyprus

Filtering of protein secondary structure prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement.

关键词： protein secondary structure prediction filtering machine learning structural bioinformatics bidirectional recurrent neural networks

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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 1-16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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Improving protein secondary structure prediction using a multi-modal BP method

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COMPUTERS IN BIOLOGY AND MEDICINE 2011年第10期41卷 946-959页

作者： Qu, Wu Sui, Haifeng Yang, Bingru Qian, Wenbin Univ Sci & Technol Beijing Sch Informat Engn Beijing 100083 Peoples R China

Methods for predicting protein secondary structures provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a multi-modal back propagation neural network (MMBP) method for predicting protein secondary structures. Using a Knowledge Discovery Theory based on Inner Cognitive Mechanism (KDTICM) method, we have constructed a compound pyramid model (CPM), which is composed of three layers of intelligent interface that integrate multi-modal back propagation neural network (MMBP), mixed-modal SVM (MMS), modified Knowledge Discovery in Databases (KDD*) process and so on. The CPM method is both an integrated web server and a standalone application that exploits recent advancements in knowledge discovery and machine learning to perform very accurate protein secondary structure predictions. Using a non-redundant test dataset of 256 proteins from RCASP256, the CPM method achieves an average Q(3) score of 86.13% (SOV99=84.66%). Extensive testing indicates that this is significantly better than any other method currently available. Assessments using RS126 and CB513 datasets indicate that the CPM method can achieve average Q(3) score approaching 83.99% (SOV99=80.25%) and 85.58% (SOV99=81.15%). By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called CPM, which performs these secondary structure predictions, is accessible at http://***/protein_Web/. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： protein secondary structure prediction Physicochemical properties Multi-modal BP Compound pyramid model

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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

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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protein secondary structure prediction USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2006年第4期6卷 551-567页

作者： Gubbi, Jayavardhana Lai, Daniel T. H. Palaniswami, Marimuthu Parker, Michael Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia St Vincents Inst Med Res Fitzroy Vic 3065 Australia

Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou-Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q(3) accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on http://***/ISSNIP/bioinf/.

关键词： protein secondary structure prediction support vector machines position specific scoring matrix (PSSM) Chou-Fasman parameters Kyte-Dolittle hydrophobicity Grantham polarity reliability index novel encoding scheme

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KAAPRO: An approach of protein secondary structure prediction based on KDD* in the compound pyramid prediction model

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第5期36卷 9000-9006页

作者： Yang, Bingru Hou, Wei Zhou, Zhun Quan, Huabin Univ Sci & Technol Beijing Informat Engn Sch Beijing 100083 Peoples R China

the problem of protein secondary structure prediction is one of the most important problems in Bioinformatics. After the study of this problem for 30 years and more, there have been some breakthroughs. Especially, the introduction of ensemble prediction model and hybrid prediction model makes the accuracy of prediction better, but there is a long distance to induce the tertiary structures from the secondary ones. As one of the extension researches of KDTICM [Bingru, Yang (2004). Knowledge discovery based oil theory of iron, cognition mechanism and application. Beijing: Electronic Industry Press] theory. this paper proposed a method KAAPRO, which is based oil Maradbem algorithm which is induced by KDD* model and combined with CBA, for protein secondary structure prediction. And a gradually enhanced, multi-layer systematic prediction model, compound pyramid model, is proposed. The kernel of this model is KAAPRO, Domain knowledge is used through the whole model, and the physical-chemical attributes are chosen by causal cellular automata. In the experiment, the test proteins used in reference Muggleton et al. (Muggleton, S. H., King, R., Sternberg, M. (1992). protein secondary structure prediction using logic-based machine learning. protein Engineering, 5(7), 647-657) are predicted. The structures of amino acids, whose structural trails arc obscure, are predicted well by KAAPRO. Hence, the result of this model is satisfying too. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.

关键词： Association analysis protein secondary structure prediction KDD Compound pyramid model

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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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Association classification algorithm based on structure sequence in protein secondary structure prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2010年第9期37卷 6381-6389页

作者： Zhou, Zhun Yang, Bingru Hou, Wei Tsinghua Univ Dept Environm Sci & Engn Beijing 100084 Peoples R China Univ Sci & Technol Beijing Dept Informat Engn Beijing 100083 Peoples R China

Objective: To propose a novel associate classification algorithm SAC (structural association classification) and develop a compound pyramid model for accurate and precise protein secondary structure prediction. Method: Based on the slide window theory, the protein sequence was treated as a window with length of 13, in which the target amino acid resided in the center, while the remaining area was targeted as secondary amino acid structures. To the head and tail of the sequence, the mirror method was employed to fill the space with an opposite- position structure in relation to the central position. In the mining process, the KDD center dot model not only focuses on the high support and confidence rules, but also pay attention to high confidence and low support rules, which is called 'knowledge in shortage'. Towards the end of the mining process, sets H, E and C, consisted of rule sets whose consequents are alpha-helix, beta-sheet and C-coil, were created respectively to meet the basic requirements for the protein secondary structure prediction. The knowledge base of protein secondary structure was then established with these three newly-acquired rule sets. Through the CMAR (Classification based on Multiple Association rules) algorithm, a novel multi-classifier was developed to determine the best likelihood of a given window to the secondary structure through the adjacent information on amino acid sequential window and screening of three different rule sets. Result: The protein knowledge base consisted of 8049 rules corresponding to sets H, E and C with 2642, 1895 and 3512 rules, respectively, was obtained. Experiment shows, theoretically, accuracy ratio exceeded 85% when confidence threshold value was 70% and 90%. Through the classification process using the multi-classifier SAC developed in four experiments, the significantly high accuracy and recall ratios up to 83.06% (According to Q(3) criterion, followed by abbreviation) in RS126 (Chen & Chaudhari, 2007;Guo et

关键词： Association classification protein secondary structure prediction KDD center dot (Knowledge Discovery(center dot)) Compound pyramid model

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