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Chen Y, Chen G, Chen CYC. MFTrans: A multi-feature transformer network for protein secondary structure prediction. Int J Biol Macromol 2024; 267:131311. [PMID: 38599417 DOI: 10.1016/j.ijbiomac.2024.131311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 03/21/2024] [Accepted: 03/30/2024] [Indexed: 04/12/2024]
Abstract
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.
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Affiliation(s)
- Yifu Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China; AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China; School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China; Department of Medical Research, China Medical University Hospital, Taichung 40447, Taiwan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan.
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Ismi DP, Pulungan R, Afiahayati. Deep learning for protein secondary structure prediction: Pre and post-AlphaFold. Comput Struct Biotechnol J 2022; 20:6271-6286. [PMID: 36420164 PMCID: PMC9678802 DOI: 10.1016/j.csbj.2022.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/05/2022] [Accepted: 11/05/2022] [Indexed: 11/13/2022] Open
Abstract
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 limit. There are still rooms for improvement to be made in the field.
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Affiliation(s)
- Dewi Pramudi Ismi
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
- Department of Infomatics, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
| | - Reza Pulungan
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Afiahayati
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Bongirwar V, Mokhade AS. Different methods, techniques and their limitations in protein structure prediction: A review. Prog Biophys Mol Biol 2022; 173:72-82. [PMID: 35588858 DOI: 10.1016/j.pbiomolbio.2022.05.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 04/16/2022] [Accepted: 05/11/2022] [Indexed: 11/17/2022]
Abstract
Because of the increase in different types of diseases in human habitats, demands for designing various types of drugs are also increasing. Protein and its structure play a very important role in drug design. Therefore researchers from different areas like mathematics, medicines, and computer science are teaming up for getting better solutions in the said field. In this paper, we have discussed different methods of secondary and tertiary protein structure prediction (PSP), along with the limitations of different approaches. Different types of datasets used in PSP are also discussed here. This paper also tells about different performance measures to evaluate the prediction accuracy of PSP methods. Different software's/servers are available for download, which are used to find the protein structures for the input protein sequence. These softwares will also help to compare the performance of any new algorithm with other available methods. Details of those softwares are also mentioned in this paper.
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Affiliation(s)
| | - A S Mokhade
- Visvesvaraya National Institute of Technology, Nagpur, India
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Stapor K, Kotowski K, Smolarczyk T, Roterman I. Lightweight ProteinUnet2 network for protein secondary structure prediction: a step towards proper evaluation. BMC Bioinformatics 2022; 23:100. [PMID: 35317722 PMCID: PMC8939211 DOI: 10.1186/s12859-022-04623-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 02/28/2022] [Indexed: 11/10/2022] Open
Abstract
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 features (from PSSM in particular), it would be worth conducting the predictive ability tests on embeddings as input features in the future. We strongly believe that our proposed here statistical methodology for the evaluation of SS prediction results will be adopted and used (and even expanded) by the research community. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04623-z.
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Affiliation(s)
- Katarzyna Stapor
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland.
| | - Krzysztof Kotowski
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Tomasz Smolarczyk
- Department of Applied Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
| | - Irena Roterman
- Department of Bioinformatics and Telemedicine, Jagiellonian University Medical College, Medyczna 7, 30-688, Kraków, Poland
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Miao Z, Wang Q, Xiao X, Kamal GM, Song L, Zhang X, Li C, Zhou X, Jiang B, Liu M. CSI-LSTM: a web server to predict protein secondary structure using bidirectional long short term memory and NMR chemical shifts. J Biomol NMR 2021; 75:393-400. [PMID: 34510297 DOI: 10.1007/s10858-021-00383-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/06/2021] [Indexed: 06/13/2023]
Abstract
Protein secondary structure provides rich structural information, hence the description and understanding of protein structure relies heavily on it. Identification or prediction of secondary structures therefore plays an important role in protein research. In protein NMR studies, it is more convenient to predict secondary structures from chemical shifts as compared to the traditional determination methods based on inter-nuclear distances provided by NOESY experiment. In recent years, there was a significant improvement observed in deep neural networks, which had been applied in many research fields. Here we proposed a deep neural network based on bidirectional long short term memory (biLSTM) to predict protein 3-state secondary structure using NMR chemical shifts of backbone nuclei. While comparing with the existing methods the proposed method showed better prediction accuracy. Based on the proposed method, a web server has been built to provide protein secondary structure prediction service.
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Affiliation(s)
- Zhiwei Miao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
| | - Qianqian Wang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
| | - Xiongjie Xiao
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
| | - Ghulam Mustafa Kamal
- Department of Chemistry, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan, Punjab, 64200, Pakistan
| | - Linhong Song
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Xu Zhang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Conggang Li
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Xin Zhou
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, 10049, China
| | - Bin Jiang
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China.
- University of Chinese Academy of Sciences, Beijing, 10049, China.
| | - Maili Liu
- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, 430071, Wuhan, China.
- University of Chinese Academy of Sciences, Beijing, 10049, China.
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Liu T, Wang Z. SOV_refine: A further refined definition of segment overlap score and its significance for protein structure similarity. Source Code Biol Med 2018; 13:1. [PMID: 29713370 PMCID: PMC5909207 DOI: 10.1186/s13029-018-0068-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 04/02/2018] [Indexed: 11/22/2022]
Abstract
Background The segment overlap score (SOV) has been used to evaluate the predicted protein secondary structures, a sequence composed of helix (H), strand (E), and coil (C), by comparing it with the native or reference secondary structures, another sequence of H, E, and C. SOV’s advantage is that it can consider the size of continuous overlapping segments and assign extra allowance to longer continuous overlapping segments instead of only judging from the percentage of overlapping individual positions as Q3 score does. However, we have found a drawback from its previous definition, that is, it cannot ensure increasing allowance assignment when more residues in a segment are further predicted accurately. Results A new way of assigning allowance has been designed, which keeps all the advantages of the previous SOV score definitions and ensures that the amount of allowance assigned is incremental when more elements in a segment are predicted accurately. Furthermore, our improved SOV has achieved a higher correlation with the quality of protein models measured by GDT-TS score and TM-score, indicating its better abilities to evaluate tertiary structure quality at the secondary structure level. We analyzed the statistical significance of SOV scores and found the threshold values for distinguishing two protein structures (SOV_refine > 0.19) and indicating whether two proteins are under the same CATH fold (SOV_refine > 0.94 and > 0.90 for three- and eight-state secondary structures respectively). We provided another two example applications, which are when used as a machine learning feature for protein model quality assessment and comparing different definitions of topologically associating domains. We proved that our newly defined SOV score resulted in better performance. Conclusions The SOV score can be widely used in bioinformatics research and other fields that need to compare two sequences of letters in which continuous segments have important meanings. We also generalized the previous SOV definitions so that it can work for sequences composed of more than three states (e.g., it can work for the eight-state definition of protein secondary structures). A standalone software package has been implemented in Perl with source code released. The software can be downloaded from http://dna.cs.miami.edu/SOV/.
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Affiliation(s)
- Tong Liu
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124 USA
| | - Zheng Wang
- Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124 USA
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