1
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Zhang T, Jia J, Chen C, Zhang Y, Yu B. BiGRUD-SA: Protein S-sulfenylation sites prediction based on BiGRU and self-attention. Comput Biol Med 2023; 163:107145. [PMID: 37336062 DOI: 10.1016/j.compbiomed.2023.107145] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/18/2023] [Accepted: 06/06/2023] [Indexed: 06/21/2023]
Abstract
S-sulfenylation is a vital post-translational modification (PTM) of proteins, which is an intermediate in other redox reactions and has implications for signal transduction and protein function regulation. However, there are many restrictions on the experimental identification of S-sulfenylation sites. Therefore, predicting S-sulfoylation sites by computational methods is fundamental to studying protein function and related biological mechanisms. In this paper, we propose a method named BiGRUD-SA based on bi-directional gated recurrent unit (BiGRU) and self-attention mechanism to predict protein S-sulfenylation sites. We first use AAC, BLOSUM62, AAindex, EAAC and GAAC to extract features, and do feature fusion to obtain original feature space. Next, we use SMOTE-Tomek method to handle data imbalance. Then, we input the processed data to the BiGRU and use self-attention mechanism to do further feature extraction. Finally, we input the data obtained to the deep neural networks (DNN) to identify S-sulfenylation sites. The accuracies of training set and independent test set are 96.66% and 95.91% respectively, which indicates that our method is conducive to identifying S-sulfenylation sites. Furthermore, we use a data set of S-sulfenylation sites in Arabidopsis thaliana to effectively verify the generalization ability of BiGRUD-SA method, and obtain better prediction results.
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Affiliation(s)
- Tingting Zhang
- College of Computer Science and Technology, Shandong University, Qingdao, 266237, China; College of Information Science and Technology, School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Jihua Jia
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China
| | - Cheng Chen
- College of Computer Science and Technology, Shandong University, Qingdao, 266237, China
| | - Yaqun Zhang
- College of Mathematics and Big Data, Dezhou University, Dezhou, 253023, China.
| | - Bin Yu
- College of Information Science and Technology, School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China; School of Data Science, University of Science and Technology of China, Hefei, 230027, China.
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2
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Dou L, Yang F, Xu L, Zou Q. A comprehensive review of the imbalance classification of protein post-translational modifications. Brief Bioinform 2021; 22:6217722. [PMID: 33834199 DOI: 10.1093/bib/bbab089] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 02/17/2021] [Accepted: 02/24/2021] [Indexed: 12/13/2022] Open
Abstract
Post-translational modifications (PTMs) play significant roles in regulating protein structure, activity and function, and they are closely involved in various pathologies. Therefore, the identification of associated PTMs is the foundation of in-depth research on related biological mechanisms, disease treatments and drug design. Due to the high cost and time consumption of high-throughput sequencing techniques, developing machine learning-based predictors has been considered an effective approach to rapidly recognize potential modified sites. However, the imbalanced distribution of true and false PTM sites, namely, the data imbalance problem, largely effects the reliability and application of prediction tools. In this article, we conduct a systematic survey of the research progress in the imbalanced PTMs classification. First, we describe the modeling process in detail and outline useful data imbalance solutions. Then, we summarize the recently proposed bioinformatics tools based on imbalanced PTM data and simultaneously build a convenient website, ImClassi_PTMs (available at lab.malab.cn/∼dlj/ImbClassi_PTMs/), to facilitate the researchers to view. Moreover, we analyze the challenges of current computational predictors and propose some suggestions to improve the efficiency of imbalance learning. We hope that this work will provide comprehensive knowledge of imbalanced PTM recognition and contribute to advanced predictors in the future.
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Affiliation(s)
- Lijun Dou
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Fenglong Yang
- University of Electronic Science and Technology of China and the Shenzhen Polytechnic, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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3
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Li S, Yu K, Wu G, Zhang Q, Wang P, Zheng J, Liu ZX, Wang J, Gao X, Cheng H. pCysMod: Prediction of Multiple Cysteine Modifications Based on Deep Learning Framework. Front Cell Dev Biol 2021; 9:617366. [PMID: 33732693 PMCID: PMC7959776 DOI: 10.3389/fcell.2021.617366] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/12/2021] [Indexed: 12/18/2022] Open
Abstract
Thiol groups on cysteines can undergo multiple post-translational modifications (PTMs), acting as a molecular switch to maintain redox homeostasis and regulating a series of cell signaling transductions. Identification of sophistical protein cysteine modifications is crucial for dissecting its underlying regulatory mechanism. Instead of a time-consuming and labor-intensive experimental method, various computational methods have attracted intense research interest due to their convenience and low cost. Here, we developed the first comprehensive deep learning based tool pCysMod for multiple protein cysteine modification prediction, including S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation. Experimentally verified cysteine sites curated from literature and sites collected by other databases and predicting tools were integrated as benchmark dataset. Several protein sequence features were extracted and united into a deep learning model, and the hyperparameters were optimized by particle swarm optimization algorithms. Cross-validations indicated our model showed excellent robustness and outperformed existing tools, which was able to achieve an average AUC of 0.793, 0.807, 0.796, 0.793, and 0.876 for S-nitrosylation, S-palmitoylation, S-sulfenylation, S-sulfhydration, and S-sulfinylation, demonstrating pCysMod was stable and suitable for protein cysteine modification prediction. Besides, we constructed a comprehensive protein cysteine modification prediction web server based on this model to benefit the researches finding the potential modification sites of their interested proteins, which could be accessed at http://pcysmod.omicsbio.info. This work will undoubtedly greatly promote the study of protein cysteine modification and contribute to clarifying the biological regulation mechanisms of cysteine modification within and among the cells.
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Affiliation(s)
- Shihua Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Kai Yu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guandi Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qingfeng Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Panqin Wang
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
| | - Jian Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ze-Xian Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jichao Wang
- CAS Key Lab of Biobased Materials, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, China
| | - Xinjiao Gao
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China
| | - Han Cheng
- School of Life Sciences, Zhengzhou University, Zhengzhou, China
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4
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Nallapareddy V, Bogam S, Devarakonda H, Paliwal S, Bandyopadhyay D. DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains. Proteins 2021; 89:745-761. [PMID: 33580578 DOI: 10.1002/prot.26056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 01/31/2021] [Indexed: 12/29/2022]
Abstract
Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scarce, in contrast to specific function prediction algorithms. Here we present a deep neural network-based multiple Cys function prediction, available on web-server (DeepCys) (https://deepcys.herokuapp.com/). DeepCys model was trained and tested on two independent datasets curated from protein crystal structures. This prediction method requires three inputs, namely, PDB identifier (ID), chain ID and residue ID for a given Cys and outputs the probabilities of four cysteine functions, namely, disulphide, metal-binding, thioether and sulphenylation and predicts the most probable Cys function. The algorithm exploits the local and global protein properties, like, sequence and secondary structure motifs, buried fractions, microenvironments and protein/enzyme class. DeepCys outperformed most of the multiple and specific Cys function algorithms. This method can predict maximum number of cysteine functions. Moreover, for the first time, explicitly predicts thioether function. This tool was used to elucidate the cysteine functions on domains of unknown functions belonging to cytochrome C oxidase subunit-II like transmembrane domains. Apart from the web-server, a standalone program is also available on GitHub (https://github.com/vam-sin/deepcys).
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Affiliation(s)
- Vamsi Nallapareddy
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Shubham Bogam
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Himaja Devarakonda
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Shubham Paliwal
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
| | - Debashree Bandyopadhyay
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, Telangana, India
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Lyu X, Li S, Jiang C, He N, Chen Z, Zou Y, Li L. DeepCSO: A Deep-Learning Network Approach to Predicting Cysteine S-Sulphenylation Sites. Front Cell Dev Biol 2020; 8:594587. [PMID: 33335901 PMCID: PMC7736615 DOI: 10.3389/fcell.2020.594587] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/12/2020] [Indexed: 01/02/2023] Open
Abstract
Cysteine S-sulphenylation (CSO), as a novel post-translational modification (PTM), has emerged as a potential mechanism to regulate protein functions and affect signal networks. Because of its functional significance, several prediction approaches have been developed. Nevertheless, they are based on a limited dataset from Homo sapiens and there is a lack of prediction tools for the CSO sites of other species. Recently, this modification has been investigated at the proteomics scale for a few species and the number of identified CSO sites has significantly increased. Thus, it is essential to explore the characteristics of this modification across different species and construct prediction models with better performances based on the enlarged dataset. In this study, we constructed several classifiers and found that the long short-term memory model with the word-embedding encoding approach, dubbed LSTMWE, performs favorably to the traditional machine-learning models and other deep-learning models across different species, in terms of cross-validation and independent test. The area under the receiver operating characteristic (ROC) curve for LSTMWE ranged from 0.82 to 0.85 for different organisms, which was superior to the reported CSO predictors. Moreover, we developed the general model based on the integrated data from different species and it showed great universality and effectiveness. We provided the on-line prediction service called DeepCSO that included both species-specific and general models, which is accessible through http://www.bioinfogo.org/DeepCSO.
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Affiliation(s)
- Xiaru Lyu
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Shuhao Li
- College of Life Sciences, Qingdao University, Qingdao, China.,School of Basic Medicine, Qingdao University, Qingdao, China
| | - Chunyang Jiang
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Ningning He
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Zhen Chen
- Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, Zhengzhou, China.,Key Laboratory of Rice Biology in Henan Province, Henan Agricultural University, Zhengzhou, China
| | - Yang Zou
- School of Basic Medicine, Qingdao University, Qingdao, China
| | - Lei Li
- School of Basic Medicine, Qingdao University, Qingdao, China.,School of Data Science and Software Engineering, Qingdao University, Qingdao, China
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6
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Do DT, Le TQT, Le NQK. Using deep neural networks and biological subwords to detect protein S-sulfenylation sites. Brief Bioinform 2020; 22:5866114. [PMID: 32613242 DOI: 10.1093/bib/bbaa128] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/11/2020] [Accepted: 05/26/2020] [Indexed: 12/11/2022] Open
Abstract
Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature. Identifying S-sulfenylation sites, therefore, could be the key to decipher its mysterious structures and functions, which are important in cell biology and diseases. However, due to the lack of effective methods, scientists in this field tend to be limited in merely a handful of some wet lab techniques that are time-consuming and not cost-effective. Thus, this motivated us to develop an in silico model for detecting S-sulfenylation sites only from protein sequence information. In this study, protein sequences served as natural language sentences comprising biological subwords. The deep neural network was consequentially employed to perform classification. The performance statistics within the independent dataset including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under the curve rates achieved 85.71%, 69.47%, 77.09%, 0.5554 and 0.833, respectively. Our results suggested that the proposed method (fastSulf-DNN) achieved excellent performance in predicting S-sulfenylation sites compared to other well-known tools on a benchmark dataset.
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Affiliation(s)
- Duyen Thi Do
- Faculty of Applied Sciences, Ton Duc Thang University
| | | | - Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, Taipei Medical University
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Wang M, Cui X, Yu B, Chen C, Ma Q, Zhou H. SulSite-GTB: identification of protein S-sulfenylation sites by fusing multiple feature information and gradient tree boosting. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04792-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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8
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Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09931-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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