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Ayadi I, Nebli S, Ben Marzoug R, Rebai A. Charge cluster occurrence in land plants' mitochondrial proteomes with functional and structural insights. J Biomol Struct Dyn 2024:1-11. [PMID: 38345014 DOI: 10.1080/07391102.2024.2313154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 01/25/2024] [Indexed: 03/22/2024]
Abstract
The Charge Clusters (CCs) are involved in key functions and are distributed according to the organism, the protein's type, and the charge of amino acids. In the present study, we have explored the occurrence, position, and annotation as a first large-scale study of the CCs in land plants mitochondrial proteomes. A new python script was used for data curation. The Finding Clusters Charge in Protein Sequences Program was performed after adjusting the reading window size. A 44316 protein sequences belonging to 52 species of land plants were analysed. The occurrence of Negative Charge Clusters (NCCs) (1.2%) is two times more frequent than the Positive Charge Clusters (PCCs) (0.64%). Moreover, 39 and 30 NCCs were conserved in 88 and 41 proteins in intra and in inter proteomes respectively, while 14 and 21 PCCs were conserved in 53 and 85 protein sequences in intra and inter proteomes consecutively. Sequences carrying mixed CCs are rare (0.12%). Despite this low abundance, CCs play a crucial role in protein function. The CCs tend to be located mainly in the terminal regions of proteins which guarantees specific protein targeting and import into the mitochondria. In addition, the functional annotation of CCs according to Gene Ontology shows that CCs are involved in binding functions of either proteins or macromolecules which are deployed in different metabolic and cellular processes such as RNA editing and transcription. This study may provide valuable information while considering the CCs in understanding the environmental adaptation of plants.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Imen Ayadi
- Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia
| | - Syrine Nebli
- Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia
| | - Riadh Ben Marzoug
- Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia
| | - Ahmed Rebai
- Laboratory of Molecular and Cellular Screening Processes, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia
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Liu X, Zhu B, Dai XW, Xu ZA, Li R, Qian Y, Lu YP, Zhang W, Liu Y, Zheng J. GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier. BMC Genomics 2023; 24:765. [PMID: 38082413 PMCID: PMC10712101 DOI: 10.1186/s12864-023-09834-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 11/23/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. RESULTS In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. CONCLUSION GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite .
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Affiliation(s)
- Xin Liu
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
| | - Bao Zhu
- Cancer Institute, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Xia-Wei Dai
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Zhi-Ao Xu
- School of Life Sciences, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Rui Li
- School of Life Sciences, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Yuting Qian
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ya-Ping Lu
- School of Humanities and Arts, China University of Mining and Technology, Xuzhou, Jiangsu, 221116, China
| | - Wenqing Zhang
- School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Yong Liu
- Cancer Institute, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
| | - Junnian Zheng
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.
- Center of Clinical Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China.
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Murad T, Ali S, Patterson M. Exploring the Potential of GANs in Biological Sequence Analysis. BIOLOGY 2023; 12:854. [PMID: 37372139 DOI: 10.3390/biology12060854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 06/03/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models' performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.
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Affiliation(s)
- Taslim Murad
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, GA 30302, USA
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Wang L, Tan Y, Yang X, Kuang L, Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Brief Bioinform 2022; 23:6553604. [PMID: 35325024 DOI: 10.1093/bib/bbac080] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/11/2022] Open
Abstract
In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.
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Affiliation(s)
- Lei Wang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Yaqin Tan
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Xiaoyu Yang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Pengyao Ping
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
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Class-Imbalanced Voice Pathology Detection and Classification Using Fuzzy Cluster Oversampling Method. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11083450] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.
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