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Adejor J, Tumukunde E, Li G, Lin H, Xie R, Wang S. Impact of Lysine Succinylation on the Biology of Fungi. Curr Issues Mol Biol 2024; 46:1020-1046. [PMID: 38392183 PMCID: PMC10888112 DOI: 10.3390/cimb46020065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/24/2024] Open
Abstract
Post-translational modifications (PTMs) play a crucial role in protein functionality and the control of various cellular processes and secondary metabolites (SMs) in fungi. Lysine succinylation (Ksuc) is an emerging protein PTM characterized by the addition of a succinyl group to a lysine residue, which induces substantial alteration in the chemical and structural properties of the affected protein. This chemical alteration is reversible, dynamic in nature, and evolutionarily conserved. Recent investigations of numerous proteins that undergo significant succinylation have underscored the potential significance of Ksuc in various biological processes, encompassing normal physiological functions and the development of certain pathological processes and metabolites. This review aims to elucidate the molecular mechanisms underlying Ksuc and its diverse functions in fungi. Both conventional investigation techniques and predictive tools for identifying Ksuc sites were also considered. A more profound comprehension of Ksuc and its impact on the biology of fungi have the potential to unveil new insights into post-translational modification and may pave the way for innovative approaches that can be applied across various clinical contexts in the management of mycotoxins.
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Affiliation(s)
- John Adejor
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Elisabeth Tumukunde
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Guoqi Li
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Hong Lin
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Rui Xie
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
| | - Shihua Wang
- Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Key Laboratory of Biopesticide and Chemical Biology of Education Ministry, College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China
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2
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Ahmed SS, Rifat ZT, Rahman MS, Rahman MS. Succinylated lysine residue prediction revisited. Brief Bioinform 2023; 24:6865109. [PMID: 36460620 DOI: 10.1093/bib/bbac510] [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: 07/12/2022] [Revised: 09/30/2022] [Accepted: 10/25/2022] [Indexed: 12/04/2022] Open
Abstract
Lysine succinylation is a kind of post-translational modification (PTM) that plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy, and attention has been given in the literature to develop such models, albeit with only moderate success in the context of different evaluation metrics. One crucial aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids. In contrast, some others used them without considering the inter-dependency among the properties. The combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by combining all the properties. We propose three deep learning architectures: CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC outperforms the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we successfully achieve better sensitivity and specificity by varying the threshold value.
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Affiliation(s)
- Shehab Sarar Ahmed
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
| | - Zaara Tasnim Rifat
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
| | - M Saifur Rahman
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
| | - M Sohel Rahman
- Computer Science and Engineering, Bangladesh University of Engineering and Technology, 1000, Dhaka, Bangladesh
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3
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Jia J, Wu G, Li M, Qiu W. pSuc-EDBAM: Predicting lysine succinylation sites in proteins based on ensemble dense blocks and an attention module. BMC Bioinformatics 2022; 23:450. [PMID: 36316638 PMCID: PMC9620660 DOI: 10.1186/s12859-022-05001-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 10/25/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lysine succinylation is a newly discovered protein post-translational modifications. Predicting succinylation sites helps investigate the metabolic disease treatments. However, the biological experimental approaches are costly and inefficient, it is necessary to develop efficient computational approaches. RESULTS In this paper, we proposed a novel predictor based on ensemble dense blocks and an attention module, called as pSuc-EDBAM, which adopted one hot encoding to derive the feature maps of protein sequences, and generated the low-level feature maps through 1-D CNN. Afterward, the ensemble dense blocks were used to capture feature information at different levels in the process of feature learning. We also introduced an attention module to evaluate the importance degrees of different features. The experimental results show that Acc reaches 74.25%, and MCC reaches 0.2927 on the testing dataset, which suggest that the pSuc-EDBAM outperforms the existing predictors. CONCLUSIONS The experimental results of ten-fold cross-validation on the training dataset and independent test on the testing dataset showed that pSuc-EDBAM outperforms the existing succinylation site predictors and can predict potential succinylation sites effectively. The pSuc-EDBAM is feasible and obtains the credible predictive results, which may also provide valuable references for other related research. To make the convenience of the experimental scientists, a user-friendly web server has been established ( http://bioinfo.wugenqiang.top/pSuc-EDBAM/ ), by which the desired results can be easily obtained.
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Affiliation(s)
- Jianhua Jia
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| | - Genqiang Wu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
| | - Meifang Li
- Computer Department, Nanchang Institute of Technology, Nanchang, 330044 China
| | - Wangren Qiu
- Computer Department, Jingdezhen Ceramic University, Jingdezhen, 333403 China
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4
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Smith BJ, Brandão-Teles C, Zuccoli GS, Reis-de-Oliveira G, Fioramonte M, Saia-Cereda VM, Martins-de-Souza D. Protein Succinylation and Malonylation as Potential Biomarkers in Schizophrenia. J Pers Med 2022; 12:jpm12091408. [PMID: 36143193 PMCID: PMC9500613 DOI: 10.3390/jpm12091408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/24/2022] [Accepted: 08/27/2022] [Indexed: 11/16/2022] Open
Abstract
Two protein post-translational modifications, lysine succinylation and malonylation, are implicated in protein regulation, glycolysis, and energy metabolism. The precursors of these modifications, succinyl-CoA and malonyl-CoA, are key players in central metabolic processes. Both modification profiles have been proven to be responsive to metabolic stimuli, such as hypoxia. As mitochondrial dysfunction and metabolic dysregulation are implicated in schizophrenia and other psychiatric illnesses, these modification profiles have the potential to reveal yet another layer of protein regulation and can furthermore represent targets for biomarkers that are indicative of disease as well as its progression and treatment. In this work, data from shotgun mass spectrometry-based quantitative proteomics were compiled and analyzed to probe the succinylome and malonylome of postmortem brain tissue from patients with schizophrenia against controls and the human oligodendrocyte precursor cell line MO3.13 with the dizocilpine chemical model for schizophrenia, three antipsychotics, and co-treatments. Several changes in the succinylome and malonylome were seen in these comparisons, revealing these modifications to be a largely under-studied yet important form of protein regulation with broad potential applications.
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Affiliation(s)
- Bradley Joseph Smith
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
- Correspondence: (B.J.S.); (D.M.-d.-S.); Tel.: +55-(19)-3521-6129 (D.M.-d.-S.)
| | - Caroline Brandão-Teles
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
| | - Giuliana S. Zuccoli
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
| | - Guilherme Reis-de-Oliveira
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
| | - Mariana Fioramonte
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
| | - Verônica M. Saia-Cereda
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Institute of Biology, Department of Biochemistry and Tissue Biology, University of Campinas, Campinas 13083-862, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Científico e Tecnológico, São Paulo 05403-000, Brazil
- Experimental Medicine Research Cluster (EMRC), University of Campinas, Campinas 13083-862, Brazil
- D’Or Institute for Research and Education (IDOR), São Paulo 04501-000, Brazil
- Correspondence: (B.J.S.); (D.M.-d.-S.); Tel.: +55-(19)-3521-6129 (D.M.-d.-S.)
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Sohrawordi M, Hossain MA, Hasan MAM. PLP_FS: prediction of lysine phosphoglycerylation sites in protein using support vector machine and fusion of multiple F_Score feature selection. Brief Bioinform 2022; 23:6655632. [PMID: 35929355 DOI: 10.1093/bib/bbac306] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 07/05/2022] [Accepted: 07/06/2022] [Indexed: 11/14/2022] Open
Abstract
A newly invented post-translational modification (PTM), phosphoglycerylation, has shown its essential role in the construction and functional properties of proteins and dangerous human diseases. Hence, it is very urgent to know about the molecular mechanism behind the phosphoglycerylation process to develop the drugs for related diseases. But accurately identifying of phosphoglycerylation site from a protein sequence in a laboratory is a very difficult and challenging task. Hence, the construction of an efficient computation model is greatly sought for this purpose. A little number of computational models are currently available for identifying the phosphoglycerylation sites, which are not able to reach their prediction capability at a satisfactory level. Therefore, an effective predictor named PLP_FS has been designed and constructed to identify phosphoglycerylation sites in this study. For the training purpose, an optimal number of feature sets was obtained by fusion of multiple F_Score feature selection techniques from the features generated by three types of sequence-based feature extraction methods and fitted with the support vector machine classification technique to the prediction model. On the other hand, the k-neighbor near cleaning and SMOTE methods were also implemented to balance the benchmark dataset. The suggested model in 10-fold cross-validation obtained an accuracy of 99.22%, a sensitivity of 98.17% and a specificity of 99.75% according to the experimental findings, which are better than other currently available predictors for accurately identifying the phosphoglycerylation sites.
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Affiliation(s)
- Md Sohrawordi
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
- Dept. of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Md Ali Hossain
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
| | - Md Al Mehedi Hasan
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
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6
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Xia Y, Jiang M, Luo Y, Feng G, Jia G, Zhang H, Wang P, Ge R. SuccSPred2.0: A Two-Step Model to Predict Succinylation Sites Based on Multifeature Fusion and Selection Algorithm. J Comput Biol 2022; 29:1085-1094. [PMID: 35714347 DOI: 10.1089/cmb.2022.0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Protein succinylation is a novel type of post-translational modification in recent decade years. It played an important role in biological structure and functions verified by experiments. However, it is time consuming and laborious for the wet experimental identification of succinylation sites. Traditional technology cannot adapt to the rapid growth of the biological sequence data sets. In this study, a new computational method named SuccSPred2.0 was proposed to identify succinylation sites in the protein sequences based on multifeature fusion and maximal information coefficient (MIC) method. SuccSPred2.0 was implemented based on a two-step strategy. At first, high-dimension features were reduced by linear discriminant analysis to prevent overfitting. Subsequently, MIC method was employed to select the important features binding classifiers to predict succinylation sites. From the compared experiments on 10-fold cross-validation and independent test data sets, SuccSPred2.0 obtained promising improvements. Comparative experiments showed that SuccSPred2.0 was superior to previous tools in identifying succinylation sites in the given proteins.
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Affiliation(s)
- Yixiao Xia
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Minchao Jiang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Yizhang Luo
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Guanwen Feng
- Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Gangyong Jia
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Hua Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Pu Wang
- Computer School, Hubei University of Arts and Science, Xiangyang, China
| | - Ruiquan Ge
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
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7
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Deep Learning-Based Advances In Protein Posttranslational Modification Site and Protein Cleavage Prediction. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2499:285-322. [PMID: 35696087 DOI: 10.1007/978-1-0716-2317-6_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Posttranslational modification (PTM ) is a ubiquitous phenomenon in both eukaryotes and prokaryotes which gives rise to enormous proteomic diversity. PTM mostly comes in two flavors: covalent modification to polypeptide chain and proteolytic cleavage. Understanding and characterization of PTM is a fundamental step toward understanding the underpinning of biology. Recent advances in experimental approaches, mainly mass-spectrometry-based approaches, have immensely helped in obtaining and characterizing PTMs. However, experimental approaches are not enough to understand and characterize more than 450 different types of PTMs and complementary computational approaches are becoming popular. Recently, due to the various advancements in the field of Deep Learning (DL), along with the explosion of applications of DL to various fields, the field of computational prediction of PTM has also witnessed the development of a plethora of deep learning (DL)-based approaches. In this book chapter, we first review some recent DL-based approaches in the field of PTM site prediction. In addition, we also review the recent advances in the not-so-studied PTM , that is, proteolytic cleavage predictions. We describe advances in PTM prediction by highlighting the Deep learning architecture, feature encoding, novelty of the approaches, and availability of the tools/approaches. Finally, we provide an outlook and possible future research directions for DL-based approaches for PTM prediction.
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8
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Zhang D, Wang S. A protein succinylation sites prediction method based on the hybrid architecture of LSTM network and CNN. J Bioinform Comput Biol 2022; 20:2250003. [PMID: 35191361 DOI: 10.1142/s0219720022500032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The succinylation modification of protein participates in the regulation of a variety of cellular processes. Identification of modified substrates with precise sites is the basis for understanding the molecular mechanism and regulation of succinylation. In this work, we picked and chose five superior feature codes: CKSAAP, ACF, BLOSUM62, AAindex, and one-hot, according to their performance in the problem of succinylation sites prediction. Then, LSTM network and CNN were used to construct four models: LSTM-CNN, CNN-LSTM, LSTM, and CNN. The five selected features were, respectively, input into each of these four models for training to compare the four models. Based on the performance of each model, the optimal model among them was chosen to construct a hybrid model DeepSucc that was composed of five sub-modules for integrating heterogeneous information. Under the 10-fold cross-validation, the hybrid model DeepSucc achieves 86.26% accuracy, 84.94% specificity, 87.57% sensitivity, 0.9406 AUC, and 0.7254 MCC. When compared with other prediction tools using an independent test set, DeepSucc outperformed them in sensitivity and MCC. The datasets and source codes can be accessed at https://github.com/1835174863zd/DeepSucc.
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Affiliation(s)
- Die Zhang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
| | - Shunfang Wang
- Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming 650504, P. R. China
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9
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Zeng Y, Chen Y, Yuan Z. iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier. BioData Min 2022; 15:3. [PMID: 35144656 PMCID: PMC8832670 DOI: 10.1186/s13040-022-00290-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 01/30/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Lysine succinylation is a type of protein post-translational modification which is widely involved in cell differentiation, cell metabolism and other important physiological activities. To study the molecular mechanism of succinylation in depth, succinylation sites need to be accurately identified, and because experimental approaches are costly and time-consuming, there is a great demand for reliable computational methods. Feature extraction is a key step in building succinylation site prediction models, and the development of effective new features improves predictive accuracy. Because the number of false succinylation sites far exceeds that of true sites, traditional classifiers perform poorly, and designing a classifier to effectively handle highly imbalanced datasets has always been a challenge. RESULTS A new computational method, iSuc-ChiDT, is proposed to identify succinylation sites in proteins. In iSuc-ChiDT, chi-square statistical difference table encoding is developed to extract positional features, and has a higher predictive accuracy and fewer features compared to common position-based encoding schemes such as binary encoding and physicochemical property encoding. Single amino acid and undirected pair-coupled amino acid composition features are supplemented to improve the fault tolerance for residue insertions and deletions. After feature selection by Chi-MIC-share algorithm, the chi-square decision table (ChiDT) classifier is constructed for imbalanced classification. With a training set of 4748:50,551(true: false sites), ChiDT clearly outperforms traditional classifiers in predictive accuracy, and runs fast. Using an independent testing set of experimentally identified succinylation sites, iSuc-ChiDT achieves a sensitivity of 70.47%, a specificity of 66.27%, a Matthews correlation coefficient of 0.205, and a global accuracy index Q9 of 0.683, showing a significant improvement in sensitivity and overall accuracy compared to PSuccE, Success, SuccinSite, and other existing succinylation site predictors. CONCLUSIONS iSuc-ChiDT shows great promise in predicting succinylation sites and is expected to facilitate further experimental investigation of protein succinylation.
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Affiliation(s)
- Ying Zeng
- School of Computer and Communication, Hunan Institute of Engineering, Xiangtan, 411104 Hunan China
| | - Yuan Chen
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, 410128 Hunan China
| | - Zheming Yuan
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, 410128 Hunan China
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10
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Ning Q, Ma Z, Zhao X, Yin M. SSKM_Succ: A Novel Succinylation Sites Prediction Method Incorporating K-Means Clustering With a New Semi-Supervised Learning Algorithm. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:643-652. [PMID: 32750881 DOI: 10.1109/tcbb.2020.3006144] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Protein succinylation is a type of post-translational modification (PTM) that occurs on lysine sites and plays a key role in protein conformation regulation and cellular function control. When training in computational method, it is difficult to designate negative samples because of the uncertainty of non-succinylation lysine sites, and if not handled properly, it may affect the performance of computational models dramatically. Therefore, we propose a new semi-supervised learning method to identify reliable non-succinylation lysine sites as negative samples. This method, named SSKM_Succ, also employs K-means clustering to divide data into 5 clusters. Besides, information of proximal PTMs and three kinds of sequence features (grey pseudo amino acid composition, K-space and position-special amino acid propensity) are utilized to formulate protein. Then, we perform a two-step feature selection to remove redundant features and construct the optimization model for each cluster. Finally, support vector machine is applied to construct a prediction model for each cluster. Promising results are obtained by this method with an accuracy of 80.18 percent for succinylation sites on the independent testing dataset. Meanwhile, we compare the result with other existing tools, and it shows that our method is promising for predicting succinylation sites. Through analysis, we further verify that succinylated protein has potential effects on amino acid degradation and fatty acid metabolism, and speculate that protein succinylation may be closely related to neurodegenerative diseases. The code of SSKM_Succ is available on the web https://github.com/yangyq505/SSKM_Succ.git.
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11
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pQLyCar: Peptide-based dynamic query-driven sample rescaling strategy for identifying carboxylation sites combined with KNN and SVM. Anal Biochem 2021; 633:114386. [PMID: 34543644 DOI: 10.1016/j.ab.2021.114386] [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: 06/01/2021] [Revised: 09/02/2021] [Accepted: 09/14/2021] [Indexed: 11/23/2022]
Abstract
Lysine carboxylation is one of the most crucial type of post-translation modification, which plays a significant role in catalytic mechanisms. Therefore, it is essential to study lysine carboxylation and explore its biological mechanism. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods are much more convenience and faster. Therefore, it is urgent to establish an accurate carboxylation identification model. Herein we proposed a method, named pQLyCar for identification of lysine carboxylation using SVM as classifier. In pQLyCar, a peptide-based dynamic query-driven sample rescaling strategy (pDQD-SR) is proposed to address the class imbalance of training data, which builds a specific prediction model for each query sample. KNN algorithm calculates distance between samples according to original sequences instead of feature vectors. Information entropy is applied to select optimal size of sliding window and various types of sequence- and position-based features are incorporated for construction of feature space, including residues composition (RC), K-space and position-special amino acid propensity (PSAAP). Finally, the performance of pQLyCar is measured with a specificity of 96.49% and a sensibility of 99.59% using jackknife test method, which indicated that pQLyCar method can be a useful tool for prediction of lysine carboxylation sites.
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12
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Wang H, Zhao J, Zhao H, Li H, Wang J. CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model. BMC Bioinformatics 2021; 22:512. [PMID: 34670488 PMCID: PMC8527680 DOI: 10.1186/s12859-021-04433-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 10/05/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduce the anticancer peptide secondary structures as additional features and propose an effective computational model, CL-ACP, that uses a combined network and attention mechanism to predict anticancer peptides. RESULTS The CL-ACP model uses secondary structures and original sequences of anticancer peptides to construct the feature space. The long short-term memory and convolutional neural network are used to extract the contextual dependence and local correlations of the feature space. Furthermore, a multi-head self-attention mechanism is used to strengthen the anticancer peptide sequences. Finally, three categories of feature information are classified by cascading. CL-ACP was validated using two types of datasets, anticancer peptide datasets and antimicrobial peptide datasets, on which it achieved good results compared to previous methods. CL-ACP achieved the highest AUC values of 0.935 and 0.972 on the anticancer peptide and antimicrobial peptide datasets, respectively. CONCLUSIONS CL-ACP can effectively recognize antimicrobial peptides, especially anticancer peptides, and the parallel combined neural network structure of CL-ACP does not require complex feature design and high time cost. It is suitable for application as a useful tool in antimicrobial peptide design.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jian Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Hong Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Haolin Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Juan Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
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13
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Sohrawordi M, Hossain MA. Prediction of lysine formylation sites using support vector machine based on the sample selection from majority classes and synthetic minority over-sampling techniques. Biochimie 2021; 192:125-135. [PMID: 34627982 DOI: 10.1016/j.biochi.2021.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 10/03/2021] [Accepted: 10/05/2021] [Indexed: 12/22/2022]
Abstract
Lysine formylation is a newly discovered and mostly interested type of post-translational modification (PTM) that is generally found on core and linker histone proteins of prokaryote and eukaryote and plays various important roles on the regulation of various cellular mechanisms. Hence, it is very urgent to properly identify formylation site in protein for understanding the molecular mechanism of formylation deeply and defining drug for relevant diseases. As experimentally identification of formylation site using traditional processes are expensive and time consuming, a simple and high speedy mathematical model for predicting accurately lysine formylation sites is highly desired. A useful computational model named PLF_SVM is deigned and proposed in this study by using binary encoding (BE), amino acid composition (AAC), reverse position relative incidence matrix (RPRIM), position relative incidence matrix (PRIM), and position specific amino acid propensity (PSAAP) feature generation methods for predicting formylated and non-formylated lysine sites. Besides, the Synthetic Minority Oversampling Technique (SMOTE) and a proposed sample selection strategy named EnSVM are applied to handle the imbalance training dataset problem. Thereafter, the optimal number of features are selected by F-score method to train the model. Finally, it has been seen that PLF_SVM outperforms the state-of-the-art approaches in validation and independent test with an accuracy of 98.61% and 98.77% respectively. At https://plf-svm.herokuapp.com/, a user-friendly web tool is also created for identifying formylation sites. Therefore, the proposed method may be helpful guideline for the analysis and prediction of formylated lysine and knowing the process of cellular regulation.
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Affiliation(s)
- Md Sohrawordi
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh; Dept. of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh.
| | - Md Ali Hossain
- Dept. of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
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Wang H, Zhao H, Yan Z, Zhao J, Han J. MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network. Biomolecules 2021; 11:biom11060872. [PMID: 34208298 PMCID: PMC8231176 DOI: 10.3390/biom11060872] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 12/26/2022] Open
Abstract
Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.
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Structure, Biosynthesis, and Biological Activity of Succinylated Forms of Bacteriocin BacSp222. Int J Mol Sci 2021; 22:ijms22126256. [PMID: 34200765 PMCID: PMC8230399 DOI: 10.3390/ijms22126256] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/04/2021] [Accepted: 06/07/2021] [Indexed: 01/21/2023] Open
Abstract
BacSp222 is a multifunctional peptide produced by Staphylococcus pseudintermedius 222. This 50-amino acid long peptide belongs to subclass IId of bacteriocins and forms a four-helix bundle molecule. In addition to bactericidal functions, BacSp222 possesses also features of a virulence factor, manifested in immunomodulatory and cytotoxic activities toward eukaryotic cells. In the present study, we demonstrate that BacSp222 is produced in several post-translationally modified forms, succinylated at the ε-amino group of lysine residues. Such modifications have not been previously described for any bacteriocins. NMR and circular dichroism spectroscopy studies have shown that the modifications do not alter the spatial structure of the peptide. At the same time, succinylation significantly diminishes its bactericidal and cytotoxic potential. We demonstrate that the modification of the bacteriocin is an effect of non-enzymatic reaction with a highly reactive intracellular metabolite, i.e., succinyl-coenzyme A. The production of succinylated forms of the bacteriocin depends on environmental factors and on the access of bacteria to nutrients. Our study indicates that the production of succinylated forms of bacteriocin occurs in response to the changing environment, protects producer cells against the autotoxicity of the excreted peptide, and limits the pathogenicity of the strain.
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LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9923112. [PMID: 34159204 PMCID: PMC8188601 DOI: 10.1155/2021/9923112] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/25/2021] [Accepted: 05/03/2021] [Indexed: 11/17/2022]
Abstract
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods were developed to deal with this challenge, few considered semantic relationship between residues. We combined long short-term memory (LSTM) and convolutional neural network (CNN) into a deep learning method for predicting succinylation site. The proposed method obtained a Matthews correlation coefficient of 0.2508 on the independent test, outperforming state of the art methods. We also performed the enrichment analysis of succinylation proteins. The results showed that functions of succinylation were conserved across species but differed to a certain extent with species. On basis of the proposed method, we developed a user-friendly web server for predicting succinylation sites.
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17
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Li A, Deng Y, Tan Y, Chen M. A Transfer Learning-Based Approach for Lysine Propionylation Prediction. Front Physiol 2021; 12:658633. [PMID: 33967828 PMCID: PMC8096918 DOI: 10.3389/fphys.2021.658633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/15/2021] [Indexed: 12/12/2022] Open
Abstract
Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient (MCC) of 0.6615 on the 10-fold crossvalidation and 0.3174 on the independent test, outperforming state-of-the-art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/.
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Affiliation(s)
- Ang Li
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Yingwei Deng
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Yan Tan
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
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18
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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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Yang Y, Wang H, Li W, Wang X, Wei S, Liu Y, Xu Y. Prediction and analysis of multiple protein lysine modified sites based on conditional wasserstein generative adversarial networks. BMC Bioinformatics 2021; 22:171. [PMID: 33789579 PMCID: PMC8010967 DOI: 10.1186/s12859-021-04101-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 03/23/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein's function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins. METHOD We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories. RESULTS In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https://github.com/Lab-Xu/MultiLyGAN . CONCLUSIONS The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
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Affiliation(s)
- Yingxi Yang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Hui Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100080, China
| | - Wen Li
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaobo Wang
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China
| | - Shizhao Wei
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yulong Liu
- No. 15 Research Institute, China Electronics Technology Group Corporation, Beijing, 100083, China
| | - Yan Xu
- Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, 100083, China.
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Ao C, Yu L, Zou Q. Prediction of bio-sequence modifications and the associations with diseases. Brief Funct Genomics 2020; 20:1-18. [PMID: 33313647 DOI: 10.1093/bfgp/elaa023] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/22/2022] Open
Abstract
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.
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Zhang L, Liu M, Qin X, Liu G. Succinylation Site Prediction Based on Protein Sequences Using the IFS-LightGBM (BO) Model. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:8858489. [PMID: 33224267 PMCID: PMC7673955 DOI: 10.1155/2020/8858489] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/25/2020] [Accepted: 10/24/2020] [Indexed: 01/08/2023]
Abstract
Succinylation is an important posttranslational modification of proteins, which plays a key role in protein conformation regulation and cellular function control. Many studies have shown that succinylation modification on protein lysine residue is closely related to the occurrence of many diseases. To understand the mechanism of succinylation profoundly, it is necessary to identify succinylation sites in proteins accurately. In this study, we develop a new model, IFS-LightGBM (BO), which utilizes the incremental feature selection (IFS) method, the LightGBM feature selection method, the Bayesian optimization algorithm, and the LightGBM classifier, to predict succinylation sites in proteins. Specifically, pseudo amino acid composition (PseAAC), position-specific scoring matrix (PSSM), disorder status, and Composition of k-spaced Amino Acid Pairs (CKSAAP) are firstly employed to extract feature information. Then, utilizing the combination of the LightGBM feature selection method and the incremental feature selection (IFS) method selects the optimal feature subset for the LightGBM classifier. Finally, to increase prediction accuracy and reduce the computation load, the Bayesian optimization algorithm is used to optimize the parameters of the LightGBM classifier. The results reveal that the IFS-LightGBM (BO)-based prediction model performs better when it is evaluated by some common metrics, such as accuracy, recall, precision, Matthews Correlation Coefficient (MCC), and F-measure.
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Affiliation(s)
- Lu Zhang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
| | - Min Liu
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
| | - Xinyi Qin
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
| | - Guangzhong Liu
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai 201306, China
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Thapa N, Chaudhari M, McManus S, Roy K, Newman RH, Saigo H, Kc DB. DeepSuccinylSite: a deep learning based approach for protein succinylation site prediction. BMC Bioinformatics 2020; 21:63. [PMID: 32321437 PMCID: PMC7178942 DOI: 10.1186/s12859-020-3342-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 01/08/2020] [Indexed: 01/15/2023] Open
Abstract
Background Protein succinylation has recently emerged as an important and common post-translation modification (PTM) that occurs on lysine residues. Succinylation is notable both in its size (e.g., at 100 Da, it is one of the larger chemical PTMs) and in its ability to modify the net charge of the modified lysine residue from + 1 to − 1 at physiological pH. The gross local changes that occur in proteins upon succinylation have been shown to correspond with changes in gene activity and to be perturbed by defects in the citric acid cycle. These observations, together with the fact that succinate is generated as a metabolic intermediate during cellular respiration, have led to suggestions that protein succinylation may play a role in the interaction between cellular metabolism and important cellular functions. For instance, succinylation likely represents an important aspect of genomic regulation and repair and may have important consequences in the etiology of a number of disease states. In this study, we developed DeepSuccinylSite, a novel prediction tool that uses deep learning methodology along with embedding to identify succinylation sites in proteins based on their primary structure. Results Using an independent test set of experimentally identified succinylation sites, our method achieved efficiency scores of 79%, 68.7% and 0.48 for sensitivity, specificity and MCC respectively, with an area under the receiver operator characteristic (ROC) curve of 0.8. In side-by-side comparisons with previously described succinylation predictors, DeepSuccinylSite represents a significant improvement in overall accuracy for prediction of succinylation sites. Conclusion Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein succinylation.
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Affiliation(s)
- Niraj Thapa
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Meenal Chaudhari
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Sean McManus
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Kaushik Roy
- Department of Computer Science, North Carolina A&T State University, Greensboro, NC, USA
| | - Robert H Newman
- Department of Biology, North Carolina A&T State University, Greensboro, NC, USA
| | - Hiroto Saigo
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | - Dukka B Kc
- Electrical Engineering and Computer Science Department, Wichita State University, Wichita, KS, USA.
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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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Zhu Y, Jia C, Li F, Song J. Inspector: a lysine succinylation predictor based on edited nearest-neighbor undersampling and adaptive synthetic oversampling. Anal Biochem 2020; 593:113592. [DOI: 10.1016/j.ab.2020.113592] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 01/14/2020] [Accepted: 01/17/2020] [Indexed: 12/13/2022]
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25
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Ning Q, Ma Z, Zhao X. dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components. J Theor Biol 2019; 470:43-49. [DOI: 10.1016/j.jtbi.2019.03.011] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 03/09/2019] [Accepted: 03/13/2019] [Indexed: 10/27/2022]
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Hasan MM, Khatun MS, Kurata H. Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites. Cells 2019; 8:cells8020095. [PMID: 30696115 PMCID: PMC6406724 DOI: 10.3390/cells8020095] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 01/24/2019] [Accepted: 01/24/2019] [Indexed: 12/19/2022] Open
Abstract
Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools.
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Affiliation(s)
- Md Mehedi Hasan
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680⁻4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| | - Mst Shamima Khatun
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680⁻4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
| | - Hiroyuki Kurata
- Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680⁻4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
- Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan.
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