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The Role of Transmembrane Proteins in Plant Growth, Development, and Stress Responses. Int J Mol Sci 2022; 23:ijms232113627. [PMID: 36362412 PMCID: PMC9655316 DOI: 10.3390/ijms232113627] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/02/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Transmembrane proteins participate in various physiological activities in plants, including signal transduction, substance transport, and energy conversion. Although more than 20% of gene products are predicted to be transmembrane proteins in the genome era, due to the complexity of transmembrane domains they are difficult to reliably identify in the predicted protein, and they may have different overall three-dimensional structures. Therefore, it is challenging to study their biological function. In this review, we describe the typical structures of transmembrane proteins and their roles in plant growth, development, and stress responses. We propose a model illustrating the roles of transmembrane proteins during plant growth and response to various stresses, which will provide important references for crop breeding.
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Li X, Zhang F, Shi Y, Bao B, Sun G. Assessing the quality consistency of Rong'e Yishen oral liquid by five-wavelength maximization profilings and electrochemical fingerprints combined with antioxidant activity analyses. Anal Chim Acta 2021; 1192:339348. [DOI: 10.1016/j.aca.2021.339348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/12/2021] [Accepted: 11/30/2021] [Indexed: 01/23/2023]
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Arango-Argoty GA, Jaramillo-Garzón JA, Castellanos-Domínguez G. Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins. J Theor Biol 2015; 364:121-30. [PMID: 25219623 DOI: 10.1016/j.jtbi.2014.08.051] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 08/27/2014] [Accepted: 08/28/2014] [Indexed: 11/16/2022]
Abstract
Predicting the localization of a protein has become a useful practice for inferring its function. Most of the reported methods to predict subcellular localizations in Gram-negative bacterial proteins make use of standard protein representations that generally do not take into account the distribution of the amino acids and the structural information of the proteins. Here, we propose a protein representation based on the structural information contained in the pairwise statistical contact potentials. The wavelet transform decodes the information contained in the primary structure of the proteins, allowing the identification of patterns along the proteins, which are used to characterize the subcellular localizations. Then, a support vector machine classifier is trained to categorize them. Cellular compartments like periplasm and extracellular medium are difficult to predict, having a high false negative rate. The wavelet-based method achieves an overall high performance while maintaining a low false negative rate, particularly, on "periplasm" and "extracellular medium". Our results suggest the proposed protein characterization is a useful alternative to representing and predicting protein sequences over the classical and cutting edge protein depictions.
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Affiliation(s)
- G A Arango-Argoty
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, s. Manizales, Campus La Nubia, km 7 via al Magdalena, Manizales, Colombia; Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, 3501 Fifth Ave, Pittsburgh, PA 15260, USA.
| | - J A Jaramillo-Garzón
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, s. Manizales, Campus La Nubia, km 7 via al Magdalena, Manizales, Colombia; Research Center of the Instituto Tecnologico Metropolitano, Calle 73 No 76A-354, Medellín, Colombia
| | - G Castellanos-Domínguez
- Signal Processing and Recognition Group, Universidad Nacional de Colombia, s. Manizales, Campus La Nubia, km 7 via al Magdalena, Manizales, Colombia
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Yu B, Zhang Y. A simple method for predicting transmembrane proteins based on wavelet transform. Int J Biol Sci 2012; 9:22-33. [PMID: 23289014 PMCID: PMC3535531 DOI: 10.7150/ijbs.5371] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2012] [Accepted: 12/02/2012] [Indexed: 11/05/2022] Open
Abstract
The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments (TMHs). So far, several prediction methods have been reported, but there are some deficiencies in prediction accuracy and adaptability in these methods. In this paper, a method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG is chosen as an example to describe the prediction process by this method. 80 proteins with known 3D structure from Mptopo database are chosen at random as data sets (including 325 TMHs) and 80 sequences are divided into 13 groups according to their function and type. TMHs prediction is carried out for each group of membrane protein sequences and obtain satisfactory result. To verify the feasibility of this method, 80 membrane protein sequences are treated as test sets, 308 TMHs can be predicted and the prediction accuracy is 96.3%. Compared with the main prediction results of seven popular prediction methods, the obtained results indicate that the proposed method in this paper has higher prediction accuracy.
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Affiliation(s)
- Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, Shandong, China.
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Yang X, Yan H. Analysis of DNA deformation patterns in nucleosome core particles based on isometric feature mapping and continuous wavelet transform. Chem Phys Lett 2012. [DOI: 10.1016/j.cplett.2012.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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OligoPred: A web-server for predicting homo-oligomeric proteins by incorporating discrete wavelet transform into Chou's pseudo amino acid composition. J Mol Graph Model 2011; 30:129-34. [DOI: 10.1016/j.jmgm.2011.06.014] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2011] [Revised: 06/18/2011] [Accepted: 06/30/2011] [Indexed: 01/13/2023]
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Qiu JD, Sun XY, Suo SB, Shi SP, Huang SY, Liang RP, Zhang L. Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: An approach from discrete wavelet transformation. Biochimie 2011; 93:1132-8. [DOI: 10.1016/j.biochi.2011.03.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2010] [Accepted: 03/28/2011] [Indexed: 12/16/2022]
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Qiu JD, Sun XY, Huang JH, Liang RP. Prediction of the Types of Membrane Proteins Based on Discrete Wavelet Transform and Support Vector Machines. Protein J 2010; 29:114-9. [PMID: 20165909 DOI: 10.1007/s10930-010-9230-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jian-Ding Qiu
- Department of Chemistry and Institute for Advanced Study, Nanchang University, 330031, Nanchang, People's Republic of China.
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Qiu JD, Luo SH, Huang JH, Sun XY, Liang RP. Predicting subcellular location of apoptosis proteins based on wavelet transform and support vector machine. Amino Acids 2009; 38:1201-8. [DOI: 10.1007/s00726-009-0331-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2009] [Accepted: 07/20/2009] [Indexed: 11/28/2022]
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Qiu JD, Luo SH, Huang JH, Liang RP. Using support vector machines for prediction of protein structural classes based on discrete wavelet transform. J Comput Chem 2009; 30:1344-50. [DOI: 10.1002/jcc.21115] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Qiu JD, Luo SH, Huang JH, Liang RP. Using support vector machines to distinguish enzymes: approached by incorporating wavelet transform. J Theor Biol 2008; 256:625-31. [PMID: 19049810 DOI: 10.1016/j.jtbi.2008.10.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2008] [Revised: 09/26/2008] [Accepted: 10/20/2008] [Indexed: 10/21/2022]
Abstract
The enzymatic attributes of newly found protein sequences are usually determined either by biochemical analysis of eukaryotic and prokaryotic genomes or by microarray chips. These experimental methods are both time-consuming and costly. With the explosion of protein sequences registered in the databanks, it is highly desirable to develop an automated method to identify whether a given new sequence belongs to enzyme or non-enzyme. The discrete wavelet transform (DWT) and support vector machine (SVM) have been used in this study for distinguishing enzyme structures from non-enzymes. The networks have been trained and tested on two datasets of proteins with different wavelet basis functions, decomposition scales and hydrophobicity data types. Maximum accuracy has been obtained using SVM with a wavelet function of Bior2.4, a decomposition scale j=5, and Kyte-Doolittle hydrophobicity scales. The results obtained by the self-consistency test, jackknife test and independent dataset test are encouraging, which indicates that the proposed method can be employed as a useful assistant technique for distinguishing enzymes from non-enzymes.
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Affiliation(s)
- Jian-Ding Qiu
- Department of Chemistry, Nanchang University, Nanchang 330031, PR China.
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Liu H, Wang R, Lu X, Chen J, Liu X, Ding L. A new approach to the prediction of transmembrane structures. Sci Bull (Beijing) 2008; 53:1011-1014. [PMID: 32214729 PMCID: PMC7088861 DOI: 10.1007/s11434-008-0055-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2007] [Accepted: 10/24/2007] [Indexed: 11/29/2022]
Abstract
About 20%–30% of genome products have been predicted as membrane proteins, which have significant biological functions. The prediction of the amount and position for the transmembrane protein helical segments (TMHs) is the hot spot in bioinformatics. In this paper, a new approach, maximum spectrum of continuous wavelet transform (MSCWT), is proposed to predict TMHs. The predictions for eight SARS-CoV membrane proteins indicate that MSCWT has the same capacity with software TMpred. Moreover, the test on a dataset of 131 structure-known proteins with 548 TMHs shows that the prediction accuracy of MSCWT for TMHs is 91.6% and that for membrane protein is 89.3%.
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Affiliation(s)
- HongDe Liu
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070 China
| | - Rui Wang
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070 China
| | - XiaoQuan Lu
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070 China
| | - Jing Chen
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070 China
| | - Xiuhui Liu
- College of Chemistry and Chemical Engineering, Northwest Normal University, Lanzhou, 730070 China
| | - Lan Ding
- College of Life Science, Northwest Normal University, Lanzhou, 730070 China
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Wen Z, Li M, Li Y, Guo Y, Wang K. Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition. Amino Acids 2006; 32:277-83. [PMID: 16729188 DOI: 10.1007/s00726-006-0341-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2006] [Accepted: 03/23/2006] [Indexed: 11/26/2022]
Abstract
As an important transmembrane protein family in eukaryon, G-protein coupled receptors (GPCRs) play a significant role in cellular signal transduction and are important targets for drug design. However, it is very difficult to resolve their tertiary structure by X-ray crystallography. In this study, we have developed a Delaunay model, which constructs a series of simplexes with latent variables to classify the families of GPCRs and projects unknown sequences to principle component space (PC-space) to predict their topology. Computational results show that, for the classification of GPCRs, the method achieves the accuracy of 91.0 and 87.6% for Class A, more than 80% for the other three classes in differentiating GPCRs from non-GPCRs and 70% for discriminating between four major classes of GPCR, respectively. When recognizing the structure of GPCRs, all the N-terminals of sequences can be determined correctly. The maximum accuracy of predicting transmembrane segments is achieved in the 7th transmembrane segment of Rhodopsin, which is 99.4%, and the average error is 2.1 amino acids, which is the lowest in all of the segments prediction. This method could provide structural information of a novel GPCR as a tool for experiments and other algorithms of structure prediction of GPCRs. Academic users should send their request for the MATLAB program for classifying GPCRs and predicting the topology of them at liml@scu.edu.cn .
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Affiliation(s)
- Z Wen
- College of Chemistry, Sichuan University, Chengdu, Sichuan, China
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Ganapathiraju M, Balakrishnan N, Reddy R, Klein-Seetharaman J. Computational Biology and Language. AMBIENT INTELLIGENCE FOR SCIENTIFIC DISCOVERY 2005. [DOI: 10.1007/978-3-540-32263-4_2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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