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Meher PK, Sahu TK, Gupta A, Kumar A, Rustgi S. ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants. THE PLANT GENOME 2024; 17:e20259. [PMID: 36098562 DOI: 10.1002/tpg2.20259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/10/2022] [Indexed: 06/15/2023]
Abstract
One of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress-responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K-mer compositional features were used as input. With ACC, K-mer, and ACC + K-mer compositional features, the overall accuracy of ∼60-77, ∼75-86, and ∼61-78% were respectively obtained using the SVM algorithm with fivefold cross-validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross-validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri-sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins.
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Affiliation(s)
| | | | - Ajit Gupta
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- Dep. of Microbiology and Immunology, Dalhousie Univ., Halifax, Nova Scotia, Canada
- Laboratory of Immunity, Shantou Univ. Medical College, Shantou, PRC
| | - Sachin Rustgi
- Dep. of Plant and Environmental Sciences, Pee Dee Research and Education Centre, Clemson Univ., Florence, SC, USA
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Zhang D, Guan ZX, Zhang ZM, Li SH, Dao FY, Tang H, Lin H. Recent Development of Computational Predicting Bioluminescent Proteins. Curr Pharm Des 2020; 25:4264-4273. [PMID: 31696804 DOI: 10.2174/1381612825666191107100758] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/04/2019] [Indexed: 12/22/2022]
Abstract
Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.
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Affiliation(s)
- Dan Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zheng-Xing Guan
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zi-Mei Zhang
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Shi-Hao Li
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Fu-Ying Dao
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hua Tang
- Department of Pathophysiology, Southwest Medical University, Luzhou 646000, China
| | - Hao Lin
- Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Wang L, You ZH, Huang YA, Huang DS, Chan KCC. An efficient approach based on multi-sources information to predict circRNA–disease associations using deep convolutional neural network. Bioinformatics 2019; 36:4038-4046. [DOI: 10.1093/bioinformatics/btz825] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 10/07/2019] [Accepted: 11/21/2019] [Indexed: 12/16/2022] Open
Abstract
Abstract
Motivation
Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. However, detection of circRNA–disease associations by biological experiments alone is often blind, limited to small scale, high cost and time consuming. Therefore, there is an urgent need for reliable computational methods to rapidly infer the potential circRNA–disease associations on a large scale and to provide the most promising candidates for biological experiments.
Results
In this article, we propose an efficient computational method based on multi-source information combined with deep convolutional neural network (CNN) to predict circRNA–disease associations. The method first fuses multi-source information including disease semantic similarity, disease Gaussian interaction profile kernel similarity and circRNA Gaussian interaction profile kernel similarity, and then extracts its hidden deep feature through the CNN and finally sends them to the extreme learning machine classifier for prediction. The 5-fold cross-validation results show that the proposed method achieves 87.21% prediction accuracy with 88.50% sensitivity at the area under the curve of 86.67% on the CIRCR2Disease dataset. In comparison with the state-of-the-art SVM classifier and other feature extraction methods on the same dataset, the proposed model achieves the best results. In addition, we also obtained experimental support for prediction results by searching published literature. As a result, 7 of the top 15 circRNA–disease pairs with the highest scores were confirmed by literature. These results demonstrate that the proposed model is a suitable method for predicting circRNA–disease associations and can provide reliable candidates for biological experiments.
Availability and implementation
The source code and datasets explored in this work are available at https://github.com/look0012/circRNA-Disease-association.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lei Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
| | - Keith C C Chan
- Department of Computing, Hong Kong Polytechnic University, Hong Kong 999077, China
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Khan S, Khan M, Iqbal N, Hussain T, Khan SA, Chou KC. A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule. Int J Pept Res Ther 2019. [DOI: 10.1007/s10989-019-09887-3] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Zhang J, Liu B. A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods. Curr Bioinform 2019. [DOI: 10.2174/1574893614666181212102749] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Proteins play a crucial role in life activities, such as catalyzing metabolic reactions, DNA replication, responding to stimuli, etc. Identification of protein structures and functions are critical for both basic research and applications. Because the traditional experiments for studying the structures and functions of proteins are expensive and time consuming, computational approaches are highly desired. In key for computational methods is how to efficiently extract the features from the protein sequences. During the last decade, many powerful feature extraction algorithms have been proposed, significantly promoting the development of the studies of protein structures and functions.Objective:To help the researchers to catch up the recent developments in this important field, in this study, an updated review is given, focusing on the sequence-based feature extractions of protein sequences.Method:These sequence-based features of proteins were grouped into three categories, including composition-based features, autocorrelation-based features and profile-based features. The detailed information of features in each group was introduced, and their advantages and disadvantages were discussed. Besides, some useful tools for generating these features will also be introduced.Results:Generally, autocorrelation-based features outperform composition-based features, and profile-based features outperform autocorrelation-based features. The reason is that profile-based features consider the evolutionary information, which is useful for identification of protein structures and functions. However, profile-based features are more time consuming, because the multiple sequence alignment process is required.Conclusion:In this study, some recently proposed sequence-based features were introduced and discussed, such as basic k-mers, PseAAC, auto-cross covariance, top-n-gram etc. These features did make great contributions to the developments of protein sequence analysis. Future studies can be focus on exploring the combinations of these features. Besides, techniques from other fields, such as signal processing, natural language process (NLP), image processing etc., would also contribute to this important field, because natural languages (such as English) and protein sequences share some similarities. Therefore, the proteins can be treated as documents, and the features, such as k-mers, top-n-grams, motifs, can be treated as the words in the languages. Techniques from these filed will give some new ideas and strategies for extracting the features from proteins.
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Affiliation(s)
- Jun Zhang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town, Xili, Shenzhen, Guangdong 518055, China
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Tian B, Wu X, Chen C, Qiu W, Ma Q, Yu B. Predicting protein–protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach. J Theor Biol 2019; 462:329-346. [DOI: 10.1016/j.jtbi.2018.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 11/08/2018] [Accepted: 11/15/2018] [Indexed: 12/26/2022]
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An JY, Zhou Y, Zhang L, Niu Q, Wang DF. Improving Self-interacting Proteins Prediction Accuracy Using Protein Evolutionary Information and Weighed-Extreme Learning Machine. Curr Bioinform 2019. [DOI: 10.2174/1574893613666180209161152] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Background:
Self Interacting Proteins (SIPs) play an essential role in various aspects of the
structural and functional organization of the cell.
Objective:
In the study, we presented a novelty sequence-based computational approach for predicting
Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an
Autocorrelation (AC) descriptor protein feature representation.
Method:
The major advantage of the proposed method mainly lies in adopting an effective feature
extraction method to represent candidate self-interacting proteins by using the evolutionary information
embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a
reliable and effective WELM classifier to perform classify.
</P><P>
Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP
datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction
accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare
our approach with the SVM classifier and existing sequence-based method on yeast and human dataset.
Experimental results show that the performance of our method is better than several other state-of-theart
methods.
Conclusion:
It is demonstrated that the proposed method is suitable for SIPs detection and can execute
incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics
research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor
(PHP) for predicting SIPs. The web server including source code and the datasets are available at
http://219.219.62.123:8888/WELMAC/.
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Affiliation(s)
- Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, China
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, China
| | - Lei Zhang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, China
| | - Qiang Niu
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, China
| | - Da-Fu Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou Jiangsu 21116, China
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Uddin MR, Sharma A, Farid DM, Rahman MM, Dehzangi A, Shatabda S. EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features. J Theor Biol 2018; 443:138-146. [DOI: 10.1016/j.jtbi.2018.02.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Revised: 01/18/2018] [Accepted: 02/03/2018] [Indexed: 12/21/2022]
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Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4563524. [PMID: 27437399 PMCID: PMC4942601 DOI: 10.1155/2016/4563524] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2016] [Accepted: 05/08/2016] [Indexed: 11/17/2022]
Abstract
Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.
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Feng Z, Hu X, Jiang Z, Song H, Ashraf MA. The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements. Saudi J Biol Sci 2016; 23:189-97. [PMID: 26980999 PMCID: PMC4778582 DOI: 10.1016/j.sjbs.2015.10.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2015] [Revised: 10/08/2015] [Accepted: 10/12/2015] [Indexed: 11/28/2022] Open
Abstract
The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.
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Affiliation(s)
- Zhenxing Feng
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Xiuzhen Hu
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Zhuo Jiang
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Hangyu Song
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Muhammad Aqeel Ashraf
- Water Research Unit, Faculty of Science and Natural Resources, University Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
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12
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Li X, Liu T, Tao P, Wang C, Chen L. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination. Comput Biol Chem 2015; 59 Pt A:95-100. [DOI: 10.1016/j.compbiolchem.2015.08.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 08/30/2015] [Accepted: 08/30/2015] [Indexed: 12/11/2022]
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13
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Feng Z, Hu X. Recognition of 27-class protein folds by adding the interaction of segments and motif information. BIOMED RESEARCH INTERNATIONAL 2014; 2014:262850. [PMID: 25136571 PMCID: PMC4127253 DOI: 10.1155/2014/262850] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 06/28/2014] [Indexed: 01/31/2023]
Abstract
The recognition of protein folds is an important step for the prediction of protein structure and function. After the recognition of 27-class protein folds in 2001 by Ding and Dubchak, prediction algorithms, prediction parameters, and new datasets for the prediction of protein folds have been improved. However, the influences of interactions from predicted secondary structure segments and motif information on protein folding have not been considered. Therefore, the recognition of 27-class protein folds with the interaction of segments and motif information is very important. Based on the 27-class folds dataset built by Liu et al., amino acid composition, the interactions of secondary structure segments, motif frequency, and predicted secondary structure information were extracted. Using the Random Forest algorithm and the ensemble classification strategy, 27-class protein folds and corresponding structural classification were identified by independent test. The overall accuracy of the testing set and structural classification measured up to 78.38% and 92.55%, respectively. When the training set and testing set were combined, the overall accuracy by 5-fold cross validation was 81.16%. In order to compare with the results of previous researchers, the method above was tested on Ding and Dubchak's dataset which has been widely used by many previous researchers, and an improved overall accuracy 70.24% was obtained.
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Affiliation(s)
- Zhenxing Feng
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
| | - Xiuzhen Hu
- Department of Sciences, Inner Mongolia University of Technology, Hohhot, China
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14
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Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes. PLoS One 2014; 9:e86703. [PMID: 24475169 PMCID: PMC3901691 DOI: 10.1371/journal.pone.0086703] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2013] [Accepted: 12/10/2013] [Indexed: 11/22/2022] Open
Abstract
Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader) were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that the proposed DBPPred can be an alternative perspective predictor for large-scale determination of DNA-binding proteins.
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Wright DB, Tripathi S, Sikarwar A, Santosh KT, Perez-Zoghbi J, Ojo OO, Irechukwu N, Ward JPT, Schaafsma D. Regulation of GPCR-mediated smooth muscle contraction: implications for asthma and pulmonary hypertension. Pulm Pharmacol Ther 2012; 26:121-31. [PMID: 22750270 DOI: 10.1016/j.pupt.2012.06.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2012] [Revised: 06/15/2012] [Accepted: 06/18/2012] [Indexed: 11/28/2022]
Abstract
Contractile G-protein-coupled receptors (GPCRs) have emerged as key regulators of smooth muscle contraction, both under healthy and diseased conditions. This brief review will discuss some key topics and novel insights regarding GPCR-mediated airway and vascular smooth muscle contraction as discussed at the 7th International Young Investigators' Symposium on Smooth Muscle (2011, Winnipeg, Manitoba, Canada) and will in particular focus on processes driving Ca(2+)-mobilization and -sensitization.
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Affiliation(s)
- D B Wright
- Department of Asthma, Allergy, and Lung Biology, King's College, London, United Kingdom
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Zhao X, Li J, Huang Y, Ma Z, Yin M. Prediction of bioluminescent proteins using auto covariance transformation of evolutional profiles. Int J Mol Sci 2012; 13:3650-3660. [PMID: 22489173 PMCID: PMC3317733 DOI: 10.3390/ijms13033650] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2012] [Revised: 02/21/2012] [Accepted: 03/05/2012] [Indexed: 12/21/2022] Open
Abstract
Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins' functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available.
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Affiliation(s)
- Xiaowei Zhao
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China; E-Mails: (X.Z.); (J.L.)
- School of Life Sciences, Northeast Normal University, Changchun 130024, China
| | - Jiakui Li
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China; E-Mails: (X.Z.); (J.L.)
| | - Yanxin Huang
- National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China
| | - Zhiqiang Ma
- School of Life Sciences, Northeast Normal University, Changchun 130024, China
| | - Minghao Yin
- School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China; E-Mails: (X.Z.); (J.L.)
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Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory. Amino Acids 2011; 43:793-804. [PMID: 22086210 DOI: 10.1007/s00726-011-1133-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2011] [Accepted: 10/20/2011] [Indexed: 10/15/2022]
Abstract
The coupling between G protein-coupled receptors (GPCRs) and guanine nucleotide-binding proteins (G proteins) regulates various signal transductions from extracellular space into the cell. However, the coupling mechanism between GPCRs and G proteins is still unknown, and experimental determination of their coupling specificity and function is both expensive and time consuming. Therefore, it is significant to develop a theoretical method to predict the coupling specificity between GPCRs and G proteins as well as their function using their primary sequences. In this study, a novel four-layer predictor (GPCRsG_CWTIT) based on support vector machine (SVM), continuous wavelet transform (CWT) and information theory (IT) is developed to classify G proteins and predict the coupling specificity between GPCRs and G proteins. SVM is used for construction of models. CWT and IT are used to characterize the primary structure of protein. Performance of GPCRsG_CWTIT is evaluated with cross-validation test on various working dataset. The overall accuracy of the G proteins at the levels of class and family is 98.23 and 85.42%, respectively. The accuracy of the coupling specificity prediction varies from 74.60 to 94.30%. These results indicate that the proposed predictor is an effective and feasible tool to predict the coupling specificity between GPCRs and G proteins as well as their functions using only the protein full sequence. The establishment of such an accurate prediction method will facilitate drug discovery by improving the ability to identify and predict protein-protein interactions. GPCRsG_CWTIT and dataset can be acquired freely on request from the authors.
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Liu X, Zhao L, Dong Q. Protein remote homology detection based on auto-cross covariance transformation. Comput Biol Med 2011; 41:640-7. [DOI: 10.1016/j.compbiomed.2011.05.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2010] [Revised: 05/03/2011] [Accepted: 05/24/2011] [Indexed: 11/26/2022]
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19
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Liu T, Geng X, Zheng X, Li R, Wang J. Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles. Amino Acids 2011; 42:2243-9. [PMID: 21698456 DOI: 10.1007/s00726-011-0964-5] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2011] [Accepted: 06/11/2011] [Indexed: 02/07/2023]
Abstract
Computational prediction of protein structural class based solely on sequence data remains a challenging problem in protein science. Existing methods differ in the protein sequence representation models and prediction engines adopted. In this study, a powerful feature extraction method, which combines position-specific score matrix (PSSM) with auto covariance (AC) transformation, is introduced. Thus, a sample protein is represented by a series of discrete components, which could partially incorporate the long-range sequence order information and evolutionary information reflected from the PSI-BLAST profile. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides the state-of-the-art performance for structural class prediction. A Web server that implements the proposed method is freely available at http://202.194.133.5/xinxi/AAC_PSSM_AC/index.htm.
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Affiliation(s)
- Taigang Liu
- College of Information Sciences and Engineering, Shandong Agricultural University, Taian, 271018, China
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20
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Yellow submarine of the Wnt/Frizzled signaling: submerging from the G protein harbor to the targets. Biochem Pharmacol 2011; 82:1311-9. [PMID: 21689640 DOI: 10.1016/j.bcp.2011.06.005] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2011] [Revised: 05/30/2011] [Accepted: 06/02/2011] [Indexed: 10/18/2022]
Abstract
The Wnt/Frizzled signaling pathway plays multiple functions in animal development and, when deregulated, in human disease. The G-protein coupled receptor (GPCR) Frizzled and its cognate heterotrimeric Gi/o proteins initiate the intracellular signaling cascades resulting in cell fate determination and polarization. In this review, we summarize the knowledge on the ligand recognition, biochemistry, modifications and interacting partners of the Frizzled proteins viewed as GPCRs. We also discuss the effectors of the heterotrimeric Go protein in Frizzled signaling. One group of these effectors is represented by small GTPases of the Rab family, which amplify the initial Wnt/Frizzled signal. Another effector is the negative regulator of Wnt signaling Axin, which becomes deactivated in response to Go action. The discovery of the GPCR properties of Frizzled receptors not only provides mechanistic understanding to their signaling pathways, but also paves new avenues for the drug discovery efforts.
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21
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Yu X, Zheng X, Liu T, Dou Y, Wang J. Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids 2011; 42:1619-25. [DOI: 10.1007/s00726-011-0848-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2010] [Accepted: 02/09/2011] [Indexed: 12/13/2022]
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22
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Guang X, Guo Y, Xiao J, Wang X, Sun J, Xiong W, Li M. Predicting the state of cysteines based on sequence information. J Theor Biol 2010; 267:312-8. [DOI: 10.1016/j.jtbi.2010.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2010] [Revised: 08/16/2010] [Accepted: 09/01/2010] [Indexed: 10/19/2022]
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23
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Prediction of neurotoxins by support vector machine based on multiple feature vectors. Interdiscip Sci 2010; 2:241-6. [PMID: 20658336 DOI: 10.1007/s12539-010-0044-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2010] [Revised: 03/27/2010] [Accepted: 03/29/2010] [Indexed: 10/19/2022]
Abstract
Neurotoxin is a toxin which acts on nerve cells by interacting with membrane proteins. Different neurotoxins have different functions and sources. With much more knowledge of neurotoxins it would be greatly helpful for the development of drug design. The support vector machine (SVM) was used to predict the neurotoxin based on multiple feature vector descriptors, including the amino acid composition, length of the protein sequence, weight of the protein and the evolution information described by position specific scoring matrix (PSSM). After a five-fold cross-validation procedure, the method achieved an accuracy of 100% in discriminating neurotoxins from non-toxins. As for classifying neurotoxins based on their sources and functions, the accuracy was 99.50% and 99.38% respectively. At last, the method yielded a good performance in sub-classification of ion channels inhibitors with the total accuracy of 87.27%. These results indicate that this method outperforms previously described NTXpred method.
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24
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Wu J, Li YZ, Li ML, Yu LZ. Two multi-classification strategies used on SVM to predict protein structural classes by using auto covariance. Interdiscip Sci 2010; 1:315-9. [PMID: 20640811 DOI: 10.1007/s12539-009-0066-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2009] [Revised: 09/01/2009] [Accepted: 09/04/2009] [Indexed: 11/24/2022]
Abstract
Machine learning methods play the very important role in protein secondary structure prediction and other related works. On condition of a certain approach, the prediction qualities mostly depend on the ways of representing protein sequences into numeric features. In this paper, two Support Vector Machine (SVM) multi-classification strategies, "one-against-one" (1-a-1) and "one-against-all" (1-a-a), were used in protein structural classes identification. Auto covariance (AC), which transforms the physicochemical properties of the amino acids of the proteins into a data matrix, focuses on the neighboring effects and the interactions between residues in protein sequences. "1-a-1" approach was used on SVM to predict protein structural classes and obtained very promising overall accuracy 90.69% by Jackknife test. It was more than 10% higher than the accuracy obtained by using "1-a-a". Experimental results led to the finding that the SVM predictor constructed by "1-a-1" can avoid the appearance of biased prediction accuracy. This current method, using the protein primary sequence information described by auto covariance (AC) and "1-a-1" approach on SVM, should play an important complementary role in other related applications.
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Affiliation(s)
- Jiang Wu
- College of Chemistry, Sichuan University, Chengdu, China
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25
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Yu L, Guo Y, Zhang Z, Li Y, Li M, Li G, Xiong W, Zeng Y. SecretP: a new method for predicting mammalian secreted proteins. Peptides 2010; 31:574-8. [PMID: 20045033 DOI: 10.1016/j.peptides.2009.12.026] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2009] [Revised: 12/17/2009] [Accepted: 12/17/2009] [Indexed: 11/19/2022]
Abstract
In contrast to a large number of classically secreted proteins (CSPs) and non-secreted proteins (NSPs), only a few proteins have been experimentally proved to enter non-classical secretory pathways. So it is difficult to identify non-classically secreted proteins (NCSPs), and no methods are available for distinguishing the three types of proteins simultaneously. In order to solve this problem, a data mining has been taken firstly, and mammalian proteins exported via ER-Golgi-independent pathways are collected through extensive literature searches. In this paper, a support vector machine (SVM)-based ternary classifier named SecretP is proposed to predict mammalian secreted proteins by using pseudo-amino acid composition (PseAA) and five additional features. When distinguishing the three types of proteins, SecretP yielded an accuracy of 88.79%. Evaluating the performance of our method by an independent test set of 92 human proteins, 76 of them are correctly predicted as NCSPs. When performed on another public independent data set, the prediction result of SecretP is comparable to those of other existing computational methods. Therefore, SecretP can be a useful supplementary tool for future secretome studies. The web server SecretP and all supplementary tables listed in this paper are freely available at http://cic.scu.edu.cn/bioinformatics/secretp/index.htm.
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Affiliation(s)
- Lezheng Yu
- College of Chemistry, Sichuan University, Chengdu 610064, PR China
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26
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Wu J, Li ML, Yu LZ, Wang C. An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition. Protein J 2010; 29:62-7. [PMID: 20049515 DOI: 10.1007/s10930-009-9222-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The purpose of this article is to identify protein structural classes by using support vector machine (SVM) ensemble classifier, which is very efficient in enhancing prediction performance. Firstly, auto covariance (AC) and pseudo-amino acid composition (PseAAC) were used in protein representation. AC focuses on adjacent effects and PseAA composition takes sequence order patterns into account. Secondly, SVMs were trained on the datasets represented by different descriptors. The last, ensemble classifier, which constructed on the individual classifiers through a voting strategy, gave the final prediction results. Meanwhile, very promising prediction accuracy 93.14% was obtained by Jackknife test. The experimental results showed that the ensemble system can improve the prediction performance greatly and generate more stable and safer predictors. The current method featured by fusing the protein primary sequence information transferred by AC and described by protein PseAA composition may play an important complementary role in other related applications.
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Affiliation(s)
- Jiang Wu
- College of Chemistry, Sichuan University, 610064, Chengdu, China
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27
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Using auto covariance method for functional discrimination of membrane proteins based on evolution information. Amino Acids 2009; 38:1497-503. [DOI: 10.1007/s00726-009-0362-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2009] [Accepted: 09/24/2009] [Indexed: 11/29/2022]
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28
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Abstract
Beta-turn is a secondary protein structure type that plays an important role in protein configuration and function. Here, we introduced an approach of beta-turn prediction that used the support vector machine (SVM) algorithm combined with predicted secondary structure information. The secondary structure information was obtained by using E-SSpred, a new secondary protein structure prediction method. A 7-fold cross validation based on the benchmark dataset of 426 non-homologous protein chains was used to evaluate the performance of our method. The prediction results broke the 80% Q (total) barrier and achieved Q (total) = 80.9%, MCC = 0.44, and Q (predicted) higher 0.9% when compared with the best method. The results in our research are coincident with the conclusion that beta-turn prediction accuracy can be improved by inclusion of secondary structure information.
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29
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Dong Q, Zhou S, Guan J. A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation. Bioinformatics 2009; 25:2655-62. [DOI: 10.1093/bioinformatics/btp500] [Citation(s) in RCA: 150] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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30
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Zeng YH, Guo YZ, Xiao RQ, Yang L, Yu LZ, Li ML. Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach. J Theor Biol 2009; 259:366-72. [PMID: 19341746 DOI: 10.1016/j.jtbi.2009.03.028] [Citation(s) in RCA: 139] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2008] [Revised: 02/25/2009] [Accepted: 03/13/2009] [Indexed: 12/20/2022]
Abstract
The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane. The model fully considers the sequence-order effects between residues a certain distance apart in the sequence by AC combined with eight representative descriptors for both common proteins and membrane proteins. As a result of jackknife cross-validation tests, the method for submitochondria location prediction yields the accuracies of 91.8%, 96.4% and 66.1% for inner membrane, matrix, and outer membrane, respectively. The total accuracy is 89.7%. When predicting membrane protein types in mitochondria inner membrane, the method achieves the prediction performance with the accuracies of 98.4%, 64.3% and 86.7% for multi-pass inner membrane, single-pass inner membrane, and matrix side inner membrane, where the total accuracy is 93.6%. The overall performance of our method is better than the achievements of the previous studies. So our method can be an effective supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://chemlab.scu.edu.cn/Predict_subMITO/index.htm.
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Affiliation(s)
- Yu-hong Zeng
- College of Chemistry, Sichuan University, Chengdu 610064, PR China.
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31
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Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res 2008; 36:3025-30. [PMID: 18390576 PMCID: PMC2396404 DOI: 10.1093/nar/gkn159] [Citation(s) in RCA: 391] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Compared to the available protein sequences of different organisms, the number of revealed protein–protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www.scucic.cn/Predict_PPI/index.htm.
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Affiliation(s)
- Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064 and State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P.R. China
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32
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Fang Y, Guo Y, Feng Y, Li M. Predicting DNA-binding proteins: approached from Chou's pseudo amino acid composition and other specific sequence features. Amino Acids 2007; 34:103-9. [PMID: 17624492 DOI: 10.1007/s00726-007-0568-2] [Citation(s) in RCA: 116] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2007] [Accepted: 05/23/2007] [Indexed: 11/25/2022]
Abstract
DNA-binding proteins play a pivotal role in gene regulation. It is vitally important to develop an automated and efficient method for timely identification of novel DNA-binding proteins. In this study, we proposed a method based on alone the primary sequences of proteins to predict the DNA-binding proteins. DNA-binding proteins were encoded by autocross-covariance transform, pseudo-amino acid composition, dipeptide composition, respectively and also the different combinations of the three encoded methods; further, these feature matrices were applied to support vector machine classifiers to predict the DNA-binding proteins. All modules were trained and validated by the jackknife cross-validation test. Through comparing the performance of these substituted modules, the best result was obtained from pseudo-amino acid composition with the overall accuracy of 96.6% and the sensitivity of 90.7%. The results suggest that it can efficiently predict the novel DNA-binding proteins only using the primary sequences.
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Affiliation(s)
- Y Fang
- College of Chemistry, Sichuan University, Chengdu, China
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