DCCP and DICP: Construction and Analyses of Databases for Copper- and Iron-Chelating Proteins

Wu, Hao; Yang, Yan; Jiang, Shan; Chen, Ling Ling; Gao, Hai Xia; Fu, Qing; Li, Feng; Guang, Bin; Zhang, Hong Yu

doi:10.1016/s1672-0229(05)03008-1

“…The presence of the metal ion allows metalloenzymes to perform functions such as redox reactions that cannot be performed by the limited set of functional groups found in amino acids [1]. Metalloproteins play important roles in structural stability and complex formation [4][5][6][7][8], gene expression regulation and alteration [9][10][11][12], DNA processing [13], signaling processes and cellular event [14], transport [11,15,16], metabolism control [15,17], antibody recognition [18] and other biological processes such as cellular respiration, photosynthesis, nitrogen fixation and antioxidant defense [19]. Approximately, 1/3 of structurally-determined proteins are metalloproteins [20].…”

Section: Introductionmentioning

confidence: 99%

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

Naik¹,

Ranjan²,

Kesari³

et al. 2011

JBPC

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Given a protein sequence, how can we identify whether it is a metalloprotein or not? If it is, which main functional class and subclasses it belongs to? This is an important biological question because they are closely related to the biological function of an uncharacterized protein. Particularly, with the avalanche of protein sequences generated in the post genomic era and since conventional techniques are time consuming and expensive, it is highly desirable to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called MetalloPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1 st layer of the prediction engine is for identifying a query protein as metalloprotein or not; the 2 nd layer for the main functional class; and the 3 rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 60% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. MetalloPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of metal binding proteins. MetalloPred is freely available to be used in-house as a standalone and is accessible at http://www.juit.ac.in/assets/Metallopred/.

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“…The presence of the metal ion allows metalloenzymes to perform functions such as redox reactions that cannot be performed by the limited set of functional groups found in amino acids [1]. Metalloproteins play important roles in structural stability and complex formation [4][5][6][7][8], gene expression regulation and alteration [9][10][11][12], DNA processing [13], signaling processes and cellular event [14], transport [11,15,16], metabolism control [15,17], antibody recognition [18] and other biological processes such as cellular respiration, photosynthesis, nitrogen fixation and antioxidant defense [19]. Approximately, 1/3 of structurally-determined proteins are metalloproteins [20].…”

Section: Introductionmentioning

confidence: 99%

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

Naik¹

2011

JBPC

View full text Add to dashboard Cite

Given a protein sequence, how can we identify whether it is a metalloprotein or not? If it is, which main functional class and subclasses it belongs to? This is an important biological question because they are closely related to the biological function of an uncharacterized protein. Particularly, with the avalanche of protein sequences generated in the post genomic era and since conventional techniques are time consuming and expensive, it is highly desirable to develop an automated method by which one can get a fast and accurate answer to these questions. Here, a top-down predictor, called MetalloPred, is developed which consists of 3 level of hierarchical classification using cascade of neural networks from sequence derived features. The 1 st layer of the prediction engine is for identifying a query protein as metalloprotein or not; the 2 nd layer for the main functional class; and the 3 rd layer for the sub-functional class. The overall success rates for all the three layers are higher than 60% that were obtained through rigorous cross-validation tests on the very stringent benchmark datasets in which none of the proteins has 30% sequence identity with any other in the same class or subclass. MetalloPred achieved good prediction accuracies and could nicely complement experimental approaches for identification of metal binding proteins. MetalloPred is freely available to be used in-house as a standalone and is accessible at http://www.juit.ac.in/assets/Metallopred/.

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Potential application of a knowledgebase of iron metabolism of Acidithiobacillus ferrooxidans as an alternative platform

Zhou

¹

,

Ma

²

,

Liu

³

et al. 2021

Electronic Journal of Biotechnology

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DCCP and DICP: Construction and Analyses of Databases for Copper- and Iron-Chelating Proteins

Cited by 4 publications

References 20 publications

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

MetalloPred: A tool for hierarchical prediction of metal ion binding proteins using cluster of neural networks and sequence derived features

Potential application of a knowledgebase of iron metabolism of Acidithiobacillus ferrooxidans as an alternative platform

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