Identification of ATP binding residues of a protein from its primary sequence

Chauhan, Jagat; Mishra, Nitish K.; Raghava, Gajendra P. S.

doi:10.1186/1471-2105-10-434

Cited by 132 publications

(164 citation statements)

References 35 publications

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“…Thus, overlapping segments centered at the target residue were generated with different window sizes ranging from 5 to 21 for every Ca 2+ -binding protein sequence (Chauhan et al, 2009). If the central residue of the segment was a calcium binding residue, then the segment was assigned as binding segment, otherwise it was assigned as non-binding segment.…”

Section: Selection Of Feature Parametersmentioning

confidence: 99%

Identification of Ca2+-binding residues of a protein from its primary sequence

Jiang

Geriletu

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Calcium is one of the most abundant minerals in the human body, playing a critical role in many cellular activities by interacting with different calcium ion (Ca 2+ )-binding proteins. Therefore, the correct identification of Ca 2+ -binding residues is essential for protein functional research. In this study, a new method was developed to predict Ca 2+ -binding residues from the primary sequence without using three-dimensional information. Through statistical analysis, four kinds of feature parameters were extracted from amino acid sequences: the increment of diversity values of amino acid composition, the matrix scoring values of position conservation, the autocross covariance of physicochemical properties, and the center motif. These features served as input for a support vector machine to predict Ca 2+ -binding residues. This method was tested on four well-established datasets using a five-fold cross-validation. The accuracies and Matthews correlation coefficients were 75.9% and 0.53 (dataset 1), 79.2% and 0.58 (dataset 2), 77.4% and 0.55 (dataset 3), and 79.1% and 0.58 (dataset 4). Comparative results show that the developed method outperforms previous methods. Based on this study, a web server was developed for predicting Ca 2+ -binding

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Section: Selection Of Feature Parametersmentioning

confidence: 99%

Identification of Ca2+-binding residues of a protein from its primary sequence

Jiang

Geriletu

et al. 2016

Genet. Mol. Res.

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“…Because the segments of positive set are far less than negative sets and the proportion is 1:21. We referred to the identification of ligand binding residues [12][13]. Equal numbers of negative set were randomly select as an identification negative set.…”

Section: The Results Using 5-fold Cross-validations Testmentioning

confidence: 99%

“…Reference to the previous' research for ligand binding residues [12][13]. In this paper, we identified SIBRs also using "sliding window" method.…”

Section: The Statistical Analysis Of Sequence Segmentmentioning

confidence: 99%

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Identifying the Sulfate Ion Binding Residues in Proteins

Li¹,

Hu²,

Sun³

et al. 2017

Proceedings of the 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017)

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Abstract. Many proteins function execution depends on the process of protein and ligand interact with each other. The identification of ligand binding residues is important for the research of the protein function. The 4442 protein chains with <25% sequence identity and resolution <3.0 Å were analyzed using Ligand Protein Contact database. Our final dataset contained 8112 sulfate ion binding residues (SIBR). We did a statistical analysis on window size as 7 amino acids. Using the amino acid composition, hydropathy information, correlation information and predicted structure information as the characteristic parameter, a Support Vector Machine algorithm for identifying sulphate ion binding residues was proposed. The overall accuracy and Matthew's correlation coefficient achieved 78.5% and 0.571 using the 5 fold cross validation. The Acc and MCC achieved 72.7% and 0.455 by using independent test. In addition, an online web server was established. http: //202.207.30.72:7321/

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“…32 Inspired by ATPint, 32 we set W = 17 in this study. This causes the dimensionality of the PSSM-view feature to be 17 × 20 = 340.…”

Section: Methodsmentioning

confidence: 99%

ATPbind: Accurate Protein–ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons

Zhang

et al. 2018

J. Chem. Inf. Model.

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Protein–ATP interactions are ubiquitous in a wide variety of biological processes. Correctly locating ATP binding sites from protein information is an important but challenging task for protein function annotation and drug discovery. However, there is no method that can optimally identify ATP binding sites for different proteins. In this study, we report a new composite predictor, ATPbind, for ATP binding sites by integrating the outputs of two template-based predictors (i.e., S-SITE and TM-SITE) and three discriminative sequence-driven features of proteins: position specific scoring matrix, predicted secondary structure, and predicted solvent accessibility. In ATPbind, we assembled multiple support vector machines (SVMs) based on a random undersampling technique to cope with the serious imbalance phenomenon between the numbers of ATP binding sites and of non-ATP binding sites. We also constructed a new gold-standard benchmark data set consisting of 429 ATP binding proteins from the PDB database to evaluate and compare the proposed ATPbind with other existing predictors. Starting from a query sequence and predicted I-TASSER models, ATPbind can achieve an average accuracy of 72%, covering 62% of all ATP binding sites while achieving a Matthews correlation coefficient value that is significantly higher than that of other state-of-the-art predictors.

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Identification of ATP binding residues of a protein from its primary sequence

Cited by 132 publications

References 35 publications

Identification of Ca2+-binding residues of a protein from its primary sequence

Identification of Ca2+-binding residues of a protein from its primary sequence

Identifying the Sulfate Ion Binding Residues in Proteins

ATPbind: Accurate Protein–ATP Binding Site Prediction by Combining Sequence-Profiling and Structure-Based Comparisons

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