The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility

Hu, Xiuzhen; Feng, Zhenxing; Zhang, Xiaojin; Liu, Liu; Wang, Shan

doi:10.3389/fgene.2020.00214

Cited by 6 publications

(12 citation statements)

References 45 publications

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“…In order to further improve the prediction accuracy, we optimized the four parameters (e.g., n.trees, interaction.depth, shrinkage, and n.minobsinnode) in the GBM algorithm. According to the reported literature ( Rawi et al, 2017 ; Hu et al, 2020 ), the parameter range was set as follows: n.trees in n{100,150,200,250,300,350,400,450,500}, interaction.depth in d{3,5,7,9}, shrinkage in r{0.01,0.1}, and n.minobsinnode in m{10,20,30,40,50}. The AUROC values were used as the evaluation indicator to obtain the optimal algorithm parameters by the grid search method.…”

Section: Calculation Results and Discussionmentioning

confidence: 99%

“…As an improved Boosting algorithm, GBM algorithm was proposed by Friedman (2001 ). It achieved excellent results in many data mining competitions and was widely used in many fields ( Feng and Li, 2017 ; Rawi et al, 2017 ; Hu et al, 2020 ). The advantage of the GBM is that it inherits the advantages of a single decision tree and discards its shortcomings.…”

Section: Methodsmentioning

confidence: 99%

“…This article used the “gbm” package in R software version 3.6.3. Here, in the algorithm, we mainly optimized the four adjustable parameters (i.e., n.trees, interaction.depth, shrinkage, and n.minobsinnode) ( Rawi et al, 2017 ; Hu et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

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Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

Feng

et al. 2022

Front. Genet.

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The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

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Section: Calculation Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

Feng

et al. 2022

Front. Genet.

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“…According to our survey, 12 out of 23 sequence-based and 6 out of 9 structure-based methods filter the candidate complexes using high resolution with ≤3 Å. Some methods [ 50 – 57 ] remove the sequences/chains whose lengths are less than 50 residues (or 45 residues [ 58 ]) since they might be potential segments or peptides. To build an unbiased dataset, it is necessary to remove homologous or redundant proteins.…”

Section: Methods Development Of Metal-binding Predictionmentioning

confidence: 99%

“…(2) Evolutionary Profile-Based Features . Recent studies [ 54 , 56 , 57 , 80 , 81 ] pointed out that functional or structural important residues tend to show higher evolutionary conservation. The conserved residues are usually involved in enzyme activity, ligand binding, or protein structural stability [ 82 ].…”

Section: Methods Development Of Metal-binding Predictionmentioning

confidence: 99%

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Zhou

Liang

et al. 2022

BioMed Research International

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Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

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Bioinformatics and computational analyses of kidney stone modulatory proteins lead to solid experimental evidence and therapeutic potential

Yoodee¹,

Thongboonkerd²

2023

Biomedicine & Pharmacotherapy

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The Identification of Metal Ion Ligand-Binding Residues by Adding the Reclassified Relative Solvent Accessibility

Cited by 6 publications

References 45 publications

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Bioinformatics and computational analyses of kidney stone modulatory proteins lead to solid experimental evidence and therapeutic potential

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