E<scp>asy</scp>MIF<scp>s</scp> and S<scp>ite</scp>H<scp>ound</scp>: a toolkit for the identification of ligand-binding sites in protein structures

Ghersi, Dario; Sánchez, Roberto

doi:10.1093/bioinformatics/btp562

Cited by 76 publications

(69 citation statements)

References 15 publications

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“…The ligand binding sites were predicted based on the energy of interaction between the target and 18 probes (Ghersi and Sanchez, 2009). The molecular interaction fields were calculated using EasyMIFs.…”

Section: Prediction Of Toxin Pockets and Ligand Binding Sitesmentioning

confidence: 99%

“…The residues which showed binding to a large number of probes (9 or above) were identified as ligand binding sites. The 18 probes used in the study for predicting ligand binding sites were: aliphatic probes (CH1, CH2, CH3), methane (CH4), methyl carbon (CMET), aromatic carbon (CR1), sulphur (S), chlorine (CL), oxygen (OMET), silicon (SI), phosphate oxygen (OP), carbonyl (O), carboxyl (OM), hydroxyl (OA), water (OW), peptide nitrogen (N), arginine nitrogen (NE), aromatic nitrogen (NR) (Ghersi and Sanchez, 2009). …”

Section: Prediction Of Toxin Pockets and Ligand Binding Sitesmentioning

confidence: 99%

“…In this study, we used an energy-based approach for calculation of ligand binding sites, based on the affinities of the target for different chemical probes (Ghersi and Sanchez, 2009). Computational solvent mapping with organic solvents was employed to identify probable "druggable" regions in the toxin.…”

Section: Introductionmentioning

confidence: 99%

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Identification of inhibitors against the potential ligandable sites in the active cholera toxin

Gangopadhyay

Datta²

2015

Computational Biology and Chemistry

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Section: Prediction Of Toxin Pockets and Ligand Binding Sitesmentioning

confidence: 99%

Section: Prediction Of Toxin Pockets and Ligand Binding Sitesmentioning

confidence: 99%

See 1 more Smart Citation

Identification of inhibitors against the potential ligandable sites in the active cholera toxin

Gangopadhyay

Datta²

2015

Computational Biology and Chemistry

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“…Usually, binding sites are pockets or cavities on the protein's surface. Prediction of these binding sites has its applications in drug discovery as well as in functional annotation of proteins [1].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Structure-based Tools for the Prediction of Ligand Binding Site Residues in Apo-structures

Ezzat

Kwoh

2012

Procedia Computer Science

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In this paper, a group of structure-based algorithms used in the prediction of ligand binding sites in proteins were tested on apo-structures, and their results are analyzed and discussed. In particular, we focused on the accuracy of predicting the residues at the binding sites as the main criterion for comparison. The data set used for the testing was compiled using the LigASite database and consisted entirely of apo-structures. The tools compared are LIGSITE, SURFNET, DEPTH, GHECOM, SiteHound, and Fpocket.

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“…Most of these methods for predicting protein interaction sites make use of the data available in the Protein Data Bank (PDB) of ligands bound to proteins and are generally based on homologues likely to have similar binding sites. Methods that have been developed for the prediction of ligand binding sites include grid based, sphere based, a-shape based and energy based methods [13,16,22,27] . Challenges of computational predictions of protein interface binding sites are to improve the prediction accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

Predicting protein-protein interactions using full Bayesian network

Liu

Burge

et al. 2012

2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops

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Protein-protein interactions (PPIs) are central to the most cellular processes. Although PPIs have been generated exponentially from experimental methods ranging from high throughput protein sequences to the crystallized structures of complexes, only a fraction of interactions have been identified. It's challenging to integrate diverse datasets for computational methods. In order to predict PPIs over diverse datasets, we proposed a full Bayesian network model. First, we investigated the dihedral angle of atom C-alpha to describe flexible and rigid regions of protein structures and then design domain-domain interaction (DDI) template library to predict DDIs by the dihedral angle of atom C-alpha. Hence, both of them are viewed as the features of a full Bayesian Network (BN). Second, we used two encoding methods on sequences. The two encoding sequences can reflect both biological and physiochemical properties of proteins. Third, we also viewed gene co-expression as a feature of the BN model. Finally, we used receiver operating characteristic (ROC) to assess the performance compared to the Support Vector Machine (SVM) model. Keywords-protein protein interactions; protein sequence;protien structure;bayesian network I.

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EasyMIFs and SiteHound: a toolkit for the identification of ligand-binding sites in protein structures

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 76 publications

References 15 publications

Identification of inhibitors against the potential ligandable sites in the active cholera toxin

Identification of inhibitors against the potential ligandable sites in the active cholera toxin

Comparison of Structure-based Tools for the Prediction of Ligand Binding Site Residues in Apo-structures

Predicting protein-protein interactions using full Bayesian network

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