PocketPicker: analysis of ligand binding-sites with shape descriptors

Weisel, Martin; Proschak, Ewgenij; Schneider, Gisbert

doi:10.1186/1752-153x-1-7

Cited by 304 publications

(305 citation statements)

References 33 publications

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“…For structural comparison of the active sites, cavities located next to the catalytic serine residues were extracted using PocketPicker (12) and converted into PoLiMorph graph representation (13). Based on these graphs (pocket frameworks), a comparison of shapes and of the pharmacophoric feature distributions within (please see supplemental Experimental Procedures for edditional information) the binding sites of HtrA species was calculated.…”

Section: Methodsmentioning

confidence: 99%

Distinct Roles of Secreted HtrA Proteases from Gram-negative Pathogens in Cleaving the Junctional Protein and Tumor Suppressor E-cadherin

Hoy

Geppert

Boehm

et al. 2012

Journal of Biological Chemistry

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148

194

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Section: Methodsmentioning

confidence: 99%

Distinct Roles of Secreted HtrA Proteases from Gram-negative Pathogens in Cleaving the Junctional Protein and Tumor Suppressor E-cadherin

Hoy

Geppert

Boehm

et al. 2012

Journal of Biological Chemistry

Self Cite

148

194

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“…There are several groups of computational tools available for this task [29]. The first group involves setting up a three-dimensional grid of voxels and then scanning them to determine whether they are occupied by the protein atoms (represented by their, possibly scaled, van der Waals spheres) or not [30][31][32][33][34][35][36]. With these algorithms, it is necessary to start the counting inside the cavity or to introduce ways of closing it.…”

Section: Shape Characterizationmentioning

confidence: 99%

Structure‐based analysis of thermodynamic and mechanical properties of cavity‐containing proteins – case study of plant pathogenesis‐related proteins of class 10

2013

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We provide theoretical comparisons of the physical properties of eighteen proteins with the pathogenesis-related proteins of class 10 (PR-10) fold, which is characterized by a large hydrophobic cavity enclosed between a curved b-sheet and a variable a-helix. Our novel algorithm to calculate the volume of internal cavities within protein structures is used to demonstrate that, although the sizes of the cavities of the investigated PR-10 proteins vary significantly, their other physical properties, such as thermodynamic and mechanical parameters or parameters related to folding, are very close. The largest variations (in the order of 20%) are predicted for the optimal folding times. We show that, on squeezing, the PR-10 proteins behave differently from typical virus capsids.

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“…Most of these methods use geometric or physical features of the binding sites themselves to identify them [1]- [3]. On the other hand, as mentioned above, we focus not only on the structural similarity among proteins in the same group but on the structural dissimilarity between the different groups [4].…”

Section: Introductionmentioning

confidence: 99%

Binding Site Extraction by Detecting Optimal Graphs from Protein Molecular Surfaces

Mitsui¹,

Ohkawa²

2014

IJBBB

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Abstract-Proteins fulfill their functions by binding with molecular compounds called ligands. This research automatically extracts a binding site from the surface of a protein. A binding site candidate can be extracted as the local portion that satisfies the following two requirements. One is the structural similarity among proteins that bind the same kind of ligands. The other is the structural dissimilarity between a binding site in a protein and any local surfaces in the proteins that bind to any other ligands. By representing a protein molecular surface as a graph, the binding site extraction problem can be regarded as an optimal subgraph detection problem in which the best subgraph is extracted that satisfies the above requirements. However, if two ligands are different but have a partly similar structure, the binding sites of the proteins that bind these ligands often resemble each other. In such situations, an optimal graph may not present the binding site. Therefore, we introduce the concept of group integration, in which more than one group with similar ligands partners is regarded as a positive group. As a result of group integration, the number of proteins in a positive group and in a negative group is changed. Therefore, based on the distance from the virtual worst subgraph, an evaluation function is introduced to compare subgraphs with and without group integration. We clarified the effectiveness of binding site extraction with group integration through an experiment with 37 proteins.

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PocketPicker: analysis of ligand binding-sites with shape descriptors

Cited by 304 publications

References 33 publications

Distinct Roles of Secreted HtrA Proteases from Gram-negative Pathogens in Cleaving the Junctional Protein and Tumor Suppressor E-cadherin

Distinct Roles of Secreted HtrA Proteases from Gram-negative Pathogens in Cleaving the Junctional Protein and Tumor Suppressor E-cadherin

Structure‐based analysis of thermodynamic and mechanical properties of cavity‐containing proteins – case study of plant pathogenesis‐related proteins of class 10

Binding Site Extraction by Detecting Optimal Graphs from Protein Molecular Surfaces

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