Graph-Based Clustering of Predicted Ligand-Binding Pockets on Protein Surfaces-Reference-Cited by-同舟云学术

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Graph-Based Clustering of Predicted Ligand-Binding Pockets on Protein Surfaces

Published:2015-09-11 Issue:9 Volume:55 Page:1944-1952
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Degac Jennifer¹,Winter Uwe¹,Helms Volkhard¹

Affiliation:

1. Center for Bioinformatics, Saarland University, 66041 Saarbruecken, Germany

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.5b00045

Reference29 articles.

1. POCKET: A computer graphies method for identifying and displaying protein cavities and their surrounding amino acids

2. LIGSITE: automatic and efficient detection of potential small molecule-binding sites in proteins

3. PocketPicker: analysis of ligand binding-sites with shape descriptors

4. Detection of multiscale pockets on protein surfaces using mathematical morphology

5. SURFNET: A program for visualizing molecular surfaces, cavities, and intermolecular interactions

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Estimating the Similarity between Protein Pockets;International Journal of Molecular Sciences;2022-10-18

2. PDBspheres: a method for finding 3D similarities in local regions in proteins;NAR Genomics and Bioinformatics;2022-10-06

3. PDBspheres - a method for finding 3D similarities in local regions in proteins;2022-01-05

4. DeeplyTough: Learning Structural Comparison of Protein Binding Sites;2019-04-05

5. P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure;Journal of Cheminformatics;2018-08-14

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