Literature DB >> 36225529

PDBspheres: a method for finding 3D similarities in local regions in proteins.

Adam T Zemla1, Jonathan E Allen1, Dan Kirshner2, Felice C Lightstone2.   

Abstract

We present a structure-based method for finding and evaluating structural similarities in protein regions relevant to ligand binding. PDBspheres comprises an exhaustive library of protein structure regions ('spheres') adjacent to complexed ligands derived from the Protein Data Bank (PDB), along with methods to find and evaluate structural matches between a protein of interest and spheres in the library. PDBspheres uses the LGA (Local-Global Alignment) structure alignment algorithm as the main engine for detecting structural similarities between the protein of interest and template spheres from the library, which currently contains >2 million spheres. To assess confidence in structural matches, an all-atom-based similarity metric takes side chain placement into account. Here, we describe the PDBspheres method, demonstrate its ability to detect and characterize binding sites in protein structures, show how PDBspheres-a strictly structure-based method-performs on a curated dataset of 2528 ligand-bound and ligand-free crystal structures, and use PDBspheres to cluster pockets and assess structural similarities among protein binding sites of 4876 structures in the 'refined set' of the PDBbind 2019 dataset.
© The Author(s) 2022. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

Entities:  

Year:  2022        PMID: 36225529      PMCID: PMC9549786          DOI: 10.1093/nargab/lqac078

Source DB:  PubMed          Journal:  NAR Genom Bioinform        ISSN: 2631-9268


  39 in total

1.  Shape variation in protein binding pockets and their ligands.

Authors:  Abdullah Kahraman; Richard J Morris; Roman A Laskowski; Janet M Thornton
Journal:  J Mol Biol       Date:  2007-02-07       Impact factor: 5.469

2.  Assessment of ligand binding site predictions in CASP10.

Authors:  Tiziano Gallo Cassarino; Lorenza Bordoli; Torsten Schwede
Journal:  Proteins       Date:  2014-02

3.  A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs).

Authors:  Christiane Ehrt; Tobias Brinkjost; Oliver Koch
Journal:  PLoS Comput Biol       Date:  2018-11-08       Impact factor: 4.475

4.  The other 90% of the protein: assessment beyond the Calphas for CASP8 template-based and high-accuracy models.

Authors:  Daniel A Keedy; Christopher J Williams; Jeffrey J Headd; W Bryan Arendall; Vincent B Chen; Gary J Kapral; Robert A Gillespie; Jeremy N Block; Adam Zemla; David C Richardson; Jane S Richardson
Journal:  Proteins       Date:  2009

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

Authors:  Takeshi Kawabata
Journal:  Proteins       Date:  2010-04

6.  A critical comparative assessment of predictions of protein-binding sites for biologically relevant organic compounds.

Authors:  Ke Chen; Marcin J Mizianty; Jianzhao Gao; Lukasz Kurgan
Journal:  Structure       Date:  2011-05-11       Impact factor: 5.006

7.  Assessment of ligand binding residue predictions in CASP8.

Authors:  Gonzalo López; Iakes Ezkurdia; Michael L Tress
Journal:  Proteins       Date:  2009

8.  Improving detection of protein-ligand binding sites with 3D segmentation.

Authors:  Marta M Stepniewska-Dziubinska; Piotr Zielenkiewicz; Pawel Siedlecki
Journal:  Sci Rep       Date:  2020-03-19       Impact factor: 4.379

9.  Clustering protein environments for function prediction: finding PROSITE motifs in 3D.

Authors:  Sungroh Yoon; Jessica C Ebert; Eui-Young Chung; Giovanni De Micheli; Russ B Altman
Journal:  BMC Bioinformatics       Date:  2007-05-22       Impact factor: 3.169

10.  Predicting binding sites from unbound versus bound protein structures.

Authors:  Jordan J Clark; Zachary J Orban; Heather A Carlson
Journal:  Sci Rep       Date:  2020-09-28       Impact factor: 4.379

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