Large-Scale Mining for Similar Protein Binding Pockets: With RAPMAD Retrieval on the Fly Becomes Real

2014 ◽  
Vol 55 (1) ◽  
pp. 165-179 ◽  
Author(s):  
Timo Krotzky ◽  
Christian Grunwald ◽  
Ute Egerland ◽  
Gerhard Klebe
Keyword(s):  
Protein Binding ◽  
Large Scale ◽  
Similar Protein ◽  
Binding Pockets

scholarly journals RsiteDB: a database of protein binding pockets that interact with RNA nucleotide bases

2009 ◽  
Vol 37 (Database) ◽  
pp. D369-D373 ◽  
Author(s):  
A. Shulman-Peleg ◽  
R. Nussinov ◽  
H. J. Wolfson
Keyword(s):  
Protein Binding ◽  
Binding Pockets ◽  
Nucleotide Bases

scholarly journals Functional characterization of 3D protein structures informed by human genetic diversity

2019 ◽  
Vol 116 (18) ◽  
pp. 8960-8965 ◽  
Author(s):  
Michael Hicks ◽  
Istvan Bartha ◽  
Julia di Iulio ◽  
J. Craig Venter ◽  
Amalio Telenti
Keyword(s):  
Large Scale ◽  
Functional Characterization ◽  
Protein Structures ◽  
Large Scale Data ◽  
Allosteric Sites ◽  
Missense Variation ◽  
Binding Pockets ◽  
Definition Of ◽  
Scale Data

Sequence variation data of the human proteome can be used to analyze 3D protein structures to derive functional insights. We used genetic variant data from nearly 140,000 individuals to analyze 3D positional conservation in 4,715 proteins and 3,951 homology models using 860,292 missense and 465,886 synonymous variants. Sixty percent of protein structures harbor at least one intolerant 3D site as defined by significant depletion of observed over expected missense variation. Structural intolerance data correlated with deep mutational scanning functional readouts for PPARG, MAPK1/ERK2, UBE2I, SUMO1, PTEN, CALM1, CALM2, and TPK1 and with shallow mutagenesis data for 1,026 proteins. The 3D structural intolerance analysis revealed different features for ligand binding pockets and orthosteric and allosteric sites. Large-scale data on human genetic variation support a definition of functional 3D sites proteome-wide.

Computational Strategy Revealing the Structural Determinant of Ligand Selectivity towards Highly Similar Protein Targets

2019 ◽  
Vol 21 (1) ◽  
pp. 76-88
Author(s):  
Hanxun Wang ◽  
Yinli Gao ◽  
Jian Wang ◽  
Maosheng Cheng
Keyword(s):  
Side Effects ◽  
Binding Pocket ◽  
Binding Affinities ◽  
Protein Targets ◽  
Ligand Selectivity ◽  
Drug Candidates ◽  
Multiple Strategies ◽  
Similar Protein ◽  
Single Target ◽  
Binding Pockets

Background: Poor selectivity of drug candidates may lead to toxicity and side effects accounting for as high as 60% failure rate, thus, the selectivity is consistently significant and challenging for drug discovery. Objective: To find highly specific small molecules towards very similar protein targets, multiple strategies are always employed, including (1) To make use of the diverse shape of binding pocket to avoid steric bump; (2) To increase binding affinities for favorite residues; (3) To achieve selectivity through allosteric regulation of target; (4) To stabalize the inactive conformation of protein target and (5) To occupy dual binding pockets of single target. Conclusion: In this review, we summarize computational strategies along with examples of their successful applications in designing selective ligands, with the aim to provide insights into everdiversifying drug development practice and inspire medicinal chemists to utilize computational strategies to avoid potential side effects due to low selectivity of ligands.

Incorporation of side chain flexibility into protein binding pockets using MTflex

2016 ◽  
Vol 24 (20) ◽  
pp. 4978-4987 ◽  
Author(s):  
Nupur Bansal ◽  
Zheng Zheng ◽  
Kenneth M. Merz
Keyword(s):  
Protein Binding ◽  
Side Chain ◽  
Chain Flexibility ◽  
Binding Pockets

scholarly journals Computational Analysis of HIV-1 Protease Protein Binding Pockets

2010 ◽  
Vol 50 (10) ◽  
pp. 1759-1771 ◽  
Author(s):  
Gene M. Ko ◽  
A. Srinivas Reddy ◽  
Sunil Kumar ◽  
Barbara A. Bailey ◽  
Rajni Garg
Keyword(s):  
Protein Binding ◽  
Computational Analysis ◽  
Binding Pockets ◽  
Hiv 1

scholarly journals Spatiotemporal identification of druggable binding sites using deep learning

2020 ◽  
Author(s):  
Igor Kozlovskii ◽  
Petr Popov
Keyword(s):  
Protein Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Large Scale ◽  
Specific Binding ◽  
Three Dimensional ◽  
Growth Factor Receptor ◽  
Protein Binding Sites ◽  
Binding Site Identification ◽  
Scale Detection

Identification of novel protein binding sites expands «druggable genome» and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble to object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, named BiteNet, that considers protein conformations as the 3D-images, binding sites as the objects on these images to detect, and conformational ensembles of proteins as the 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding sites in G protein-coupled receptors. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minute to analyze 1000 conformations of a protein with 2000 atoms. BiteNet is available at https://github.com/i-Molecule/bitenet.

scholarly journals Free enthalpies of replacing water molecules in protein binding pockets

2012 ◽  
Vol 26 (12) ◽  
pp. 1293-1309 ◽  
Author(s):  
Sereina Riniker ◽  
Luzi J. Barandun ◽  
François Diederich ◽  
Oliver Krämer ◽  
Andreas Steffen ◽  
...  
Keyword(s):  
Protein Binding ◽  
Water Molecules ◽  
Binding Pockets
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