Architectural Repertoire of Ligand-Binding Pockets on Protein Surfaces

ChemBioChem ◽  
2010 ◽  
Vol 11 (4) ◽  
pp. 556-563 ◽  
Author(s):  
Martin Weisel ◽  
Jan M. Kriegl ◽  
Gisbert Schneider
Keyword(s):  
Ligand Binding ◽  
Protein Surfaces ◽  
Binding Pockets

scholarly journals Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gert-Jan Bekker ◽  
Ikuo Fukuda ◽  
Junichi Higo ◽  
Yoshifumi Fukunishi ◽  
Narutoshi Kamiya
Keyword(s):  
Ligand Binding ◽  
Cancer Progression ◽  
Hydrophobic Interactions ◽  
Binding Mechanism ◽  
Docking Simulations ◽  
Large Conformational Change ◽  
Binding Pockets ◽  
Dynamic Docking ◽  
Site Binding ◽  
Apo State

AbstractWe have performed multicanonical molecular dynamics (McMD) based dynamic docking simulations to study and compare the binding mechanism between two medium-sized inhibitors (ABT-737 and WEHI-539) that bind to the cryptic site of Bcl-xL, by exhaustively sampling the conformational and configurational space. Cryptic sites are binding pockets that are transiently formed in the apo state or are induced upon ligand binding. Bcl-xL, a pro-survival protein involved in cancer progression, is known to have a cryptic site, whereby the shape of the pocket depends on which ligand is bound to it. Starting from the apo-structure, we have performed two independent McMD-based dynamic docking simulations for each ligand, and were able to obtain near-native complex structures in both cases. In addition, we have also studied their interactions along their respective binding pathways by using path sampling simulations, which showed that the ligands form stable binding configurations via predominantly hydrophobic interactions. Although the protein started from the apo state, both ligands modulated the pocket in different ways, shifting the conformational preference of the sub-pockets of Bcl-xL. We demonstrate that McMD-based dynamic docking is a powerful tool that can be effectively used to study binding mechanisms involving a cryptic site, where ligand binding requires a large conformational change in the protein to occur.

scholarly journals BionoiNet: ligand-binding site classification with off-the-shelf deep neural network

2020 ◽  
Vol 36 (10) ◽  
pp. 3077-3083
Author(s):  
Wentao Shi ◽  
Jeffrey M Lemoine ◽  
Abd-El-Monsif A Shawky ◽  
Manali Singha ◽  
Limeng Pu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Supplementary Information ◽  
Heme Binding ◽  
Unseen Data ◽  
Ligand Binding Sites ◽  
Binding Pockets

Abstract Motivation Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. Results We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. Availability and implementation BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals NMR Spectroscopy of supramolecular chemistry on protein surfaces

2020 ◽  
Vol 16 ◽  
pp. 2505-2522
Author(s):  
Peter Bayer ◽  
Anja Matena ◽  
Christine Beuck
Keyword(s):  
Nmr Spectroscopy ◽  
Supramolecular Chemistry ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Atomic Resolution ◽  
Protein Protein Interactions ◽  
Protein Surfaces ◽  
Interaction Partners ◽  
Binding Pockets ◽  
Nmr Methods

As one of the few analytical methods that offer atomic resolution, NMR spectroscopy is a valuable tool to study the interaction of proteins with their interaction partners, both biomolecules and synthetic ligands. In recent years, the focus in chemistry has kept expanding from targeting small binding pockets in proteins to recognizing patches on protein surfaces, mostly via supramolecular chemistry, with the goal to modulate protein–protein interactions. Here we present NMR methods that have been applied to characterize these molecular interactions and discuss the challenges of this endeavor.

scholarly journals Probing Ligand-binding Pockets of the Mevalonate Pathway Enzymes fromStreptococcus pneumoniae

2010 ◽  
Vol 285 (27) ◽  
pp. 20654-20663 ◽  
Author(s):  
Scott T. Lefurgy ◽  
Sofia B. Rodriguez ◽  
Chan Sun Park ◽  
Sean Cahill ◽  
Richard B. Silverman ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Mevalonate Pathway ◽  
Binding Pockets

A method for localizing ligand binding pockets in protein structures

2005 ◽  
Vol 62 (2) ◽  
pp. 479-488 ◽  
Author(s):  
Fabian Glaser ◽  
Richard J. Morris ◽  
Rafael J. Najmanovich ◽  
Roman A. Laskowski ◽  
Janet M. Thornton
Keyword(s):  
Ligand Binding ◽  
Protein Structures ◽  
Binding Pockets

In-silico Screening using Flexible Ligand Binding Pockets: A Molecular Dynamics-based Approach

2005 ◽  
Vol 19 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Dakshanamurthy Sivanesan ◽  
Rajendram V Rajnarayanan ◽  
Jason Doherty ◽  
Nagarajan Pattabiraman
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
In Silico ◽  
In Silico Screening ◽  
Flexible Ligand ◽  
Binding Pockets

scholarly journals Molecular Similarities in the Ligand Binding Pockets of an Odorant Receptor and the Metabotropic Glutamate Receptors

2003 ◽  
Vol 278 (43) ◽  
pp. 42551-42559 ◽  
Author(s):  
Donghui Kuang ◽  
Yi Yao ◽  
Minghua Wang ◽  
N. Pattabiraman ◽  
Lakshmi P. Kotra ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Glutamate Receptors ◽  
Metabotropic Glutamate Receptors ◽  
Odorant Receptor ◽  
Metabotropic Glutamate ◽  
Binding Pockets
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