Architectural Repertoire of Ligand-Binding Pockets on Protein Surfaces

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.

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Simple method for locating possible ligand binding sites on protein surfaces

Journal of Computational Chemistry ◽

10.1002/(sici)1096-987x(19990715)20:9<983::aid-jcc9>3.0.co;2-r ◽

1999 ◽

Vol 20 (9) ◽

pp. 983-988 ◽

Cited By ~ 23

Author(s):

Andrey A. Bliznyuk ◽

Jill E. Gready

Keyword(s):

Ligand Binding ◽

Binding Sites ◽

Simple Method ◽

Protein Surfaces ◽

Ligand Binding Sites

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

Bioinformatics ◽

10.1093/bioinformatics/btaa094 ◽

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.

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NMR Spectroscopy of supramolecular chemistry on protein surfaces

Beilstein Journal of Organic Chemistry ◽

10.3762/bjoc.16.203 ◽

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.

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