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

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 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 FTSite: high accuracy detection of ligand binding sites on unbound protein structures

2011 ◽  
Vol 28 (2) ◽  
pp. 286-287 ◽  
Author(s):  
Chi-Ho Ngan ◽  
David R. Hall ◽  
Brandon Zerbe ◽  
Laurie E. Grove ◽  
Dima Kozakov ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
High Accuracy ◽  
Ligand Binding Sites

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 Identification of protein pockets and cavities by Euclidean Distance Transform

2018 ◽  
Author(s):  
Sebastian Daberdaku
Keyword(s):  
Ligand Binding ◽  
Active Sites ◽  
Euclidean Distance ◽  
Protein Structures ◽  
Ligand Docking ◽  
Distance Transform ◽  
Structure Based Drug Design ◽  
Distance Map ◽  
Euclidean Distance Transform ◽  
Protein Pockets

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

scholarly journals The role of water and protein flexibility in the structure-based virtual screening of allosteric GPCR modulators: an mGlu5 receptor case study

2019 ◽  
Vol 33 (9) ◽  
pp. 787-797 ◽  
Author(s):  
Zoltán Orgován ◽  
György G. Ferenczy ◽  
György M. Keserű
Keyword(s):  
Virtual Screening ◽  
Ligand Binding ◽  
Conformational Changes ◽  
Metabotropic Glutamate Receptor ◽  
Protein Structures ◽  
Protein Flexibility ◽  
Water Molecules ◽  
Large Set ◽  
Allosteric Modulators

Abstract Stabilizing unique receptor conformations, allosteric modulators of G-protein coupled receptors (GPCRs) might open novel treatment options due to their new pharmacological action, their enhanced specificity and selectivity in both binding and signaling. Ligand binding occurs at intrahelical allosteric sites and involves significant induced fit effects that include conformational changes in the local protein environment and water networks. Based on the analysis of available crystal structures of metabotropic glutamate receptor 5 (mGlu5) we investigated these effects in the binding of mGlu5 receptor negative allosteric modulators. A large set of retrospective virtual screens revealed that the use of multiple protein structures and the inclusion of selected water molecules improves virtual screening performance compared to conventional docking strategies. The role of water molecules and protein flexibility in ligand binding can be taken into account efficiently by the proposed docking protocol that provided reasonable enrichment of true positives. This protocol is expected to be useful also for identifying intrahelical allosteric modulators for other GPCR targets.

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.

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
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