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 ◽  
...  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gert-Jan Bekker ◽  
Ikuo Fukuda ◽  
Junichi Higo ◽  
Yoshifumi Fukunishi ◽  
Narutoshi Kamiya

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.


ChemBioChem ◽  
2010 ◽  
Vol 11 (4) ◽  
pp. 556-563 ◽  
Author(s):  
Martin Weisel ◽  
Jan M. Kriegl ◽  
Gisbert Schneider

2020 ◽  
Vol 36 (10) ◽  
pp. 3077-3083
Author(s):  
Wentao Shi ◽  
Jeffrey M Lemoine ◽  
Abd-El-Monsif A Shawky ◽  
Manali Singha ◽  
Limeng Pu ◽  
...  

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.


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

2005 ◽  
Vol 19 (4) ◽  
pp. 213-228 ◽  
Author(s):  
Dakshanamurthy Sivanesan ◽  
Rajendram V Rajnarayanan ◽  
Jason Doherty ◽  
Nagarajan Pattabiraman

2003 ◽  
Vol 278 (43) ◽  
pp. 42551-42559 ◽  
Author(s):  
Donghui Kuang ◽  
Yi Yao ◽  
Minghua Wang ◽  
N. Pattabiraman ◽  
Lakshmi P. Kotra ◽  
...  

2020 ◽  
Author(s):  
Benjamin Thomas VIART ◽  
Claudio Lorenzi ◽  
María Moriel-Carretero ◽  
Sofia Kossida

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket .


2020 ◽  
Vol 21 (19) ◽  
pp. 7144
Author(s):  
Xiangyang Li ◽  
Xueqing Yang ◽  
Xiaodong Zheng ◽  
Miao Bai ◽  
Deyu Hu

Molecular targets play important roles in agrochemical discovery. Numerous pesticides target the key proteins in pathogens, insect, or plants. Investigating ligand-binding pockets and/or active sites in the proteins’ structures is usually the first step in designing new green pesticides. Thus, molecular target structures are extremely important for the discovery and development of such pesticides. In this manuscript, we present a review of the molecular target structures, including those of antiviral, fungicidal, bactericidal, insecticidal, herbicidal, and plant growth-regulator targets, currently used in agrochemical research. The data will be helpful in pesticide design and the discovery of new green pesticides.


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