Fluorescent nanoparticle sensors with tailor-made recognition units and proximate fluorescent reporter groups

2018 ◽  
Vol 42 (12) ◽  
pp. 9377-9380 ◽  
Author(s):  
Xiaoyu Xing ◽  
Yan Zhao
Keyword(s):  
Molecular Imprinting ◽  
Binding Pocket ◽  
Fluorescent Sensors ◽  
Covalent Modification ◽  
Fluorescent Reporter ◽  
Binding Pockets ◽  
Fluorescent Nanoparticle

Molecular imprinting in micelles followed by covalent modification of the binding pocket yielded fluorescent sensors with precisely constructed binding pockets.

scholarly journals Structure-based inhibitor design of mutant RAS proteins—a paradigm shift

2020 ◽  
Vol 39 (4) ◽  
pp. 1091-1105
Author(s):  
Kinga Nyíri ◽  
Gergely Koppány ◽  
Beáta G. Vértessy
Keyword(s):  
Covalent Modification ◽  
Small Gtpase ◽  
Drug Binding ◽  
Ras Proteins ◽  
Structure Based Drug Design ◽  
Lung Cancers ◽  
Long Time ◽  
Binding Pockets ◽  
Cancer Types ◽  
Kras Protein

AbstractAs a member of small GTPase family, KRAS protein is a key physiological modulator of various cellular activities including proliferation. However, mutations of KRAS present in numerous cancer types, most frequently in pancreatic (> 60%), colorectal (> 40%), and lung cancers, drive oncogenic processes through overactivation of proliferation. The G12C mutation of KRAS protein is especially abundant in the case of these types of malignancies. Despite its key importance in human disease, KRAS was assumed to be non-druggable for a long time since the protein seemingly lacks potential drug-binding pockets except the nucleotide-binding site, which is difficult to be targeted due to the high affinity of KRAS for both GDP and GTP. Recently, a new approach broke the ice and provided evidence that upon covalent targeting of the G12C mutant KRAS, a highly dynamic pocket was revealed. This novel targeting is especially important since it serves with an inherent solution for drug selectivity. Based on these results, various structure-based drug design projects have been launched to develop selective KRAS mutant inhibitors. In addition to the covalent modification strategy mostly applicable for G12C mutation, different innovative solutions have been suggested for the other frequently occurring oncogenic G12 mutants. Here we summarize the latest advances of this field, provide perspectives for novel approaches, and highlight the special properties of KRAS, which might issue some new challenges.

scholarly journals Exploring Multi‐Subsite Binding Pockets in Proteins: DEEP‐STD NMR Fingerprinting and Molecular Dynamics Unveil a Cryptic Subsite at the GM1 Binding Pocket of Cholera Toxin B

2020 ◽  
Vol 26 (44) ◽  
pp. 10024-10034
Author(s):  
Serena Monaco ◽  
Samuel Walpole ◽  
Hassan Doukani ◽  
Ridvan Nepravishta ◽  
Macarena Martínez‐Bailén ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Cholera Toxin ◽  
Binding Pocket ◽  
Toxin B ◽  
Cholera Toxin B ◽  
Binding Pockets ◽  
Std Nmr

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.

scholarly journals Susceptibility of amphibians to chytridiomycosis is associated with MHC class II conformation

2015 ◽  
Vol 282 (1805) ◽  
pp. 20143127 ◽  
Author(s):  
Arnaud Bataille ◽  
Scott D. Cashins ◽  
Laura Grogan ◽  
Lee F. Skerratt ◽  
David Hunter ◽  
...  
Keyword(s):  
Mhc Class Ii ◽  
Batrachochytrium Dendrobatidis ◽  
Binding Pocket ◽  
Class Ii ◽  
Population Declines ◽  
Mhc Ii ◽  
Resistant Species ◽  
Binding Pockets ◽  
Experimental Approaches ◽  
Binding Groove

The pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd) can cause precipitous population declines in its amphibian hosts. Responses of individuals to infection vary greatly with the capacity of their immune system to respond to the pathogen. We used a combination of comparative and experimental approaches to identify major histocompatibility complex class II (MHC-II) alleles encoding molecules that foster the survival of Bd-infected amphibians. We found that Bd-resistant amphibians across four continents share common amino acids in three binding pockets of the MHC-II antigen-binding groove. Moreover, strong signals of selection acting on these specific sites were evident among all species co-existing with the pathogen. In the laboratory, we experimentally inoculated Australian tree frogs with Bd to test how each binding pocket conformation influences disease resistance. Only the conformation of MHC-II pocket 9 of surviving subjects matched those of Bd-resistant species. This MHC-II conformation thus may determine amphibian resistance to Bd, although other MHC-II binding pockets also may contribute to resistance. Rescuing amphibian biodiversity will depend on our understanding of amphibian immune defence mechanisms against Bd. The identification of adaptive genetic markers for Bd resistance represents an important step forward towards that goal.

Chemical parameters influencing fine-tuning in the binding of macrolide antibiotics to the ribosomal tunnel

2007 ◽  
Vol 79 (6) ◽  
pp. 955-968 ◽  
Author(s):  
Erez Pyetan ◽  
David Baram ◽  
Tamar Auerbach-Nevo ◽  
Ada Yonath
Keyword(s):  
Deinococcus Radiodurans ◽  
Bacterial Species ◽  
Ribosomal Subunit ◽  
Binding Pocket ◽  
Fine Tuning ◽  
23S Rrna ◽  
Structure Based Drug Design ◽  
Altered Level ◽  
Ribosomal Tunnel ◽  
Binding Pockets

In comparison to existing structural, biochemical, and therapeutical data, the crystal structures of large ribosomal subunit from the eubacterial pathogen model Deinococcus radiodurans in complex with the 14-membered macrolides erythromycylamine, RU69874, and the 16-membered macrolide josamycin, highlighted the similarities and differences in macrolides binding to the ribosomal tunnel. The three compounds occupy the macrolide binding pocket with their desosamine or mycaminose aminosugar, the C4-C7 edge of the macrolactone ring and the cladinose sugar sharing similar positions and orientations, although the latter, known to be unnecessary for antibiotic activity, displays fewer contacts. The macrolactone ring displays altogether few contacts with the ribosome and can, therefore, tilt in order to optimize its interaction with the 23S rRNA. In addition to their contacts with nucleotides of domain V of the 23S RNA, erythromycylamine and RU69874 interact with domain II nucleotide U790, and RU69874 also reaches van der Waals distance from A752, in a fashion similar to that observed for the ketolides telithromycin and cethromycin. The variability in the sequences and consequently the diversity of the conformations of macrolide binding pockets in various bacterial species can explain the drug's altered level of effectiveness on different organisms and is thus an important factor in structure-based drug design.

scholarly journals Covalent Modification and Regulation of the Nuclear Receptor Nurr1 by a Dopamine Metabolite

2018 ◽  
Author(s):  
John M. Bruning ◽  
Yan Wang ◽  
Francesca Oltrabella ◽  
Boxue Tian ◽  
Svetlana A. Kholodar ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Ligand Binding ◽  
Nuclear Receptor ◽  
Target Genes ◽  
Cultured Cells ◽  
Binding Pocket ◽  
Regulatory Elements ◽  
Covalent Modification ◽  
X Ray Crystallography

SUMMARYNurr1, a nuclear receptor essential for the development, maintenance, and survival of midbrain dopaminergic neurons, is a potential therapeutic target for Parkinson’s disease, a neurological disorder characterized by the degeneration of these same neurons. Efforts to identify Nurr1 agonists have been hampered by the recognition that it lacks several classic regulatory elements of nuclear receptor function, including the canonical ligand-binding pocket. Here we report that the dopamine metabolite 5,6-dihydroxyindole (DHI) binds directly to and modulates the activity of Nurr1. Using biophysical assays and x-ray crystallography we show that DHI binds to the ligand binding domain within a non-canonical pocket, forming a covalent adduct with Cys566. In cultured cells and zebrafish, DHI stimulates Nurr1 activity, including the transcription of target genes underlying dopamine homeostasis. These findings suggest avenues for developing synthetic Nurr1 ligands to ameliorate the symptoms and progression of Parkinson’s disease.

scholarly journals Pairing interactions between nucleobases and ligands in aptamer:ligand complexes of riboswitches: Crystal structure analysis, classification, optimal structures and accurate interaction energies

2019 ◽  
Author(s):  
Preethi S. Prabhakar ◽  
Purshotam Sharma ◽  
Abhijit Mitra
Keyword(s):  
Hydrogen Bonding ◽  
Visual Inspection ◽  
Weak Interactions ◽  
Binding Pocket ◽  
Crystal Structure Analysis ◽  
Thiamine Pyrophosphate ◽  
Interaction Energies ◽  
Binding Pockets ◽  
Pairing Interactions

ABSTRACTIn the present work, sixty-seven crystal structures of the aptamer domains of RNA riboswitches, are chosen for analysis of the structure and strength of hydrogen bonding (pairing) interactions between nucleobases constituting the aptamer binding pockets and the bound ligands. A total of eighty unique base:ligand hydrogen-bonded pairs containing at least two hydrogen bonds were identified through visual inspection. Classification of these contacts in terms of the interacting edge of the aptamer nucleobase revealed that interactions involving the Watson-Crick edge are the most common, followed by the sugar edge of purines and the Hoogsteen edge of uracil. Alternatively, classification in terms of the chemical constitution of the ligand yields five unique classes of base:ligand pairs: base:base, base:amino acid, base:sugar, base:phosphate and base:other. Further, quantum mechanical (QM) geometry optimizations revealed that sixty seven out of eighty pairs exhibit stable geometries and optimal deviations from their macromolecular crystal occurrences. This indicates that these contacts are well-defined RNA aptamer:ligand interaction motifs. QM calculated interaction energies of base:ligand pairs reveal rich hydrogen bonding landscape, ranging from weak interactions (base:other, –3 kcal/mol) to strong (base:phosphate, –48 kcal/mol) contacts. The analysis was further extended to study the biological importance of base:ligand interactions in the binding pocket of the tetrahydrofolate riboswitch and thiamine pyrophosphate riboswitch. Overall, our study helps in understanding the structural and energetic features of base:ligand pairs in riboswitches, which could aid in developing meaningful hypotheses in context of RNA:ligand recognition. This can, in turn contribute towards current efforts to develop antimicrobials that target RNAs.

scholarly journals The protein-binding pocket of Botulinum neurotoxin B accommodates a preassembled synaptotagmin / ganglioside complex

2021 ◽  
Author(s):  
Jorge Ramirez-Franco ◽  
Fodil Azzaz ◽  
Marion Sangiardi ◽  
G&eacuteraldine Ferracci ◽  
Fahamoe Youssouf ◽  
...  
Keyword(s):  
Botulinum Neurotoxin ◽  
Transmembrane Domain ◽  
Binding Pocket ◽  
Lipid Binding ◽  
Membrane Topology ◽  
Neuronal Membranes ◽  
Natural Variant ◽  
Synaptotagmin 1 ◽  
Binding Pockets ◽  
Serotype B

Botulinum neurotoxin serotype B (BoNT/B) uses two separate protein and polysialoglycolipid-binding pockets to interact with synaptotagmin 1/2 and gangliosides. However, an integrated model of this therapeutic tool bound to its neuronal receptors in a native membrane topology is still lacking. Using a panel of in silico and experimental approaches, we present here a new model for BoNT/B binding to neuronal membranes, in which the toxin binds to a preassembled synaptotagmin-ganglioside GT1b complex and a free ganglioside. This interaction allows a lipid-binding loop of BoNT/B to engage in a series of concomitant interactions with the glycone part of GT1b and the transmembrane domain of synaptotagmin. Furthermore, our data provide molecular support for the decrease in BoNT/B sensitivity in Felidae that harbor the natural variant synaptotagmin2-N59Q. These results reveal multiple interactions of BoNT/B with gangliosides and support a novel paradigm in which a toxin recognizes a protein/ganglioside complex.

scholarly journals PickPocket : Pocket binding prediction for specific ligands family using neural networks.

2020 ◽  
Author(s):  
Benjamin Thomas VIART ◽  
Claudio Lorenzi ◽  
María Moriel-Carretero ◽  
Sofia Kossida
Keyword(s):  
Neural Networks ◽  
Fatty Acid ◽  
Ligand Binding ◽  
Binding Sites ◽  
Structural Data ◽  
Binding Pocket ◽  
Fatty Acid Binding Proteins ◽  
Binding Prediction ◽  
Fatty Acid Binding ◽  
Binding Pockets

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 .

scholarly journals PyVOL: a PyMOL plugin for visualization, comparison, and volume calculation of drug-binding sites

2019 ◽  
Author(s):  
Ryan H.B. Smith ◽  
Arvin C. Dar ◽  
Avner Schlessinger
Keyword(s):  
Binding Pocket ◽  
Drug Binding ◽  
Identification Algorithm ◽  
Volume Determination ◽  
Volume Calculation ◽  
User Input ◽  
Binding Pockets ◽  
Drug Binding Sites ◽  
Python Package ◽  
Pymol Plugin

AbstractMotivationBinding pocket volumes are a simple yet important predictor of small molecule binding; however, generating visualizations of pocket topology and performing meaningful volume comparisons can be difficult with available tools. Current programs for accurate volume determination rely on extensive user input to define bulk solvent boundaries and to partition cavities into subpockets, increasing inter-user variability in measurements as well as time demands.ResultsWe developed PyVOL, a python package with a PyMOL interface and GUI, to visualize, to characterize, and to compare binding pockets. PyVOL’s pocket identification algorithm is designed to maximize reproducibility through minimization of user-provided parameters, avoidance of grid-based methods, and automated subpocket identification. This approach permits efficient, scalable volume calculations.AvailabilityPyVOL is released under the MIT License. Source code and documentation are available through github (https://github.com/schlessingerlab/pyvol/) with distribution through PyPI (bio-pyvol)[email protected], [email protected]

Sign in / Sign up

Export Citation Format

Share Document