AIE-induced fluorescent vesicles containing amphiphilic binding pockets and the FRET triggered by host–guest chemistry

<p>The reliability evaluation of the predicted binding constants in numerous models is also a challenge for supramolecular host-guest chemistry. Here, I briefly formulate binding isotherm with the derivation of the multivalent equilibrium model for the chemist who wants to determine the binding constants of their compounds. This article gives an in-depth understanding of the stoichiometry of binding equilibrium to take divalent binding equilibria bearing two structurally identical binding sites as an example. The stoichiometry of binding equilibrium is affected by (1) the cooperativity of complex, (2) the concentration of titration media, and (3) the equivalents of guests. The simulations were conducted with simple Python codes.</p>

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Subunit stoichiometry and arrangement in a heteromeric glutamate-gated chloride channel

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1423753113 ◽

2016 ◽

Vol 113 (5) ◽

pp. E644-E653 ◽

Cited By ~ 6

Author(s):

Nurit Degani-Katzav ◽

Revital Gortler ◽

Lilach Gorodetzki ◽

Yoav Paas

Keyword(s):

Binding Site ◽

Binding Sites ◽

Parasitic Diseases ◽

Β Subunit ◽

River Blindness ◽

Subunit Stoichiometry ◽

Binding Pockets ◽

Extracellular Side ◽

Electrophysiological Characterization

The invertebrate glutamate-gated chloride-selective receptors (GluClRs) are ion channels serving as targets for ivermectin (IVM), a broad-spectrum anthelmintic drug used to treat human parasitic diseases like river blindness and lymphatic filariasis. The native GluClR is a heteropentamer consisting of α and β subunit types, with yet unknown subunit stoichiometry and arrangement. Based on the recent crystal structure of a homomeric GluClαR, we introduced mutations at the intersubunit interfaces where Glu (the neurotransmitter) binds. By electrophysiological characterization of these mutants, we found heteromeric assemblies with two equivalent Glu-binding sites at β/α intersubunit interfaces, where the GluClβ and GluClα subunits, respectively, contribute the “principal” and “complementary” components of the putative Glu-binding pockets. We identified a mutation in the IVM-binding site (far away from the Glu-binding sites), which significantly increased the sensitivity of the heteromeric mutant receptor to both Glu and IVM, and improved the receptor subunits’ cooperativity. We further characterized this heteromeric GluClR mutant as a receptor having a third Glu-binding site at an α/α intersubunit interface. Altogether, our data unveil heteromeric GluClR assemblies having three α and two β subunits arranged in a counterclockwise β-α-β-α-α fashion, as viewed from the extracellular side, with either two or three Glu-binding site interfaces.

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Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

Frontiers in Chemistry ◽

10.3389/fchem.2020.596412 ◽

2020 ◽

Vol 8 ◽

Author(s):

Chinmayee Choudhury ◽

Anshu Bhardwaj

Keyword(s):

Binding Site ◽

Binding Sites ◽

Chemical Space ◽

Pharmacophore Model ◽

Model Systems ◽

Receptor Interaction ◽

Hybrid Molecules ◽

Wide Range ◽

Binding Pockets ◽

Pharmacophore Models

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.

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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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Effect of Bile Acids and Other Factors on Cholesterol Uptake by Inverted Intestinal Sacs

American Journal of Physiology-Legacy Content ◽

10.1152/ajplegacy.1958.195.3.773 ◽

1958 ◽

Vol 195 (3) ◽

pp. 773-778 ◽

Cited By ~ 6

Author(s):

Archie L. Smith ◽

C. R. Treadwell

Keyword(s):

Small Intestine ◽

Bile Acid ◽

Bile Acids ◽

Binding Sites ◽

Cellular Protein ◽

Rat Small Intestine ◽

Cholesterol Uptake ◽

Quantitative Studies ◽

Mucosal Cells ◽

Intestinal Mucosal Cells

Conditions for the use of inverted sacs of rat small intestine for quantitative studies of cholesterol uptake are described. The uptake of cholesterol by sacs did not require glucose in the incubation medium. Albumin aided cholesterol uptake but was not obligatory for this process. A binding of cholesterol to a cellular protein is proposed as the mechanism for the entrance of cholesterol into intestinal mucosal cells. Both conjugated and unconjugated bile acids inhibited cholesterol uptake possibly by blocking the binding sites of the protein responsible for cholesterol uptake. Commercial taurocholate and glycocholate contain an inhibitor of cholesterol uptake other than the bile acid.

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

10.1101/2020.04.15.042655 ◽

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 .

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Titration Simulations for Understanding of the Binding Equilibrium

10.26434/chemrxiv.12980348.v2 ◽

2020 ◽

Author(s):

Dae Hyup Sohn

Keyword(s):

Equilibrium Model ◽

Binding Sites ◽

Binding Constants ◽

Reliability Evaluation ◽

Binding Isotherm ◽

Guest Chemistry

<p>The reliability evaluation of the predicted binding constants in numerous models is a challenge for supramolecular host-guest chemistry. Here, I briefly formulate binding isotherm with the derivation of the multivalent equilibrium model for the chemist who wants to determine the binding constants of their compounds. This article gives an in-depth understanding of the stoichiometry of binding equilibrium to take divalent binding equilibria bearing two structurally identical binding sites as an example. The stoichiometry of binding equilibrium is affected by (1) the cooperativity of complex, (2) the concentration of titration media, and (3) the equivalents of guests. The simulations were conducted with simple Python codes.</p>

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Structural basis for human TRPC5 channel inhibition by two distinct inhibitors

eLife ◽

10.7554/elife.63429 ◽

2021 ◽

Vol 10 ◽

Author(s):

Kangcheng Song ◽

Miao Wei ◽

Wenjun Guo ◽

Li Quan ◽

Yunlu Kang ◽

...

Keyword(s):

Anxiety Disorder ◽

Binding Sites ◽

Structural Basis ◽

Inhibitory Mechanism ◽

Inhibitor Binding ◽

Design And Optimization ◽

Physiological Processes ◽

Channel Inhibition ◽

Binding Pockets ◽

Extracellular Side

TRPC5 channel is a non-selective cation channel that participates diverse physiological processes. TRPC5 inhibitors show promise in the treatment of anxiety disorder, depression and kidney disease. However, the binding sites and inhibitory mechanism of TRPC5 inhibitors remain elusive. Here we present the cryo-EM structures of human TRPC5 in complex with two distinct inhibitors, namely clemizole and HC-070, to the resolution of 2.7 Å. The structures reveal that clemizole binds inside the voltage sensor-like domain of each subunit. In contrast, HC-070 is wedged between adjacent subunits and replaces the glycerol group of a putative DAG molecule near the extracellular side. Moreover, we found mutations in the inhibitor binding pockets altered the potency of inhibitors. These structures suggest that both clemizole and HC-070 exert the inhibitory functions by stabilizing the ion channel in a non-conductive closed state. These results pave the way for further design and optimization of inhibitors targeting human TRPC5.

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A universal pocket in Fatty acyl-AMP ligases ensures redirection of fatty acid pool away from Coenzyme A-based activation

eLife ◽

10.7554/elife.70067 ◽

2021 ◽

Vol 10 ◽

Author(s):

Gajanan Shrikant Patil ◽

Priyadarshan Kinatukara ◽

Sudipta Mondal ◽

Sakshi Shambhavi ◽

Ketan D Patel ◽

...

Keyword(s):

Fatty Acids ◽

Binding Sites ◽

Molecular Basis ◽

Coenzyme A ◽

Acyl Carrier Protein ◽

Fatty Acyl ◽

Carrier Protein ◽

Binding Pockets ◽

Fatty Acid Pool ◽

Do So

Fatty acyl-AMP ligases (FAALs) channelize fatty acids towards biosynthesis of virulent lipids in mycobacteria and other pharmaceutically or ecologically important polyketides and lipopeptides in other microbes. They do so by bypassing the ubiquitous coenzyme A-dependent activation and rely on the acyl carrier protein-tethered 4'-phosphopantetheine (holo-ACP). The molecular basis of how FAALs strictly reject chemically identical and abundant acceptors like coenzyme A (CoA) and accept holo-ACP unlike other members of the ANL superfamily remains elusive. We show FAALs have plugged the promiscuous canonical CoA-binding pockets and utilize highly selective alternative binding sites. These alternative pockets can distinguish adenosine 3', 5'-bisphosphate-containing CoA from holo-ACP and thus FAALs can distinguish between CoA and holo-ACP. These exclusive features helped identify the omnipresence of FAAL-like proteins and their emergence in plants, fungi, and animals with unconventional domain organisations. The universal distribution of FAALs suggests they are parallelly evolved with FACLs for ensuring a CoA-independent activation and redirection of fatty acids towards lipidic metabolites.

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Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

Molecules ◽

10.3390/molecules26051224 ◽

2021 ◽

Vol 26 (5) ◽

pp. 1224

Author(s):

Paula Jofily ◽

Pedro G. Pascutti ◽

Pedro H. M. Torres

Keyword(s):

Binding Sites ◽

Search Algorithm ◽

Pose Prediction ◽

Success Rates ◽

Protein Surfaces ◽

Cavity Detection ◽

Docking Method ◽

Potential Binding ◽

Binding Pockets ◽

Automated Pipeline

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.

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