scholarly journals Mapping convulsants’ binding to the GABA-A receptor chloride ionophore: A proposed model for channel binding sites

2007 ◽  
Vol 50 (1) ◽  
pp. 61-68 ◽  
Author(s):  
A.V. Kalueff
Keyword(s):  
Binding Sites ◽  
Gaba A ◽  
Proposed Model ◽  
Chloride Ionophore

3D U-Net: A voxel-based method in binding site prediction of protein structure

2021 ◽  
Vol 19 (02) ◽  
pp. 2150006
Author(s):  
Fatemeh Nazem ◽  
Fahimeh Ghasemi ◽  
Afshin Fassihi ◽  
Alireza Mehri Dehnavi
Keyword(s):  
Drug Design ◽  
Binding Site ◽  
Binding Sites ◽  
Semantic Segmentation ◽  
Structure Based Drug Design ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Novel Proteins ◽  
Proposed Model ◽  
Deep Learning Model

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted much interest. In this study, the binding site prediction is viewed as a semantic segmentation problem. An improved 3D version of the U-Net model based on the dice loss function is utilized to predict the binding sites accurately. The performance of the proposed model on the independent test datasets and SARS-COV-2 shows the segmentation model could predict the binding sites with a more accurate shape than the recently published deep learning model, i.e. DeepSite. Therefore, the model may help predict the binding sites of proteins and could be used in drug design for novel proteins.

scholarly journals Competitive dewetting underlies site-specific binding of general anesthetics to GABA(A) receptors

2019 ◽  
Author(s):  
Sruthi Murlidaran ◽  
Jérôme Hénin ◽  
Grace Brannigan
Keyword(s):  
Free Energy ◽  
Binding Sites ◽  
Critical Role ◽  
Free Energy Calculations ◽  
Water Molecules ◽  
General Anesthetics ◽  
General Anesthetic ◽  
Water Displacement ◽  
Gaba A ◽  
Acyl Chains

AbstractGABA(A) receptors are pentameric ligand-gated ion channels playing a critical role in the modulation of neuronal excitability. These inhibitory receptors, gated by γ-aminobutyric acid (GABA), can be potentiated and even directly activated by intravenous and inhalational anesthetics. Intersubunit cavities in the transmembrane domain have been consistently identified as putative binding sites by numerous experiment and simulation results. Synaptic GABA(A) receptors are predominantly found in a 2α:2β:1γ stoichiometry, with four unique inter-subunit interfaces. Experimental and computational results have suggested a perplexing specificity, given that cavity-lining residues are highly conserved, and the functional effects of general anesthetics are only weakly sensitive to most mutations of cavity residues. Here we use Molecular Dynamics simulations and thermodynamically rigorous alchemical free energy perturbation (AFEP) techniques to calculate affinities of the intravenous anesthetic propofol and the inhaled anesthetic sevoflurane to all intersubunit sites in a heteromeric GABA(A) receptor. We find that the best predictor of general anesthetic affinity for the intersubunit cavity sites is water displacement: combinations of anesthetic and binding site that displace more water molecules have higher affinities than those that displace fewer. The amount of water displacement is, in turn, a function of size of the general anesthetic, successful competition of the general anesthetic with water for the few hydrogen bonding partners in the site, and inaccessibility of the site to lipid acyl chains. The latter explains the surprisingly low affinity of GAs for the γ − α intersubunit site, which is missing a bulky methionine residue at the cavity entrance and can be occupied by acyl chains in the unbound state. Simulations also identify sevoflurane binding sites in the β subunit centers and in the pore, but predict that these are lower affinity than the intersubunit sites.SignificanceAfter over a century of research, it is established that general anesthetics interact directly with hydrophobic cavities in proteins. We still do not know why not all small hydrophobic molecules can act as general anesthetics, or why not all hydrophobic cavities bind these molecules. General anesthetics can even select among homologous sites on one critical target, the GABA(A) heteropentamer, although the origins of selectivity are unknown. Here we used rigorous free energy calculations to find that binding affinity correlates with the number of released water molecules, which in turn depends upon the lipid content of the cavity without bound anesthetic. Results suggest a mechanism that reconciles lipid-centered and protein-centered theories, and which can directly inform design of new anesthetics.

Selective Actions of Benzodiazepines at the Transmembrane Anesthetic Binding Sites of the GABA A Receptor: In Vitro and In Vivo Studies

2021 ◽  
Author(s):  
Megan McGrath ◽  
Helen Hoyt ◽  
Andrea Pence ◽  
Stuart A. Forman ◽  
Douglas E. Raines
Keyword(s):  
Binding Sites ◽  
In Vivo Studies ◽  
Gaba A

scholarly journals Heterogeneity of signal transduction by Na-K-ATPase α-isoforms: role of Src interaction

2018 ◽  
Vol 314 (2) ◽  
pp. C202-C210 ◽  
Author(s):  
Hui Yu ◽  
Xiaoyu Cui ◽  
Jue Zhang ◽  
Joe X. Xie ◽  
Moumita Banerjee ◽  
...  
Keyword(s):  
Binding Sites ◽  
Direct Interaction ◽  
Causative Role ◽  
Wild Type ◽  
Caveolin 1 ◽  
Proposed Model ◽  
Signaling Function ◽  
Domain Interactions ◽  
Key Residues

Of the four Na-K-ATPase α-isoforms, the ubiquitous α1 Na-K-ATPase possesses both ion transport and Src-dependent signaling functions. Mechanistically, we have identified two putative pairs of domain interactions between α1 Na-K-ATPase and Src that are critical for α1 signaling function. Our subsequent report that α2 Na-K-ATPase lacks these putative Src-binding sites and fails to carry on Src-dependent signaling further supported our proposed model of direct interaction between α1 Na-K-ATPase and Src but fell short of providing evidence for a causative role. This hypothesis was specifically tested here by introducing key residues of the two putative Src-interacting domains present on α1 but not α2 sequence into the α2 polypeptide, generating stable cell lines expressing this mutant, and comparing its signaling properties to those of α2-expressing cells. The mutant α2 was fully functional as a Na-K-ATPase. In contrast to wild-type α2, the mutant gained α1-like signaling function, capable of Src interaction and regulation. Consistently, the expression of mutant α2 redistributed Src into caveolin-1-enriched fractions and allowed ouabain to activate Src-mediated signaling cascades, unlike wild-type α2 cells. Finally, mutant α2 cells exhibited a growth phenotype similar to that of the α1 cells and proliferated much faster than wild-type α2 cells. These findings reveal the structural requirements for the Na-K-ATPase to function as a Src-dependent receptor and provide strong evidence of isoform-specific Src interaction involving the identified key amino acids. The sequences surrounding the putative Src-binding sites in α2 are highly conserved across species, suggesting that the lack of Src binding may play a physiologically important and isoform-specific role.

scholarly journals Fine specificity of drug-dependent antibodies reactive with a restricted domain of platelet GPIIIA

Blood ◽  
2008 ◽  
Vol 111 (3) ◽  
pp. 1234-1239 ◽  
Author(s):  
Julie A. Peterson ◽  
Tamara N. Nelson ◽  
Adam J. Kanack ◽  
Richard H. Aster
Keyword(s):  
Binding Sites ◽  
Immune Thrombocytopenia ◽  
Restricted Domain ◽  
Drug Induced ◽  
Structural Domains ◽  
Fine Specificity ◽  
Proposed Model ◽  
Restricted Domains ◽  
Glycoprotein Iiia ◽  
Drug Dependent

Abstract Drug-induced immune thrombocytopenia is caused by drug-dependent antibodies (DDAbs) that bind tightly to platelet glycoproteins only when drug is present. How drugs mediate this interaction is not yet resolved. Several studies indicate that sites recognized by DDAbs tend to cluster in specific structural domains, suggesting they may recognize a limited number of distinct epitopes. To address this issue, we characterized the binding sites for 16 quinine-dependent antibodies thought on the basis of preliminary studies to be possibly specific for a single epitope on glycoprotein IIIa (GPIIIa). Fourteen of the antibodies reacted with a 29-kDa GPIIIa fragment comprising only the GPIIIa hybrid and plextrin-semaphorin-integrin homology domains. However, studies with mutant GPIIIa and the blocking monoclonal antibody AP3 showed that the 14 DDAbs recognize at least 6 and possibly more distinct, but overlapping, structures involving GPIIIa residues 50 to 66. The findings suggest that even antibodies specific for restricted domains on a target glycoprotein may each have a slightly different fine specificity; ie, “unique” epitopes recognized by DDAbs may be rare or nonexistent. The observations are consistent with a recently proposed model in which drug reacts noncovalently with both target protein and antibody to promote binding of an otherwise nonreactive immunoglobulin.

scholarly journals AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites

2021 ◽  
Author(s):  
Mehdi Yazdani-Jahromi ◽  
Niloofar Yousefi ◽  
Aida Tayebi ◽  
Ozlem Ozmen Garibay ◽  
Sudipta Seal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computational Methods ◽  
Binding Sites ◽  
Drug Target ◽  
Critical Role ◽  
3D Structure ◽  
Target Protein ◽  
Binding Interaction ◽  
Target Interaction ◽  
Proposed Model

Investigating drug-target interactions plays a critical role in drug design and discovery. The vast chemical and proteomic space, along with the cost associated with invirto experiments motivate the use of computational methods to narrow down the search space for novel interaction of drug target pairs. Among all computational methods, deep learning algorithms have gained increased attention due to their power in automatically learning and extracting feature representations, and therefore identifying, processing and extrapolating complex hidden interactions between drugs and targets. In this study, we introduce and implement a new graph-based prediction model called AttentionSiteDTI. Our proposed model utilize the binding sites (pockets) of the proteins as the input for the target protein, and it uses a self-attention mechanism to make the model learn which binding sites of the protein interact with a given ligand. This, indeed, complements the black-box nature of deep learning-based methods and enables interpretability, while achieving state of the art results in drug target interaction prediction task on three datasets. The AttentionSite DTI achieves AUC of 0.97 (for seen proteins), 0.94 (for unseen proteins) in the customized BindingDB dataset, 0.971 in the DUD-E dataset, and 0.991 in the human dataset. In general, the prediction results on these datasets show the superiority of our AttentionSiteDTI compared to previous graph-based models, and our ablation studies proves the effectiveness of our proposed model in prediction of drug-target interactions. In addition, through multidisciplinary collaboration in this work, we further experimentally evaluate the practical potential of our proposed approach. To achieve this, we first computationally predict binding interaction of some candidate compounds with a target protein, and then experimentally validate the binding interactions for these pairs in the laboratory. The high agreement between the computationally-predicted and experimentally observed (measured) drug-target interactions illustrates the potential of our AttentionSiteDTI as effective pre-screening tool in drug repurposing applications.

Monastrol, a 3,4-dihydropyrimidin-2(1 H )-thione, as structural scaffold for the development of modulators for GHB high-affinity binding sites and α 1 β 2 δ GABA A receptors

2017 ◽  
Vol 138 ◽  
pp. 300-312 ◽  
Author(s):  
Maria Damgaard ◽  
Anas Al-Khawaja ◽  
Mia Nittegaard-Nielsen ◽  
Rebekka F. Petersen ◽  
Petrine Wellendorph ◽  
...  
Keyword(s):  
Binding Sites ◽  
Affinity Binding ◽  
High Affinity Binding ◽  
Gaba A ◽  
High Affinity
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