Stability of Ligand-induced Protein Conformation Influences Affinity in Maltose-binding Protein

Our understanding of what determines ligand affinity of proteins is poor, even with high-resolution structures available. Both the non-covalent ligand–protein interactions and the relative free energies of available conformations contribute to the affinity of a protein for a ligand. Distant, non-binding site residues can influence the ligand affinity by altering the free energy difference between a ligand-free and ligand-bound conformation. Our hypothesis is that when different ligands induce distinct ligand-bound conformations, it should be possible to tweak their affinities by changing the free energies of the available conformations. We tested this idea for the maltose-binding protein (MBP) from Escherichia coli. We used single-molecule Förster resonance energy transfer (smFRET) to distinguish several unique ligand-bound conformations of MBP. We engineered mutations, distant from the binding site, to affect the stabilities of different ligand-bound conformations. We show that ligand affinity can indeed be altered in a conformation-dependent manner. Our studies provide a framework for the tuning of ligand affinity, apart from modifying binding site residues.     

Crystallographic and Functional Characterization of the Fluorodifen-inducible Glutathione Transferase from Glycine max Reveals an Active Site Topography Suited for Diphenylether Herbicides and a Novel L-site

Glutathione transferases (GSTs) from the tau class (GSTU) are unique to plants and have important roles in stress tolerance and the detoxification of herbicides in crops and weeds. A fluorodifen-induced GST isoezyme (GmGSTU4-4) belonging to the tau class was purified from Glycine max by affinity chromatography. This isoenzyme was cloned and expressed in Escherichia coli, and its structural and catalytic properties were investigated. The structure of GmGSTU4-4 was determined at 1.75 Å resolution in complex with S-(p-nitrobenzyl)-glutathione (Nb-GSH). The enzyme adopts the canonical GST fold but with a number of functionally important differences. Compared with other plant GSTs, the three-dimensional structure of GmGSTU4-4 primarily shows structural differences in the hydrphobic substrate binding site, the linker segment and the C-terminal region. The X-ray structure identifies key amino acid residues in the hydrophobic binding site (H-site) and provides insights into the substrate specificity and catalytic mechanism of the enzyme. The isoenzyme was highly active in conjugating the diphenylether herbicide fluorodifen. A possible reaction pathway involving the conjugation of glutathione with fluorodifen is described based on site-directed mutagenesis and molecular modeling studies. A serine residue (Ser13) is present in the active site, at a position that would allow it to stabilise the thiolate anion of glutathione and enhance its nucleophilicity. Tyr107 and Arg111 present in the active site are important structural moieties that modulate the catalytic efficiency and specificity of the enzyme, and participate in k_cat regulation by affecting the rate-limiting step of the catalytic reaction. A hitherto undescribed ligand-binding site (L-site) located in a surface pocket of the enzyme was also found. This site is formed by conserved residues, suggesting it may have an important functional role in the transfer and delivery of bound ligands, presumably to specific protein receptors.     

Analysis of non-structural proteins,NSPs of SARS-CoV-2 as targets for computational drug designing

BackgroundThe ORF1ab of Severe Acute Respiratory Syndrome, SARS Corona Virus, SARS-CoV-2 genome is processed into 15 non-structural proteins, NSPs by proteases and each NSP has a specific role in the life cycle and pathogenicity of the virus. This research analyzes possible drugs for these proteins as targets in computational drug designing using already available experimental drugs from the drug bank database.MethodsOut of 471 proteins and 8820 drugs download from Protein Data Bank, PDB and Drug Bank database respectively, 16 proteins similar to NSP 1–15 and 31 drugs as per the “Rule of three” were selected for docking. Out of 88 docking results using PyRx, 18 proteins/chains with three promising drugs, DB01977, DB07132 and DB07535 were analyzed using PyMOL for final results.ResultsNSPs 3, 5, 11, 14 and 15 were identified as targets for the drugs, DB01977, BD07132 and DB07535. Drugs, DB01977 and DB07535 bind in the same binding pockets of NSP 5 and NSP 15. Drug, DB07132 binds with more number of residues when compared with the other two drugs and this indicates that the strength of protein-drug association is more by this drug with the NSPs than other drugs. Binding pockets of NSPs for these three drugs are very close with many sharing residues in common suggesting of similarity of pharmacophore of these drugs with the target binding pockets.ConclusionThe binding pockets of NSPs are well matched with the pharmacophore of drugs and with polar surface of drugs less than or equal to 100 A², drugs, DB01977, DB07132 and DB07535 bind individually and effectively with NSPs 3, 5, 11, 14 and 15 of ORF1ab of SARS-CoV-2 genome to bring changes in the activity of SARS-CoV-2 which may be useful for biological and clinical considerations.     

OdoriFy: A conglomerate of artificial intelligence–driven prediction engines for olfactory decoding

The molecular mechanisms of olfaction, or the sense of smell, are relatively underexplored compared with other sensory systems, primarily because of its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors (ORs) allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a Web server featuring powerful deep neural network–based prediction engines. OdoriFy enables (1) identification of odorant molecules for wildtype or mutant human ORs (Odor Finder); (2) classification of user-provided chemicals as odorants/nonodorants (Odorant Predictor); (3) identification of responsive ORs for a query odorant (OR Finder); and (4) interaction validation using Odorant–OR Pair Analysis. In addition, OdoriFy provides the rationale behind every prediction it makes by leveraging explainable artificial intelligence. This module highlights the basis of the prediction of odorants/nonodorants at atomic resolution and for the ORs at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human ORs with their known agonists and nonagonists, making it a highly interactive and resource-enriched Web server. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.     

Fluorine-18 (18F)-labeled retinoid x receptor (RXR) partial agonist whose tissue transferability is affected by other RXR ligands

Bexarotene (1), a retinoid X receptor (RXR) agonist approved for the treatment of cutaneous T cell lymphoma (CTCL), was reported to migrate into baboon brain based on findings obtained by positron emission tomography (PET) with a ¹¹C-labeled tracer. However, co-administration of non-radioactive 1 had no effect on the distribution of [¹¹C]1, probably due to non-specific binding of 1 as a result of its high lipophilicity. Here, we report a fluorine-18 (¹⁸F)-labeled PET tracer [¹⁸F]6 derived from RXR partial agonist CBt-PMN (2), which has lower lipophilicity and weaker RXR-binding ability than [¹¹C]1. The concomitant administration of 1 or 2 with [¹⁸F]6 with resulted in decreased accumulation of [¹⁸F]6 in liver, together with increased brain uptake and increased accumulation in kidney and muscle, as visualized by PET. A plausible explanation of these findings is the inhibition of [¹⁸F]6 uptake into the liver by concomitantly administered 1 or 2, leading to an increase in blood concentration of [¹⁸F]6 followed by increased accumulation in other tissues.     

Binding Studies of TNF Receptor Superfamily (TNFRSF) Receptors on Intact Cells

Ligands of the tumor necrosis factor superfamily (TNFSF) interact with members of the TNF receptor superfamily (TNFRSF). TNFSF ligand-TNFRSF receptor interactions have been intensively evaluated by many groups. The affinities of TNFSF ligand-TNFRSF receptor interactions are highly dependent on the oligomerization state of the receptor, and cellular factors (e.g. actin cytoskeleton and lipid rafts) influence the assembly of ligand-receptor complexes, too. Binding studies on TNFSF ligand-TNFRSF receptor interactions were typically performed using cell-free assays with recombinant fusion proteins that contain varying numbers of TNFRSF ectodomains. It is therefore not surprising that affinities determined for an individual TNFSF ligand-TNFRSF interaction differ sometimes by several orders of magnitude and often do not reflect the ligand activity observed in cellular assays. To overcome the intrinsic limitations of cell-free binding studies and usage of recombinant receptor domains, we performed comprehensive binding studies with Gaussia princeps luciferase TNFSF ligand fusion proteins for cell-bound TNFRSF members on intact cells at 37 °C. The affinities of the TNFSF ligand G. princeps luciferase-fusion proteins ranged between 0.01 and 19 nm and offer the currently most comprehensive and best suited panel of affinities for in silico studies of ligand-receptor systems of the TNF family.     

Interactions between alpha-conotoxin MI and the Torpedo marmorata receptor alpha-delta interface

The muscle-type nicotinic receptor has two distinguishable acetylcholine binding sites at the alpha-gamma and alpha-delta subunit interfaces; alpha-conotoxins can bind them selectively. Moreover, we previously reported that alpha-conotoxin MI can interact with Torpedo californica and Torpedo marmorata receptors showing that conotoxins can also detect receptors from different species of the same genus [L. Cortez, S.G. del Canto, F. Testai, M.B. de Jiménez Bonino, Conotoxin MI inhibits the acetylcholine binding site of the Torpedo marmorata receptor, Biochem. Biophys. Res. Commun. 295 (2002) 791-795]. Herein, to identify T. marmorata receptor regions involved in alpha-conotoxin MI binding, a photoactivatable reagent was used and labeled sites were mapped by enzymatic proteolysis, MALDI-TOF-MS and Edman degradation. alpha-Conotoxin MI binding determinants were found and studies revealed a second binding motif at the alpha/delta interface. A proposal for receptor-toxin interaction is discussed based on experimental results and docking studies.