Search

An attractive drug target to combat COVID‐19 is the main protease (Mpro) because of its key role in the viral life cycle by processing the polyproteins translated from the viral RNA. Studying the crystal structures of the protease is important to enhance our understanding of its mechanism of action at the atomic‐level resolution, and consequently may provide crucial structural insights for structure‐based drug discovery. In the current study, we report a comparative structural analysis of the Mpro substrate binding site for both apo and holo forms to identify key interacting residues and conserved water molecules during the ligand‐binding process. It is shown that in addition to the catalytic dyad residues (His41 and Cys145), the oxyanion hole residues (Asn142–Ser144) and residues His164–Glu166 form essential parts of the substrate‐binding pocket of the protease in the binding process. Furthermore, we address the issue of the substrate‐binding pocket flexibility and show that two adjacent loops in the Mpro structures (residues Thr45–Met49 and Arg188–Ala191) with high flexibility can regulate the binding cavity' accessibility for different ligand sizes. Moreover, we discuss in detail the various structural and functional roles of several important conserved and mobile water molecules within and around the binding site in the proper enzymatic function of Mpro. We also present a new docking protocol in the framework of the ensemble docking strategy. The performance of the docking protocol has been evaluated in predicting ligand binding pose and affinity ranking for two popular docking programs; AutoDock4 and AutoDock Vina. Our docking results suggest that the top‐ranked poses of the most populated clusters obtained by AutoDock Vina are the most important representative docking runs that show a very good performance in estimating experimental binding poses and affinity ranking. [ABSTRACT FROM AUTHOR]

Antibody-based drugs can be highly toxic, because they target normal tissue as well as tumor tissue. The pH value of the extracellular microenvironments around tumor tissues is lower than that of normal tissues. Therefore, antibodies that engage in pH-dependent binding at slightly acidic pH are crucial for improving the safety of antibody-based drugs. Thus, we implemented a stepwise mutagenesis approach to engineering pH-dependent antibodies capable of selective binding in the acidic microenvironment in this study. The first step involved single-residue histidine scanning mutagenesis of the antibody's complementarity-determining regions to prescreen for pH-dependent mutants and identify ionizable sensitive hot-spot residues that could be substituted by acidic amino acids to obtain pH-dependent antibodies. The second step involved single-acidic amino acid residue substitutions of the identified residues and the assessment of pH-dependent binding. We identified six ionizable sensitive hot-spot residues using single-histidine scanning mutagenesis. Nine pH-dependent antibodies were isolated using single-acidic amino acid residue mutagenesis at the six hot-spot residue positions. Relative to wild-type anti-CEA chimera antibody, the binding selectivity of the best performing mutant was improved by approximately 32-fold according to ELISA and by tenfold according to FACS assay. The mutant had a high affinity in the pH range of 5.5–6.0. This study supports the development of pH-dependent protein switches and increases our understanding of the role of ionizable residues in protein interfaces. The stepwise mutagenesis approach is rapid, general, and robust and is expected to produce pH-sensitive protein affinity reagents for various applications. [ABSTRACT FROM AUTHOR]

Antibody-based drugs can be highly toxic, because they target normal tissue as well as tumor tissue. The pH value of the extracellular microenvironments around tumor tissues is lower than that of normal tissues. Therefore, antibodies that engage in pH-dependent binding at slightly acidic pH are crucial for improving the safety of antibody-based drugs. Thus, we implemented a stepwise mutagenesis approach to engineering pH-dependent antibodies capable of selective binding in the acidic microenvironment in this study. The first step involved single-residue histidine scanning mutagenesis of the antibody's complementarity-determining regions to prescreen for pH-dependent mutants and identify ionizable sensitive hot-spot residues that could be substituted by acidic amino acids to obtain pH-dependent antibodies. The second step involved single-acidic amino acid residue substitutions of the identified residues and the assessment of pH-dependent binding. We identified six ionizable sensitive hot-spot residues using single-histidine scanning mutagenesis. Nine pH-dependent antibodies were isolated using single-acidic amino acid residue mutagenesis at the six hot-spot residue positions. Relative to wild-type anti-CEA chimera antibody, the binding selectivity of the best performing mutant was improved by approximately 32-fold according to ELISA and by tenfold according to FACS assay. The mutant had a high affinity in the pH range of 5.5–6.0. This study supports the development of pH-dependent protein switches and increases our understanding of the role of ionizable residues in protein interfaces. The stepwise mutagenesis approach is rapid, general, and robust and is expected to produce pH-sensitive protein affinity reagents for various applications. [ABSTRACT FROM AUTHOR]

Background: The SARS-CoV-2 has led to a worldwide disaster. Thus, developing prophylactics/therapeutics is required to overcome this public health issue. Among these, producing the anti-SARS-CoV-2 single-chain variable fragment (scFv) antibodies has attracted a significant attention. Accordingly, this study aims to address this question: Is it possible to bioinformatics-based design of a potent anti-SARS-CoV-2 scFv as an alternative to current production approaches?, Method: Using the complexed SARS-CoV-2 spike-antibodies, two sets analyses were performed: (1) B-cell epitopes (BCEs) prediction in the spike receptor-binding domain (RBD) region as a parameter for antibody screening; (2) the computational analysis of antibodies variable domains (V H /V L ). Based on these primary screenings, and docking/binding affinity rating, one antibody was selected. The protein-protein interactions (PPIs) among the selected antibody-epitope complex were predicted and its epitope conservancy was also evaluated. Thereafter, some elements were added to the final scFv: (1) the PelB signal peptide; (2) a GSGGGGS linker to connect the V H -V L . Finally, this scFv was analyzed/optimized using various web servers., Results: Among the antibody library, only one met the various criteria for being an efficient scFv candidate. Moreover, no interaction was predicted between its paratope and RBD hot-spot residues of SARS-CoV-2 variants-of-Concern (VOCs)., Conclusions: Herein, a step-by-step bioinformatics platform has been introduced to bypass some barriers of traditional antibody production approaches. Based on existing literature, the current study is one of the pioneer works in the field of bioinformatics-based scFv production. This scFv may be a good candidate for diagnostics/therapeutics design against the SARS-CoV-2 as an emerging aggressive pathogen., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The response regulator PhoP is part of the PhoP/PhoQ two-component system, which is responsible for regulating the expression of multiple genes involved in controlling virulence, biofilm formation, and resistance to antimicrobial peptides. Therefore, modulating the transcriptional function of the PhoP protein is a promising strategy for developing new antimicrobial agents. There is evidence suggesting that phosphorylation-mediated dimerization in the regulatory domain of PhoP is essential for its transcriptional function. Disruption or stabilization of protein-protein interactions at the dimerization interface may inhibit or enhance the expression of PhoP-dependent genes. In this study, we performed molecular dynamics simulations on the active and inactive dimers and monomers of the PhoP regulatory domains, followed by pocket-detecting screenings and a quantitative hot-spot analysis in order to assess the druggability of the protein. Consistent with prior hypothesis, the calculation of the binding free energy shows that phosphorylation enhances dimerization of PhoP. Furthermore, we have identified two different putative binding sites at the dimerization active site (the α4-β5-α5 face) with energetic 'hot-spot' areas, which could be used to search for modulators of protein-protein interactions. This study delivers insight into the dynamics and druggability of the dimerization interface of the PhoP regulatory domain, and may serve as a basis for the rational identification of new antimicrobial drugs. [ABSTRACT FROM AUTHOR]

The function of the p53 protein is impaired by the overexpression of its negative regulator murine double minute 2 protein (MDM2) and homologous protein MDMX. Disruption of the p53-MDM2/MDMX interaction to restore the transcriptional function of p53 is considered a promising strategy for cancer therapy. To design dual MDM2/MDMX inhibitors, the binding modes of MDM2 or MDMX with their inhibitors are elucidated. Several hot-spot residues of MDM2 or MDMX are identified by molecular dynamics simulations, alanine scanning and MM-GBSA calculations. Then, focusing on the interaction with hot-spot residues, two series of derivatives bearing 1,3-diketone and α-aminoketone scaffolds are designed and synthesized. Among these compounds, C16 is identified as the most potent compound with low micromolar binding affinities with MDM2 and MDMX. C16 also displays moderate antiproliferative activities against MDM2-overexpressing and MDMX-overexpressing cells, with IC 50 values of 0.68 μM in HCT116 cells and 0.54 μM in SH-SY5Y cells. Furthermore, C16 inhibits cell migration and invasion, reactivates the function of p53, arrests the cell cycle and induces cellular apoptosis in HCT116 and SH-SY5Y cells. Collectively, C16 can be developed as a dual MDM2 and MDMX inhibitor for cancer therapy. [Display omitted] • A series of p53-MDM2/MDMX dual inhibitors bearing α-aminoketone was designed. • C16 possessed low micromolar binding affinities with MDM2 and MDMX. • C16 demonstrated moderate anti-proliferative activities against HCT116 and SH-SY5Y cells. • C16 recovered the p53 function, and induced cell-cycle arrest and apoptosis. [ABSTRACT FROM AUTHOR]

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein-protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein-protein interactions, or providing modeled structural data for drug discovery targeting protein-protein interactions., (© 2018 Elsevier Inc. All rights reserved.)