Current Research in Structural Biology最新文献

Research is continuously being pursued to treat cancer patients and prevent the disease by developing new medicines. However, experimental drug design and development is a costly, time-consuming, and challenging process. Alternatively, computational and mathematical techniques play an important role in optimally achieving this goal. Among these mathematical techniques, topological indices (TIs) have many applications in the drugs used for the treatment of breast cancer. TIs can be utilized to forecast the effectiveness of drugs by providing molecular structure information and related properties of the drugs. In addition, these can assist in the design and discovery of new drugs by providing insights into the structure-property/structure-activity relationships. In this article, a Quantitative Structure Property Relationship (QSPR) analysis is carried out using some novel degree-based molecular descriptors and regression models to predict various properties (such as boiling point, melting point, enthalpy, flashpoint, molar refraction, molar volume, and polarizability) of 14 drugs used for the breast cancer treatment. The molecular structures of these drugs are topologically modeled through vertex and edge partitioning techniques of graph theory, and then linear regression models are developed to correlate the computed values with the experimental properties of the drugs to investigate the performance of TIs in predicting these properties. The results confirmed the potential of the considered topological indices as a tool for drug discovery and design in the field of breast cancer treatment.

G-quadruplexes (G4s) are reported to present on the SARS-CoV-2 RNA genome and control various viral activities. Specific ligands targeting those viral nucleic acid structures could be investigated as promising detection methods or antiviral reagents to suppress this menacing virus. Herein, we demonstrate the binding between a G4 structure in the RNA of SARS-CoV-2 and a fluorescent probe created by fusing a parallel-G4 specific RHAU53 and a cyan fluorescent protein. The specific binding of G4 in SARS-CoV-2 by RHAU peptide was easily detected under the fluorescence spectrometer. The drawbacks of this approach and potential solutions are also discussed.

The EGFR-C797S resistance mutation to third-generation drugs has been overcome by fourth-generation inhibitors, allosteric inhibitors, namely EAI045 and has reached phase 3 clinical trials, so the Allosteric Site is currently an attractive target for development. In this study, researchers are interested in knowing the activity of metabolite compounds from marine natural ingredients Clathria Sp. against the Allosteric Site of EGFR computationally. The methods used include molecular docking using Autodock4 software and Molecular Dynamics simulation performed using GROMACS software. The research began with the preparation of metabolite samples from Clathria Sp. through the KnapSack database site and the preparation of EGFR receptors that have been complexed with allosteric inhibitors, namely proteins with PDB code 5D41. Each compound was docked to the Allosteric Site of the natural ligand and then molecular dynamics simulations were performed on the compound with the best docking energy compared to the natural ligand. From the docking results, the Clathrin_A compound showed the lowest binding energy compared to other metabolites, and the value was close to the natural ligand. Then from the molecular dynamics results, the clathrin_A compound shows good stability and resembles the natural ligand, which is analyzed through RMSD, RMSF, SASA, Rg, and PCA, and shows the binding free energy from MMPBSA analysis which is close to the natural ligand. It can be concluded, Clathrin_A compound has potential as an allosteric inhibitor.

Nowadays, one of the methods to prevent the progress of Alzheimer's disease (AD) is to prescribe compounds that inhibit the acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzymes. Researchers are actively pursuing compounds, particularly of natural origin, that exhibit enhanced efficacy and reduced side effects. The inhibition of AChE and BChE using natural flavonoids represents a promising avenue for regulating AD. This study aims to identify alternative flavonoids capable of modulating AD by down-regulating AChE and BChE activity through a molecular docking approach. Molecular docking analysis identified Ginkgetin and Kolaflavanone as potent inhibitors of AChE and BChE, respectively, among the selected flavonoids. Asn87 and Ala127 involved in the interactions of AChE-Ginkgetin complex through conventional hydrogen bonds. While in the BChE-Kolaflavanone complex, Asn83, Ser79, Gln 47, and Ser287 are involved. In vitro analysis further corroborated the inhibitory potential, with Ginkgetin exhibiting an IC₅₀ of 3.2 mM against AChE, and Kolaflavanone displaying an IC₅₀ of 3.6 mM against BChE. These findings underscore the potential of Ginkgetin and Kolaflavanone as candidate inhibitors for the treatment of AD through the inhibition of AChE and BChE enzymes. Nevertheless, additional in vitro and in vivo studies are imperative to validate the efficacy of these compounds.

The mechanisms behind Concanavalin A (ConA) circular permutation have been under investigation since 1985. Although a vast amount of information is available about this lectin and its applications, the exact purpose of its processing remains unclear. To shed light on this, this study employed computer simulations to compare the unprocessed ProConA with the mature ConA. This approach aimed to reveal the importance of the post-translational modifications, especially how they affect the lectin stability and carbohydrate-binding properties. To achieve these goals, we conducted 200 ns molecular dynamics simulations and trajectory analyses on the monomeric forms of ProConA and ConA (both unbound and in complex with D-mannose and the GlcNAc2Man9 N-glycan), as well as on their oligomeric forms. Our findings reveal significant stability differences between ProConA and ConA at both the monomeric and tetrameric levels, with ProConA exhibiting consistently lower stability parameters compared to ConA. In terms of carbohydrate binding properties, however, both lectins showed remarkable similarities in their interaction profiles, contact numbers, and binding free energies with D-mannose and the high-mannose N-glycan. Overall, our results suggest that the processing of ProConA significantly enhances the stability of the mature lectin, especially in maintaining the tetrameric oligomer, without substantially affecting its carbohydrate-binding properties.

A lattice-based method is presented for creating 3D models of entire bacterial nucleoids integrating ultrastructural information cryoelectron tomography, genomic and proteomic data, and experimental atomic structures of biomolecules and assemblies. The method is used to generate models of the minimal genome bacterium JCVI-Syn3A, producing a series of models that test hypotheses about transcription, condensation, and overall distribution of the genome. Lattice models are also used to generate atomic models of an entire JCVI-Syn3A cell.

The COVID-19 pandemic in the later phase showed the presence of the B.1.1.529 variant of the SARS-CoV-2 designated as Omicron. AYUSH-64 a poly herbal drug developed by Central Council for Research in Ayurvedic Sciences (CCRAS) has been recommended by Ministry of Ayush in asymptomatic, mild to moderate COVID-19 patients. One of the earlier, in-silico study has shown the binding of the constituents of AYUSH-64 to the main protease (M^pro) of the SARS-CoV-2. This study enlisted four phytochemicals of AYUSH-64, which were found to have significant binding with the M^pro. In continuation to the same, the current study proposes to understand the binding of these four phytochemicals to main protease (M^pro) and receptor binding domain (RBD) of spike protein of the Omicron variant. An enhanced molecular docking methodology, namely, ensemble docking has been used to find the most efficiently binding phytochemical. Using molecular dynamics (MD) simulations and clustering approach it was observed that the M^pro and RBD Spike of Omicron variant of SARS-CoV-2 in complex with human ACE2 tends to attain 4 and 8 conformational respectively. Based on the docking studies, the best binding phytochemical of the AYUSH-64, akummicine N-oxide was selected for MD simulations. MD simulations of akummicine N-oxide bound to omicron variant of M^pro and RBD Spike-ACE complex was performed. The conformational, interaction and binding energy analysis suggested that the akummicine N-oxide binds well with M^pro and RBD Spike-ACE2 complex. The interaction between RBD Spike and ACE2 was observed to weaken in the presence of akummicine N-oxide. Hence, it can be inferred that, these phytochemicals from AYUSH-64 formulation may have the potential to act against the Omicron variant of SARS-CoV-2.

The 21st amino acid, selenocysteine (Sec), is synthesized on its dedicated transfer RNA (tRNA^Sec). In bacteria, Sec is synthesized from Ser-tRNA^[Ser]Sec by Selenocysteine Synthase (SelA), which is a pivotal enzyme in the biosynthesis of Sec. The structural characterization of bacterial SelA is of paramount importance to decipher its catalytic mechanism and its role in the regulation of the Sec-synthesis pathway. Here, we present a comprehensive single-particle cryo-electron microscopy (SPA cryoEM) structure of the bacterial SelA with an overall resolution of 2.69 Å. Using recombinant Escherichia coli SelA, we purified and prepared samples for single-particle cryoEM. The structural insights from SelA, combined with previous in vivo and in vitro knowledge, underscore the indispensable role of decamerization in SelA's function. Moreover, our structural analysis corroborates previous results that show that SelA adopts a pentamer of dimers configuration, and the active site architecture, substrate binding pocket, and key K295 catalytic residue are identified and described in detail. The differences in protein architecture and substrate coordination between the bacterial enzyme and its counterparts offer compelling structural evidence supporting the independent molecular evolution of the bacterial and archaea/eukarya Ser-Sec biosynthesis present in the natural world.

Alzheimer's disease (AD) leads to gradual memory loss including other compromised cognitive abilities. Acetylcholinesterase (AChE), an important biochemical enzyme from the cholinesterase (ChE) family, is recognized as primary pharmacological target for treating AD. Currently marketed drugs for AD treatment are primarily AChE inhibitors and coumarin derivatives comprising a wide variety of pharmacological activities have proved their efficacy towards AChE inhibition. Ensaculin (KA-672 HCl), a compound that belong to the coumarin family, is a clinical trial candidate for AD treatment. Therefore, a ligand library was prepared with 60 reported coumarin derivatives for field-based 3D-QSAR and pharmacophore modelling. The field-based 3D-QSAR model obtained at partial least square (PLS) factor 7, was the best validated model that predicted activity closer to original activity for each ligand introduced. The contour maps demonstrated spatial distribution of favourable and unfavorable steric, hydrophobic, electrostatic and H-bond donor and acceptor contours around coumarin nucleus. The best pharmacophore model, ADHRR_1 exhibited five essential pharmacophoric features of four different traits for optimum AChE inhibition. Virtual screening through ADHRR_1 accompanied with molecular docking and MM/GBSA identified 10 HITs from a 4,00,000 coumarin derivatives from PubChem database. HITs comprised docking scores ranging from −12.096 kcal/mol to −8.271 kcal/mol and compared with the reference drug Donepezil (-8.271 kcal/mol). ADME properties analysis led into detecting two leads (HIT 1 and HIT 2) among these 10 HITs. Molecular Dynamics Simulation indicated thermodynamic stability of the complex of lead compounds with AChE protein. Finally, thorough survey of the experimental results from 3D-QSAR modelling, pharmacophore modelling and molecular docking interactions led us to develop the lead formula I for future advancements in treating AD through AChE inhibitors.