Novel Degree-Based Topological Descriptors of Fenofibrate Using M-Polynomial

Chemical graph theory is currently expanding the use of topological indices to numerically encode chemical structure. The prediction of the characteristics provided by the chemical structure of the molecule is a key feature of these topological indices. The concepts from graph theory are presented in a brief discussion of one of its many applications to chemistry, namely, the use of topological indices in quantitative structure-activity relationship (QSAR) studies and quantitative structure-property relationship (QSPR) studies. This study uses the M-polynomial approach, a newly discovered technique, to determine the topological indices of the medication fenofibrate. With the use of degree-based topological indices, we additionally construct a few novel degree based topological descriptors of fenofibrate structure using M-polynomial. When using M-polynomials in place of degree-based indices, the computation of the topological indices can be completed relatively quickly. The topological indices are also plotted. Using M-polynomial, we compute novel formulas for the modified first Zagreb index, modified second Zagreb index, first and second hyper Zagreb indices, SK index, SK1 index, SK2 index, modified Albertson index, redefined first Zagreb index, and degree-based topological indices.

Current Evidence of Cannabidiol's Potential as an Anxiolytic Agent

This article will summarize the current knowledge and scientific evidence regarding cannabidiol as a possible pharmacological tool for anxiety disorders. Although the use of this substance in medical practice is gaining momentum, gaps can still be found in the current knowledge regarding its molecular targets, drug-to-drug interactions, efficacy in different populations, adequate dosage, duration of treatment, and correct formulation. Moreover, current evidence is still preliminary, lacking robust, blinded, and placebo-controlled clinical trials in many areas of investigation. After reading this article, readers should have a thorough understanding of the current scientific evidence regarding the use of CBD as an anxiolytic drug. [Psychiatr Ann. 2023;53(6):242–246.]

Safety Profile of Molnupiravir with Significant Effect on COVID-19: A Review

Background: As the COVID era unfolds, researchers reveal that rapid changes in viral genetic material allow viruses to circumvent challenges triggered by the host immune system and resist anti-viral drugs, potentially leading to persistent viral manifestations in host cells. Molnupiravir (RNA-dependent RNA polymerase inhibitor) is a novel anti-viral medicine promising a vital role in coming setbacks.Objectives: This review aims to clarify the safety and efficacy of the molnupiravir molecule in light of existing case studies. As a result, it is intended to explore and discuss the molecular structure, mechanism of action, discovery and development process, preclinical research, clinical investigations, and other subtopics.Methods: A total of 75 publications were searched using multiple engines, such as Google Scholar, PubMed, Web of Science, Embase, Cochrane Library, ClinicalTrials.gov, and others, with a constraint applied to exclude publications published over 11 years ago. Molnupiravir, safety, efficacy, COVID- 19, RdRp, PK-PD, and clinical study were utilized as keywords.Results: Clinical results on molnupiravir are supported by investigations that were recently disclosed in a study on both sex volunteers (male and female) with an age restriction of 19 to 60 years, followed by a Phase-3 Clinical Trial (NCT04575584) with 775 randomly assigned participants and no fatalities reported due to treatment.Conclusion: Molnupiravir proved a high level of safety, allowing it to be tested further. This review supports the safety and efficacy of this molecule based on the established evidence, which claims the most anticipated employment of molnupiravir in COVID protocol.

On Analysis of Temperature Based Topological Indices of Some Covid-19 Drugs

A variety of graphical invariants have been described and tested, offering lots of applications in the fields of nanochemistry, computational networks and in different scientific research areas. One commonly studied group of invariants is the topological index, which allows to research the chemical, biological, and physical properties of a chemical structure. Topological indexes are numerical quantities that can be used to describe the properties of the molecular graph. In this article, we draw from the analytically closed formulas of certain molecular structures of coronavirus such as Ribavirin, Sofosbuvir and Oseltamivir by calculating temperature based topological indices.

PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning

Of fundamental importance in biochemical and biomedical research is understanding a molecule's biological properties—its structure, its function(s), and its activity(ies). To this end, computational methods in Artificial Intelligence, in particular Deep Learning (DL), have been applied to further biomolecular understanding—from analysis and prediction of protein–protein and protein–ligand interactions to drug discovery and design. While choosing the most appropriate DL architecture is vitally important to accurately model the task at hand, equally important is choosing the features used as input to represent molecular properties in these DL models. Through hypothesis testing, bioinformaticians have created thousands of engineered features for biomolecules such as proteins and their ligands. Herein we present an organizational taxonomy for biomolecular features extracted from 808 articles from across the scientific literature. This objective view of biomolecular features can reduce various forms of experimental and/or investigator bias and additionally facilitate feature selection in biomolecular analysis and design tasks. The resulting dataset contains 1360 nondeduplicated features, and a sample of these features were classified by their properties, clustered, and used to suggest new features. The complete feature dataset (the Public Repository of Engineered Features for Molecular Deep Learning, PREFMoDeL) is released for collaborative sourcing on the web.

Chemoinformatics and bioinformatics by discrete mathematics and numbers: an adventure from small data to the realm of emerging big data

Currently, we are witnessing the emergence of big data in various fields including the biomedical and natural sciences. The size of chemoinformatics and bioinformatics databases is increasing every day. This gives us both challenges and opportunities. This chapter discusses the mathematical methods used in these fields both for the generation and analysis of such data. It is emphasized that proper use of robust statistical and machine learning methods in the analysis of the available big data may facilitate both hypothesis-driven and discovery-oriented research. © 2023 Elsevier Inc. All rights reserved.

Analysis of the Antiviral Drugs in Lanzhou index using M Polynomial

Analysis of the physical-chemical characteristics of chemical structures requires the incorporation of topological indices into account. A structure-based approach that concentrates antiviral treatment on the main protease of the SARS-COV-2 virus. One degree-based measure that is a little comparable to the Zagreb index and Lanzhou index. This paper analyzed the effectiveness of the chemical compounds pyroxene, amphibole, detour-saturated plants, and several antiviral medications listed for the Lanzhou index. The obtained results demonstrate a significant relationship between the Lanzhou index under investigation and the physicochemical features of potential antiviral medications. The Lanzhou index for antioxidant drugs for M polynomial is enumerated. © 2023 Taylor & Francis Group, LLC.

Structure Analysis and Molecular Simulation Study of ACC Deaminase Mutants from Pseudomonas sp., an Endophyte in Reducing Abiotic Stress in Plants

[...]heme-copper oxidases from Rhodobacter sphaeroides underwent single amino acid substitutions, including N139D, which resulted in the elimination of proton pumping activity and an increase in steady-state activity rather than an inhibitory effect (Pawate et al. 2002). [...]ACC deaminase must begin the cyclopropane ring opening reaction without the availability of an R-carbanionic intermediate. [...]ACC deaminase from Pseudomonas sp. Site-specific docking with ACC was performed for each mutated structure, and the T199S mutant produced binding energy of -4.3 kcal/mol, whereas the E295G mutant was found to have a binding energy of -4.9 kcal/mol. [...]the E295G mutant, in which glutamic acid is substituted with glycine at the 295th position, was chosen

Synthesis, characterization, computational analyses, in silico ADMET studies, and inhibitory action against SARS-CoV-2 main protease (Mpro) of a Schiff base

COVID-19 disease caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has struck the whole world and raised severe health, economic, and social problems. Many scientists struggled to find a vaccine or an antiviral drug. Eventually, both vaccines and recommended drugs, repurposed drugs, or drug combinations were found, but new strains of SARS-CoV-2 continue to threaten human life and health. As part of the fight against COVID-19 disease, this study involves an in silico molecular docking analysis on the main protease (Mpro) of SARS-CoV-2. To this aim, a Schiff base compound was synthesized and characterized using spectroscopic techniques, including X-ray, FTIR, and UV-Vis. Surface analysis and electronic properties of this molecule were investigated using the DFT method. The drug-likeness parameters of the title compound were studied according to the rules of Lipinski, Veber, Ghose, Egan, and Muegge and were found in agreement with these rules. In silico toxicity analyses revealed that the new compound is a potentially mutagenic and carcinogenic chemical. The title compound was predicted to be an inhibitor of cytochrome P450 enzymes (5 CYPs). This inhibitory effect indicates a weak metabolism of the molecule in the liver. In addition, this compound was displayed good intestinal absorption and blood-brain barrier penetration. The druggability properties of the title compound were investigated, and Swiss Target Prediction predicted it to be a protease inhibitor. In this context, the SARS-CoV-2 main protease was selected as a biological target in molecular docking studies. Docking results were compared with the known native ligand N3 inhibitor. The value of binding energy between the Schiff base compound and the binding pocket of the main protease is higher than that of the reference ligand N3. The calculated free energies of binding of the Schiff base compound and the reference ligand N3 are -8.10 and -7.11 kcal/mol, respectively.

Chapter 26 - Phase separation and infectious diseases

Infectious diseases continue to represent a major threat to the humankind. This is reiterated by the current COVID-19 pandemic that affected almost 550 million people worldwide and caused more than 6.35 million deaths. It is clear that in addition to the existing preventive measures and treatments for various pathogens, better understanding is needed of the relationship between pathogen infection and the human antiinfection immune response and of the specific mechanisms underlying these complex processes. There is a constant warfare between the hosts and infectious pathogens, where humans have evolved a very effective and broadly amended antiinfection immune system, but, in their turn, pathogens have evolved a multitude of immune escape mechanisms to efficiently oppose it. It is recognized now that liquid–liquid phase separation (LLPS) occupies a special place among the important molecular mechanisms of the antiinfection immune response. Some illustrative examples of the roles of LLPS in the antiinfection immune response are considered in this chapter.

Synthesis, Characterization, Biological Activity, Molecular Docking and Thermal Analysis of Binuclear Complexes

Because of their potential medical applications as antimicrobial medicines, metal-ligand complexes have sparked a lot of attention. Synthesis and characterization of various metal-diamine complexes were the goals of the research detailed in this paper. As a result, synthesis of new binuclear ligand;N,N'-bis(3-carboxysalcylidene)-4-chloro-1,2-phenylenediamine (H(4)fsacph) which is derivative from the condensation of 3-formyl-2-hydroxybenzoic acid and 4-chlorobenzene-1,2-diamine has been performed. The new synthesized ligand has formed a mononuclear complex with Cu(II). The mononuclear Cu(II) complex was used to form binary nuclear complexes with some metal ions, like Cr(III), Mn(II), Fe(III), Ru(III), Pd(II) and La(III) ions. Elemental analysis, IR, UV-Visible, and thermal analyzes have been used to characterize the complexes. The interaction of a ligand with the receptors of Candida albicans and SARS-CoV-2 was predicted using molecular docking. Toward bacteria and fungi showing predominant activity against all fungi verified antibacterial activity of the synthesized complexes, while they have almost no activity against all bacteria. All compounds have shown antibacterial and antifungal activities, but the metal complexes showed better activities as compared to the original ligands, especially all zinc(II) complexes. The above results suggest that both ligands and their metal complexes have the potential to be explored as active pharmaceutical agents.

From COVID-19 to the Central Dogma: Investigating the SARS-CoV-2 Spike Protein

Students often struggle with visualizing protein structures when working with two-dimensional textbook and lecture materials, so introducing them to 3D visualization software developed by and for structural biologists offers them a unique opportunity to work with authentic data while furthering their spatial reasoning skills and understanding of molecular structure and function. This article presents an active learning virtual laboratory in which students use authentic structural biology data to investigate the effects of both hypothetical and real-world SARS-CoV-2 mutations on the virus’s ability to bind to human ACE2 receptors and infect a host, causing COVID-19. Through this activity, introductory-level college students or advanced high school students gain a better understanding of applied biology, such as how vaccines and treatments are designed, as well as strengthening their understanding of core disciplinary concepts, such as the relationship between protein structure and function and the central dogma of molecular biology. While there were challenges during the pilot phase of activity development due to COVID-19 restrictions, students in the pilot groups came away from the activity with deeper understanding of the relationship between proteins and amino acid sequences and a new appreciation for the ways researchers design treatments for and study viruses.

Trimeric receptor-binding domain of SARS-CoV-2 acts as a potent inhibitor of ACE2 receptor-mediated viral entry.

The COVID-19 pandemic has caused over four million deaths and effective methods to control CoV-2 infection, in addition to vaccines, are needed. The CoV-2 binds to the ACE2 on human cells through the receptor-binding domain (RBD) of the trimeric spike protein. Our modeling studies show that a modified trimeric RBD (tRBD) can interact with three ACE2 receptors, unlike the native spike protein, which binds to only one ACE2. We found that tRBD binds to the ACE2 with 58-fold higher affinity than monomeric RBD (mRBD) and blocks spike-dependent pseudoviral infection over 4-fold more effectively compared to the mRBD. Although mRBD failed to block CoV-2 USA-WA1/2020 infection, tRBD efficiently blocked the true virus infection in plaque assays. We show that tRBD is a potent inhibitor of CoV-2 through both competitive binding to the ACE2 and steric hindrance, and has the potential to emerge as a first-line therapeutic method to control COVID-19.

QSPR Modeling with Topological Indices of Some Potential Drug Candidates against COVID-19

COVID-19, which has spread all over the world and was declared as a pandemic, is a new disease caused by the coronavirus family. There is no medicine yet to prevent or end this pandemic. Even if existing drugs are used to alleviate the pandemic, this is not enough. Therefore, combinations of existing drugs and their analogs are being studied. Vaccines produced for COVID-19 may not be effective for new variants of this virus. Therefore, it is necessary to find the drugs for this disease as soon as possible. Topological indices are the numerical descriptors of a molecular structure obtained by the molecular graph. Topological indices can provide information about the physicochemical properties and biological properties of molecules in the quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies. In this paper, some analogs of lopinavir, favipiravir, and ritonavir drugs that have the property of being potential drugs against COVID-19 are studied. QSPR models are studied using linear and quadratic regression analysis with topological indices for enthalpy of vaporization, flash point, molar refractivity, polarizability, surface tension, and molar volume properties of these analogs.

Structural and functional analysis of an inter-Spike bivalent neutralizing antibody against SARS-CoV-2 variants.

The different variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have attracted most public concern because they caused "wave and wave" COVID-19 pandemic. The initial step of viral infection is mediated by the SARS-CoV-2 Spike (S) protein, which mediates the receptor recognition and membrane fusion between virus and host cells. Neutralizing antibodies (nAbs) targeting the S protein of SARS-CoV-2 have become promising candidates for clinical intervention strategy, while multiple studies have shown that different variants have enhanced infectivity and antibody resistance. Here, we explore the structure and function of STS165, a broadly inter-Spike bivalent nAb against SARS-CoV-2 variants and even SARS-CoV, contributing to further understanding of the working mechanism of nAbs.

Tailoring Resonant Energy Transfer Processes for Sustainable and Bio-Inspired Sensing

Dipole–Dipole interactions (DDI) constitute an effective mechanism by which two physical entities can interact with each other. DDI processes can occur in a resonance framework if the energies of the two dipoles are very close. In this case, an energy transfer can occur without the need to emit a photon, taking the name of Förster Resonance Energy Transfer (FRET). Given their large dependence on the distance and orientation between the two dipoles, as well as on the electromagnetic properties of the surrounding environment, DDIs are exceptional for sensing applications. There are two main ways to carry out FRET-based sensing: (i) enhancing or (ii) inhibiting it. Interaction with resonant environments such as plasmonic, optical cavities, and/or metamaterials promotes the former while acting on the distance between the FRET molecules favors the latter. In this review, we browse both the two ways, pointing the spotlight to the intrinsic interdisciplinarity these two sensing routes imply. We showcase FRET-based sensing mechanisms in a variety of contexts, from pH sensors to molecular structure measurements on a nano-metrical scale, with a particular accent on the central and still mostly overlooked role played between a nano-photonically structured environment and photoluminescent molecules.

Simulating Electronic Absorption Spectra of Atmospherically Relevant Molecules: A Systematic Assignment for Enhancing Undergraduate STEM Education

Computational and atmospheric chemistry are two important branches of contemporary chemistry. With the present topical nature of climate change and global warming, it is more crucial than ever that students are aware of and exposed to atmospheric chemistry, with an emphasis on how modeling may aid in understanding how atmospherically relevant chemical compounds interact with incoming solar radiation. Nonetheless, computational and atmospheric chemistry are under-represented in most undergraduate chemistry curricula. In this manuscript, we describe a simple and efficient method for simulating the electronic absorption spectral profiles of atmospherically relevant molecules that may be utilized in an undergraduate computer laboratory. The laboratory results give students hands-on experience in computational and atmospheric chemistry, as well as electronic absorption spectroscopy.

Sequence Analysis, Structure Prediction of Receptor Proteins and In Silico Study of Potential Inhibitors for Management of Life Threatening COVID-19

Background: Treatment of the Covid-19 pandemic caused by the highly contagious and pathogenic SARS-CoV-2 is a global menace. Day by day, this pandemic is getting worse. Doctors, scientists and researchers across the world are urgently scrambling for a cure for novel corona virus and continuously working at break neck speed to develop vaccines or drugs. But to date, there are no specific drugs or vaccines available in the market to cope up with the virus. Objective: The present study helps us to elucidate 3D structures of SARS-CoV-2 proteins and also to identify natural compounds as potential inhibitors against COVID-19. Methods: The 3D structures of the proteins were constructed using Modeller 9.16 modeling tool. Modelled proteins were validated with PROCHECK by Ramachandran plot analysis. In this study, a small library of natural compounds (fifty compounds) was docked to the hACE2 binding site of the modelled surface glycoprotein of SARS-CoV-2 using AutoDock Vina to repurpose these inhibitors against SARS-CoV-2. Conceptual density functional theory calculations of the best eight compounds had been performed by Gaussian-09. Geometry optimizations for these molecules were done at M06-2X/ def2-TZVP level of theory. ADME parameters, pharmacokinetic properties and drug likeness of the compounds were analyzed using swissADME website. Results: In this study, we analysed the sequences of surface glycoprotein, nucleocapsid phosphoprotein and envelope protein obtained from different parts of the globe. We modelled all the different sequences of surface glycoprotein and envelop protein in order to derive 3D structure of a molecular target, which is essential for the development of therapeutics. Different electronic properties of the inhibitors have been calculated using DFT through M06-2X functional with def2-TZVP basis set. Docking result at the hACE2 binding site of all modelled surface glycoproteins of SARSCoV- 2 showed that all the eight inhibitors (actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) studied here were many folds better compared to hydroxychloroquine which has been found to be effective to treat patients suffering from COVID-19. All the inhibitors meet most of the criteria of drug likeness assessment. Conclusion: We expect that eight compounds (actinomycin D, avellanin C, ichangin, kanglemycin A, obacunone, ursolic acid, ansamiotocin P-3 and isomitomycin A) can be used as potential inhibitors against SARS-CoV-2.

Optimization of Soybean Protein Extraction Using By-Products from NaCl Electrolysis as an Application of the Industrial Symbiosis Concept

Featured ApplicationThis study shows the use of a by-product from the manufacture of a novel antiseptic/disinfectant (HOCl) to obtain a protein isolate from defatted soybean flour (a co-product from the soybean oil industry);an optimization process was carried out to create an industrial symbiosis.Defatted soybean flour is generated during the oil extraction process of soybean, and it has a protein content of ~50%. On the other hand, an alkaline solution of NaOH is produced during the electrolysis process of NaCl in a novel method used to make a potent disinfectant/antiseptic (HOCl). In the present work, we suggest using these two products to produce soy protein isolate (SPI), aiming to create an industrial symbiosis. A Box–Behnken experimental design was executed, and a surface response analysis was performed to optimize temperature, alkaline solution, and time used for SPI extraction. The SPI produced at optimal conditions was then characterized. The experimental results fit well with a second-order polynomial equation that could predict 93.15% of the variability under a combination of 70 °C, alkaline solution 3 (pH 12.68), and 44.7 min of the process. The model predicts a 49.79% extraction yield, and when tested, we obtained 48.30% within the confidence interval (46.66–52.93%). The obtained SPI was comparable in content and structure with a commercial SPI by molecular weight and molecular spectroscopy characterization. Finally, the urease activity (UA) test was negative, indicating no activity for trypsin inhibitor. Based on the functional properties, the SPI is suitable for food applications.

From Development to Function: Hands-On & Inexpensive Clay Modeling of Mammalian Kidney Development

In light of the coronavirus disease 2019 (COVID-19), recent clinical research has demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects breathing and internal organs, especially the kidneys and liver function. It is evident that the kidneys are induced by the virus through the course of the medication treatments, such as the side effects that lead to kidney and liver damage. In order to scaffold kidney pathophysiology with normal kidney development and function in a virtual class or lab setting during the COVID-19 pandemic, we have developed a hands-on and cost-effective clay modeling teaching tool at the undergraduate level for learning about kidney anatomy and development. Given remote teaching, this innovative tool can be used to link the structure to molecular and cellular function through an easy hands-on model for both learning and teaching demonstration for all students.