IARD 2022 The 13th Biennial Conference on Classical and Quantum Relativistic Dynamics of Particles and Fields

PrefaceThe International Association for Relativistic Dynamics was organized in February 1998 in Houston, Texas, with John R. Fanchi as president. Although the subject of relativistic dynamics has been explored, from both classical and quantum mechanical points of view, since the work of Einstein and Dirac, its most striking development has been in the framework of quantum field theory. The very accurate calculations of spectral and scattering properties, for example, of the anomalous magnetic moment of the electron and the Lamb shift in quantum electrodynamics, and many qualitative features of the strong and electroweak interactions, demonstrate the very great power of description achieved in this framework. Yet, many fundamental questions remain to be clarified, such as the structure of classical relativistic dynamical theories on the level of Hamilton and Lagrange in Minkowski space as well as on the curved manifolds of general relativity. There, moreover, remain the important questions of the covariant classical description of systems at high energy for which particle production effects are not large, such as discussed in Synge's book, The Relativistic Gas, and in Balescu's book on relativistic statistical mechanics, and the development of a consistent single and many body relativistic quantum theory. In recent years, highly accurate telescopes and advanced facilities for computation have brought a high level of interest in cosmological problems, such as the structure of galaxies (dark matter) and the apparently anomalous expansion of the universe (dark energy). Some of the papers reported here deal with these problems, as well as other fundamental related issues.It was for this purpose, to bring together researchers from a wide variety of fields, such as particle physics, astrophysics, cosmology, foundations of relativity theory, and mathematical physics, with a common interest in relativistic dynamics, to investigate fundamental questions of this type, that this Association was founded. The second meeting took place in 2000 at Bar Ilan University in Israel, the third, in 2002, at Howard University in Washington, D.C., and the fourth, in 2004, in Saas Fee, Switzerland. Subsequent meeting took place in 2006 at the University of Connecticut Storrs, in 2008 at Aristotle University of Thessalonica, in 2010 at National Dong Hwa University, Hualien, Taiwan, in 2012 at the Galileo Galilei Institute for Theoretical Physics (GGI) in Florence, in 2014 as the University of Connecticut Storrs, Connecticut, in 2016 at Jožef Stefan Institute in Ljubljana, Slovenia, and in 2018 in Mérida, Yucatán, Mexico, under the sponsorship of the Instituto Politécnic Nacional. The 2020 meeting, planned for Czech Technical University in Prague, was successfully held online at the height of the Covid-19 pandemic, and the physical meeting in Prague was delayed to 2022.The 2022 meeting forms the basis for the Proceedings that are recorded in this issue of the Journal of Physics: Conference Series. Along with the work of some of the founding and newer but already much engaged members of the Association, we were fortunate to have lecturers from application areas that provided strong challenges for further developments in quantum field theory, cosmological problems, and in the dynamics of systems subject to accelerations and the effects of general relativity. Topics treated in this issue include studies in general relativity and astrophysics, relativistic dynamics and electrodynamics, quantum theory and particles, and foundations of relativistic dynamics.This first physical meeting of the Covid-19 era took place 6 - 9 June at Czech Technical University in Prague, as originally planned for 2020. The meeting was divided into seven plenary sessions over four days. As a result of continued travel restrictions in some areas, a small number of talks were delivered by videoconferencing. The papers presented in this volume represent extensions and refinements to the conference talks, building on feedback and discussions associated with the lect re . We once again express our gratitude to Czech Technical University, and especially the local conference chair Petr Jizba, for their generous hospitality.List of Scientific Advisory Committee, International Organizing Committee and Editorial Board of the proceedings, Dedication are available in this Pdf.

GIREP Malta Webinar 2021

PrefaceAfter the success of the GIREP Malta Webinar 2020, both GIREP Board members and the organisers/hosts from the University of Malta decided to plan for another meeting for GIREP members, to be organised in 2021. Restrictions due to the COVID-19 pandemic had automatically led the organisers of the first meeting to decide in favour of organising a webinar. There was hope, from both organisers and participants, that the 2nd meeting would be held face-to-face in Malta. But even this idea had to be abandoned in 2021. So a second webinar was organised instead. Seeing that participants of the first webinar indicated the need for further discussion related to physics teacher education, this being such an important topic, the GIREP Board decided that Webinar 2021 would still focus on ‘Physics Teacher Education: What matters?' This was the title of the GIREP Malta Webinar 2021.During the Webinar, various keynote speeches were presented. Participants were then sub-divided into groups, according to the workgroups of their interest. The workgroups dealt with specific topics for discussion, led by experts in the field. Participants had time at their disposal to present their work and research. They actively interacted with each other and the group leaders, during discussions. A number of papers were submitted, post-webinar. Each paper was independently and anonymously reviewed by two experts.The workgroups dealt specifically with the following topics:• Preparing teachers for TPACK (technological, pedagogical and content knowledge) and Lab work;• Developing and evaluating teacher PCK (Pedagogical Content Knowledge) in Quantum Mechanics (tools and approaches);• In-service Physics teacher education for early childhood and primary levels;• Pre-service Physics teacher education at all levels;• In-service Physics teacher professional learning for second and higher level education.The topic titles shown above have been used as Section Titles for this Journal of Physics: Conference Series publication. Published papers have been sectioned according to the working group in which they were originally presented.Once again, we must admit that organising the GIREP Malta Webinar 2021 and finalising the editing process for the post webinar publications has been quite a challenge. We have worked with passion and enthusiasm. We firmly believe that discussions, interactions and publications of this kind can help improve teaching and learning at all levels, offering creative ideas that can be used personally by teachers to enhance the class environment, both physically and mentally, as well as creating the important link between theory and practice. We would like to thank all those who contributed to the publication of these papers, including the GIREP board, the authors and reviewers. Thank you for your time and effort.List of International Advisory Committee and Scientific Programme Committee, Local Organising Committee, The Editors are available in this Pdf.

Landscape Analysis of Quercetin: A Potential Candidate Against SARS-CoV-2

Fruit, vegetables, and green tea contain quercetin (a flavonoid). Some of the diet's most signifi-cant sources of quercetin are apples, onions, tomatoes, broccoli, and green tea. Antioxidant, anticancer, anti-inflammatory, antimicrobial, antibacterial, and anti-viral effects have been studied of quercetin. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus, ribonucleic acid (RNA) polymer-ase, and other essential viral life-cycle enzymes are all prevented from entering the body by quercetin. Despite extensive in vitro and in vivo investigations on the immune-modulating effects of quercetin and vitamin C treatment. 3-methyl-quercetin has been shown to bind to essential proteins necessary to convert minus-strand RNA into positive-strand RNAs, preventing the replication of viral RNA in the cytoplasm. Quercetin has been identified as a potential SARS-CoV-2 3C-like protease (3CLpro) suppressor in recent molecular docking studies and in silico assessment of herbal medicines. It has been demonstrated that quercetin increases the expression of heme oxygenase-1 through the nuclear factor erythroid-related factor 2 (Nrf2) signal network. Inhibition of heme oxygenase-1 may increase bilirubin synthesis, an endoge-nous antioxidant that defends cells. When human gingival fibroblast (HGF) cells were exposed to lipo-polysaccharide (LPS), inflammatory cytokine production was inhibited. The magnesium (Mg+2) cation complexation improves quercetin free radical scavenging capacity, preventing oxidant loss and cell death. The main objective of this paper is to provide an overview of the pharmacological effects of quercetin, its protective role against SARS-CoV-2 infection, and any potential molecular processes.Copyright © 2022 Bentham Science Publishers.

Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods.

The main protease of SARS-CoV-2 (called Mpro or 3CLpro) is essential for processing polyproteins encoded by viral RNA. Several Mpro mutations were found in SARS-CoV-2 variants, which are related to higher transmissibility, pathogenicity, and resistance to neutralization antibodies. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. In this study, we used a hybrid simulation method to generate intermediate structures along the six lowest frequency normal modes and sample the conformational space and characterize the structural dynamics and global motions of WT SARS-CoV-2 Mpro and 48 mutations, including mutations found in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. We tried to contribute to the elucidation of the effects of mutation in the structural dynamics of SARS-CoV-2 Mpro. A machine learning analysis was performed following the investigation regarding the influence of the K90R, P99L, P108S, and N151D mutations on the dimeric interface assembling of the SARS-CoV-2 Mpro. The parameters allowed the selection of potential structurally stable dimers, which demonstrated that some single surface aa substitutions not located at the dimeric interface (K90R, P99L, P108S, and N151D) are able to induce significant quaternary changes. Furthermore, our results demonstrated, by a Quantum Mechanics method, the influence of SARS-CoV-2 Mpro mutations on the catalytic mechanism, confirming that only one of the chains of the WT and mutant SARS-CoV-2 Mpros are prone to cleave substrates. Finally, it was also possible to identify the aa residue F140 as an important factor related to the increasing enzymatic reactivity of a significant number of SARS-CoV-2 Mpro conformations generated by the normal modes-based simulations.

Piloting a Full-Year, Optics-Based High School Course on Quantum Computing

Quantum computing was once regarded as a mere theoretical possibility, but recent advances in engineering and materials science have brought practical quantum computers closer to reality. Currently, representatives from industry, academia, and governments across the world are working to build the educational structures needed to produce the quantum workforce of the future. Less attention has been paid to growing quantum computing capacity at the high school level. This article details work at The University of Texas at Austin to develop and pilot the first full-year high school quantum computing class. Over the course of two years, researchers and practitioners involved with the project learned several pedagogical and practical lessons that can be helpful for quantum computing course design and implementation at the secondary level. In particular, we find that the use of classical optics provides a clear and accessible avenue for representing quantum states and gate operators and facilitates both learning and the transfer of knowledge to other Science, Technology, and Engineering (STEM) skills. Furthermore, students found that exploring quantum optical phenomena prior to the introduction of mathematical models helped in the understanding and mastery of the material.

Exploiting reverse vaccinology approach for the design of a multiepitope subunit vaccine against the major SARS-CoV-2 variants.

The current COVID-19 pandemic, an infectious disease caused by the novel coronavirus (SARS-CoV-2), poses a threat to global health because of its high rate of spread and death. Currently, vaccination is the most effective method to prevent the spread of this disease. In the present study, we developed a novel multiepitope vaccine against SARS-CoV-2 containing Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (BA.1) variants. To this end, we performed a robust immunoinformatics approach based on multiple epitopes of the four structural proteins of SARS-CoV-2 (S, M, N, and E) from 475 SARS-CoV-2 genomes sequenced from the regions with the highest number of registered cases, namely the United States, India, Brazil, France, Germany, and the United Kingdom. To investigate the best immunogenic epitopes for linear B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL), we evaluated antigenicity, allergenicity, conservation, immunogenicity, toxicity, human population coverage, IFN-inducing, post-translational modifications, and physicochemical properties. The tertiary structure of a vaccine prototype was predicted, refined, and validated. Through docking experiments, we evaluated its molecular coupling to the key immune receptor Toll-Like Receptor 3 (TLR3). To improve the quality of docking calculations, quantum mechanics/molecular mechanics calculations (QM/MM) were used, with the QM part of the simulations performed using the density functional theory formalism (DFT). Cloning and codon optimization were performed for the successful expression of the vaccine in E. coli. Finally, we investigated the immunogenic properties and immune response of our SARS-CoV-2 multiepitope vaccine. The results of the simulations show that administering our prototype three times significantly increases the antibody response and decreases the amount of antigens. The proposed vaccine candidate should therefore be tested in clinical trials for its efficacy in neutralizing SARS-CoV-2.

Graphenic nanospace: Bondonic entanglement perspectives

The recently predicted quantum particles of the chemical bonding, the bondons, are recognized as the elemental driving quasi-particle driving EPR (Einstein-Podolsky-Rosen) entangling signal under the topological Stone-Wales rotations in a quantum completeness Alice-Bob nano-portation typical description among their representation on carbon-based nano-structures as fullerenic, graphenic, including positive and negative nanospace evertions. To this end, special conceptual symmetrical related framework is through the three significant levels: (i) spherical symmetry-by the morphism between fullerene 240 and icosahedral modeled SARS-Cov-2 surface patterned coronavirus, toward providing the topo-reactive triggered activity by means of HOMO and LUMO electronegativity and chemical hardness based descriptors;(ii) planar symmetry-through the carbon-based graphentronics is advanced at the bondonic quantum level controlling the logical gates in metrological transmitting the qubit Bob's signal in homo-mixed graphenic plated and-for the new generation of photovoltaics (PV) by bondots (bondons as quantum dots)-toward 2(N)-Qbits entangled pristine and defective (by Stone-Wale rotations) double graphenic sheets;(iii) self-folding symmetry-across the graphenic modified surfaces in positive (nano-tori) and negative (Klein Bottle) nanospaces in driving the bondonic bosonic transmission by dynamics of quantum chemical bonding on extended nano-surfaces.

GIREP Malta Webinar 2020

PrefaceIn November 2020, over 200 physics educators and researchers from all over the world met virtually to participate in the GIREP Malta Webinar. The aim was to discuss research and results based on what matters in physics teacher education and how this can be improved for the satisfaction of both teachers and learners. The webinar was the result of a first collaboration between GIREP (the International Research Group on Physics Teaching) and the University of Malta. Initially, a face-to-face Seminar was being planned, but due to the outbreak of COVID-19, a Webinar was organised instead.During the webinar, apart from the presentation of keynote speeches, working groups were also held. These dealt with specific topics for discussion, led by experts in the field. Participants presented their work and actively interacted as they discussed the topics presented. A good number of papers were submitted, post-webinar. Each paper was independently and anonymously reviewed by two experts.The general theme of the webinar was Physics Teacher Education.The working groups dealt specifically with the following topics:• ICT and multimedia in teacher education• Experiments and laboratory work in teacher education• Quantum Mechanics in teaching and learning physics• Formal, non-formal and informal aspects of physics education at primary level• Strategies for pre-service physics teacher education at all levels• In-service teacher professional learning strategiesThe topic titles shown above, have been used as Section Titles for the Journal of Physics: Conference Series publication. Published papers have been sectioned according to the working group in which they were originally presented.Organising the GIREP Malta Webinar 2020 and finalising the editing for the post webinar publications has been quite a challenge. My colleagues and I have worked with passion and enthusiasm. We would like to thank all those who contributed to the publication of these proceedings, including the GIREP committee, the authors and reviewers. Thank you for your time and effort.The editors:Joan BORG MARKSPauline GALEASuzanne GATTDavid SANDS

In silico exploration of disulfide derivatives of Ferula foetida oleo-gum (Covexir®) as promising therapeutics against SARS-CoV-2.

Although vaccines have been significantly successful against coronavirus, due to the high rate of the Omicron variant spread many researchers are trying to find efficient drugs against COVID-19. Herein, we conducted a computational study to investigate the binding mechanism of four potential inhibitors (including disulfide derivatives isolated from Ferula foetida) to SARS-CoV-2 main protease. Our findings revealed that the disulfides mainly interacted with HIS41, MET49, CYS145, HIS64, MET165, and GLN189 residues of SARS-CoV-2 main protease. The binding free energy decomposition results also showed that the van der Waals (vdW) energy plays the main role in the interaction of HIS41, MET49, CYS145, HIS64, MET165, and GLN189 residues with the inhibitors. Furthermore, it is found that the Z-isomer derivatives have a stronger interaction with SARS-CoV-2, and the strongest interaction belongs to the (Z)-1-(1-(methylthio)propyl)-2-(prop-1-enyl)disulfane (ΔG = -18.672 kcal/mol). The quantum mechanical calculations demonstrated that the second-order perturbation stabilization energy and the electron density values for MET49-ligand interactions are higher than the other residue-ligand complexes. This finding confirms the stronger interaction of this residue with the ligands.

Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods.

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space.

Evaluation of protein descriptors in computer-aided rational protein engineering tasks and its application in property prediction in SARS-CoV-2 spike glycoprotein.

The importance of protein engineering in the research and development of biopharmaceuticals and biomaterials has increased. Machine learning in computer-aided protein engineering can markedly reduce the experimental effort in identifying optimal sequences that satisfy the desired properties from a large number of possible protein sequences. To develop general protein descriptors for computer-aided protein engineering tasks, we devised new protein descriptors, one sequence-based descriptor (PCgrades), and three structure-based descriptors (PCspairs, 3D-SPIEs_5.4 Å, and 3D-SPIEs_8Å). While the PCgrades and PCspairs include general and statistical information in physicochemical properties in single and pairwise amino acids respectively, the 3D-SPIEs include specific and quantum-mechanical information with parameterized quantum mechanical calculations (FMO2-DFTB3/D/PCM). To evaluate the protein descriptors, we made prediction models with the new descriptors and previously developed descriptors for diverse protein datasets including protein expression and binding affinity change in SARS-CoV-2 spike glycoprotein. As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance ( R 2 = 0.783 for protein expression and R 2 = 0.711 for binding affinity). As a result, the newly devised descriptors showed a good performance in diverse datasets, in which the PCspairs showed the best performance. Similar approaches with those descriptors would be promising and useful if the prediction models are trained with sufficient quantitative experimental data from high-throughput assays for industrial enzymes or protein drugs.

De novo protein folding on computers. Benefits and challenges.

There has been recent success in prediction of the three-dimensional folded native structures of proteins, most famously by the AlphaFold Algorithm running on Google's/Alphabet's DeepMind computer. However, this largely involves machine learning of protein structures and is not a de novo protein structure prediction method for predicting three-dimensional structures from amino acid residue sequences. A de novo approach would be based almost entirely on general principles of energy and entropy that govern protein folding energetics, and importantly do so without the use of the amino acid sequences and structural features of other proteins. Most consider that problem as still unsolved even though it has occupied leading scientists for decades. Many consider that it remains one of the major outstanding issues in modern science. There is crucial continuing help from experimental findings on protein unfolding and refolding in the laboratory, but only to a limited extent because many researchers consider that the speed by which real proteins folds themselves, often from milliseconds to minutes, is itself still not fully understood. This is unfortunate, because a practical solution to the problem would probably have a major effect on personalized medicine, the pharmaceutical industry, biotechnology, and nanotechnology, including for example "smaller" tasks such as better modeling of flexible "unfolded" regions of the SARS-COV-2 spike glycoprotein when interacting with its cell receptor, antibodies, and therapeutic agents. Some important ideas from earlier studies are given before moving on to lessons from periodic and aperiodic crystals, and a possible role for quantum phenomena. The conclusion is that better computation of entropy should be the priority, though that is presented guardedly.

Discovery of novel TMPRSS2 inhibitors for COVID-19 using in silico fragment-based drug design, molecular docking, molecular dynamics, and quantum mechanics studies.

The global expansion of COVID-19 and the mutations of severe acute respiratory syndrome coronavirus necessitate quick development of treatment and vaccination. Because the androgen-responsive serine protease TMPRSS2 is involved in cleaving the SARS-CoV-2 spike protein allowing the virus to enter the cell, therefore, direct TMPRSS2 inhibition will inhibit virus activation and disease progression which make it an important target for drug discovery. In this study, a homology model of TMPRSS2 protein was initially developed. Then, we used the fragment-based drug design (FBDD) technique to develop effective TMPRSS2 inhibitors. Over a half-million fragments from the enamine database were screened for their binding ability to target protein, and then best-scoring fragments were linked to building new molecules with a good binding affinity. XP docking and MM-GBSA studies revealed 10 new formed molecules with docking score ≤ -14.982 kcal/mol compared to ambroxol (control) with a docking score of -6.464 kcal/mol. Finally, molecular dynamics (MD) and density functional theory (DFT) were calculated for the top 3 molecules.

Novel Molecules derived from 3-O-(6-galloylglucoside) inhibit Main Protease of SARS-CoV 2 In Silico.

The ongoing pandemic caused by the severe acute respiratory syndrome 2 (SARS-CoV 2) has led to more than 168 million confirmed cases with 3.5 million deaths as at 28th May, 2021 across 218 countries. The virus has a cysteine protease called main protease (Mpro) which is significant to it life cycle, tagged as a suitable target for novel antivirals. In this computer-assisted study, we designed 100 novel molecules through an artificial neural network-driven platform called LigDream (https://playmolecule.org/LigDream/) using 3-O-(6-galloylglucoside) as parent molecule for design. Druglikeness screening of the molecules through five (5) different rules was carried out, followed by a virtual screening of those molecules without a single violation of the druglike rules using AutoDock Vina against Mpro. The in silico pharmacokinetic features were predicted and finally, quantum mechanics/molecular mechanics (QM/MM) study was carried out using Molecular Orbital Package 2016 (MOPAC2016) on the overall hit compound with controls to determine the stability and reactivity of the lead molecule. The findings showed that eight (8) novel molecules violated none of the druglikeness rules of which three (3) novel molecules (C33, C35 and C54) showed the utmost binding affinity of -8.3 kcal/mol against Mpro; C33 showed a good in silico pharmacokinetic features with acceptable level of stability and reactively better than our controls based on the quantum chemical descriptors analysis. However, there is an urgent need to carry out more research on these novel molecules for the fight against the disease. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11696-021-01899-y.

Supercomputer simulation of the covalent inhibition of the main protease of SARS-CoV-2.

Molecular modeling tools were applied to design a potential covalent inhibitor of the main protease (Mpro) of the SARS-CoV-2 virus and to investigate its interaction with the enzyme. The compound includes a benzoisothiazolone (BZT) moiety of antimalarial drugs and a 5-fluoro-6-nitropyrimidine-2,4(1.H,3H)-dione (FNP) moiety mimicking motifs of inhibitors of other cysteine proteases. The BZT moiety provides a fair binding of the ligand on the protein surface, whereas the warhead FNP is responsible for efficient nucleophilic aromatic substitution reaction with the catalytic cysteine residue in the Mpro active site, leading to a stable covalent adduct. According to supercomputer calculations of the reaction energy profile using the quantum mechanics/molecular mechanics method, the energy of the covalent adduct is 21 kcal mol-1 below the energy of the reactants, while the highest barrier along the reaction pathway is 9 kcal mol-1. These estimates indicate that the reaction can proceed efficiently and can block the Mpro enzyme. The computed structures along the reaction path illustrate the nucleophilic aromatic substitution (SNAr) mechanism in enzymes. The results of this study are important for the choice of potential drugs blocking the development of coronavirus infection.

Applications of density functional theory in COVID-19 drug modeling.

The rapidly evolving Coronavirus 2019 (COVID-19) pandemic has led to millions of deaths around the world, highlighting the pressing need to develop effective antiviral pharmaceuticals. Recent efforts with computer-aided rational drug discovery have allowed detailed examination of drug-macromolecule interactions primarily by molecular mechanics (MM) techniques. Less widely applied in COVID-19 drug modeling is density functional theory (DFT), a quantum mechanics (QM) method that enables electronic structure calculations and elucidations of reaction mechanisms. Here, we review recent advances in applying DFT in molecular modeling studies of COVID-19 pharmaceuticals. We start by providing an overview of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) drugs and targets, followed by a brief introduction to DFT. We then provide a discussion of different approaches by which DFT has been applied. Finally, we discuss essential factors to consider when incorporating DFT in future drug modeling research.

Dietary polyphenols mitigate SARS-CoV-2 main protease (Mpro)-Molecular dynamics, molecular mechanics, and density functional theory investigations.

The recent evolution of the SARS-like Coronavirus has ravaged the world. The deadly virus has claimed over millions of lives across the world and hence highlights the need to develop effective therapeutic drugs to contain the disease posed by this parasite. In this study, the inhibitory potential of fifty (50) dietary polyphenols against Coronavirus (SARS-CoV-2) main protease (Mpro) was conducted using the Autodock Vina Molecular docking tool. In the virtual screening process, the binding affinity of Remdesivir (-7.7 kcal/mol) currently used to treat COVID-19 patients was set as the cut-off value to screen out less probable inhibitors. Ellagic acid, Kievitone, and Punicalin were the only promising ligands with binding affinities (-8.9 kcal/mol, -8.0 kcal/mol and -7.9 kcal/mol respectively) lower than the set cut-off value. Furthermore, we validated Ellagic acid and Kievitone efficacy by subjecting them to molecular dynamics simulation and further stability was assessed at the molecular mechanics and quantum levels. The overall analysis indicates both compounds demonstrate higher stability and inhibitory potential to bind to the crucial His41 and Cys145 catalytic dyad of Mpro than the standard drug. However, further analysis of punicalin after evaluating its docking score was not conducted as the ligand pharmacokinetics properties suggests it could pose serious adverse effect to the health of participants in clinical trials. Hence, we employed a more safe approach by filtering out the compound during this study. Conclusively, while Ellagic acid and kievitone polyphenolic compounds have been demonstrated to be promising under this in silico research, further studies are needed to substantiate their clinical relevance.

Quantum simulations of SARS-CoV-2 main protease Mpro enable high-quality scoring of diverse ligands.

The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main protease Mpro, an enzyme that cleaves the viral polyproteins into individual proteins required for viral replication and transcription. Unfortunately, standard computational screening methods face difficulties in ranking diverse ligands to a receptor due to disparate ligand scaffolds and varying charge states. Here, we describe full density functional quantum mechanical (DFT) simulations of Mpro in complex with various ligands to obtain absolute ligand binding energies. Our calculations are enabled by a new cloud-native parallel DFT implementation running on computational resources from Amazon Web Services (AWS). The results we obtain are promising: the approach is quite capable of scoring a very diverse set of existing drug compounds for their affinities to M pro and suggest the DFT approach is potentially more broadly applicable to repurpose screening against this target. In addition, each DFT simulation required only ~ 1 h (wall clock time) per ligand. The fast turnaround time raises the practical possibility of a broad application of large-scale quantum mechanics in the drug discovery pipeline at stages where ligand diversity is essential.

Why Should Natural Principles Be Simple?

One of the criteria to a strong principle in natural sciences is simplicity. The conventional view holds that the world is provided with natural laws that must be simple. This common-sense approach is a modern rewording of the medieval philosophical/theological concept of the Multiple arising from (and generated by) the One. Humans need to pursue unifying frameworks, classificatory criteria and theories of everything. Still, the fact that our cognitive abilities tend towards simplification and groupings does not necessarily entail that this is the way the world works. Here we ask: what if singularity does not pave the way to multiplicity? How will we be sure if the Ockham's razor holds in real life? We will show in the sequel that the propensity to reduce to simplicity the relationships among the events leads to misleading interpretations of scientific issues. We are not going to take a full sceptic turn: we will engage in active outreach, suggesting examples from biology and physics to demonstrate how a novel methodological antiunitary approach might help to improve our scientific attitude towards world affairs. We will provide examples from aggregation of SARS-Cov-2 particles, unclassified extinct creatures, pathological brain stiffness. Further, we will describe how antiunitary strategies, plagiarising medieval concepts from William od Ockham and Gregory of Rimini, help to explain novel relational approaches to quantum mechanics and the epistemological role of our mind in building the real world.