Geometrical aspects of hypermagnetic field interactions

We investigate the role of a dynamical torsion field coming from a geometrical non Riemannian model. This model is reminiscent of a generalized Born-Infeld theory and the torsion plays a fundamental role trough its Hodge dual: the pseudovector h_{μ}. This h_{μ} contains axion, Kalb-Ramond 2-form and physical observables of macroscopic character. An emergent interaction Lagrangian arises from the model and it is compared with a superstring inspired one from [8] pulling out several important consequences in favor of our proposal, as the possibility of establishing a clear connection between the change of CMB polarization plane and the anomalous current n_{chiral}.

Computational Insights Into the Effects of the R190K and N121Q Mutations on the SARS-CoV-2 Spike Complex With Biliverdin

The SARS-CoV-2 spike has been regarded as the main target of antibody design against COVID-19. Two single-site mutations, R190K and N121Q, were deemed to weaken the binding affinity of biliverdin although the underlying molecular mechanism is still unknown. Meanwhile, the effect of the two mutations on the conformational changes of “lip” and “gate” loops was also elusive. Thus, molecular dynamics simulation and molecular mechanics/generalized Born surface area (MM/GBSA) free energy calculation were conducted on the wild-type and two other SARS-CoV-2 spike mutants. Our simulations indicated that the R190K mutation causes Lys190 to form six hydrogen bonds, guided by Asn99 and Ile101, which brings Lys190 closer to Arg102 and Asn121, thereby weakening the interaction energy between biliverdin and Ile101 as well as Lys190. For the N121Q mutation, Gln121 still maintained a hydrogen bond with biliverdin; nevertheless, the overall binding mode deviated significantly under the reversal of the side chain of Phe175. Moreover, the two mutants would stabilize the lip loop, which would restrain the meaningful upward movement of the lip. In addition, N121Q significantly promoted the gate loop deviating to the biliverdin binding site and compressed the site. This work would be useful in understanding the dynamics binding biliverdin to the SARS-CoV-2 spike.

Determination of tyrosinase-cyanidin-3-O-glucoside and (−/+)-catechin binding modes reveal mechanistic differences in tyrosinase inhibition

Tyrosinase, exquisitely catalyzes the phenolic compounds into brown or black pigment, inhibition is used as a treatment for dermatological or neurodegenerative disorders. Natural products, such as cyanidin-3-O-glucoside and (−/+)-catechin, are considered safe and non-toxic food additives in tyrosinase inhibition but their ambiguous inhibitory mechanism against tyrosinase is still elusive. Thus, we presented the mechanistic insights into tyrosinase with cyanidin-3-O-glucoside and (−/+)-catechin using computational simulations and in vitro assessment. Initial molecular docking results predicted ideal docked poses (− 9.346 to − 5.795 kcal/mol) for tyrosinase with selected flavonoids. Furthermore, 100 ns molecular dynamics simulations and post-simulation analysis of docked poses established their stability and oxidation of flavonoids as substrate by tyrosinase. Particularly, metal chelation via catechol group linked with the free 3-OH group on the unconjugated dihydropyran heterocycle chain was elucidated to contribute to tyrosinase inhibition by (−/+)-catechin against cyanidin-3-O-glucoside. Also, predicted binding free energy using molecular mechanics/generalized Born surface area for each docked pose was consistent with in vitro enzyme inhibition for both mushroom and murine tyrosinases. Conclusively, (−/+)-catechin was observed for substantial tyrosinase inhibition and advocated for further investigation for drug development against tyrosinase-associated diseases.

In silico identification of A1 agonists and A2a inhibitors in pain based on molecular docking strategies and dynamics simulations

Most recently, the adenosine is considered as one of the most promising targets for treating pain, with few side effects. It exists in the central nervous system, and plays a key role in nociceptive afferent pathway. It is reported that the A1 receptor (A1R) could inhibit Ca2+ channels to reduce the pain like analgesic mechanism of morphine. And, A2a receptor (A2aR) was reported to enhance the accumulation of AMP (cAMP) and released peptides from sensory neurons, resulting in constitutive activation of pain. Much evidence showed that A1R and A2aR could be served as the interesting targets for the treatment of pain. Herein, virtual screening was utilized to identify the small molecule compounds towards A1R and A2aR, and top six molecules were considered as candidates via amber scores. The molecular dynamic (MD) simulations and molecular mechanics/generalized born surface area (MM/GBSA) were employed to further analyze the affinity and binding stability of the six molecules towards A1R and A2aR. Moreover, energy decomposition analysis showed significant residues in A1R and A2aR, including His1383, Phe1276, and Glu1277. It provided basics for discovery of novel agonists and antagonists. Finally, the agonists of A1R (ZINC19943625, ZINC13555217, and ZINC04698406) and inhibitors of A2aR (ZINC19370372, ZINC20176051, and ZINC57263068) were successfully recognized. Taken together, our discovered small molecules may serve as the promising candidate agents for future pain research.

Potential Inhibitors of Fascin From A Database of Marine Natural Products: A Virtual Screening and Molecular Dynamics Study

Marine nature products are unique compounds that are produced by the marine environment including plants, animals, and microorganisms. The wide diversity of marine natural products have great potential and are versatile in terms of drug discovery. In this paper, we use state-of-the-art computational methods to discover inhibitors from marine natural products to block the function of Fascin, an overexpressed protein in various cancers. First, virtual screening (pharmacophore model and molecular docking) was carried out based on a marine natural products database (12015 molecules) and provided eighteen molecules that could potentially inhibit the function of Fascin. Next, molecular mechanics generalized Born surface area (MM/GBSA) calculations were conducted and indicated that four molecules have higher binding affinities than the inhibitor NP-G2-029, which was validated experimentally. ADMET analyses of pharmacokinetics demonstrated that one of the four molecules does not match the criterion. Finally, ligand Gaussian accelerated molecular dynamics (LiGaMD) simulations were carried out to validate the three inhibitors binding to Fascin stably. In addition, dynamic interactions between protein and ligands were analyzed systematically. Our study will accelerate the development of the cancer drugs targeting Fascin.

Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2

The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD−hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π−π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single– and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.

Structure-Based Virtual Screening Reveals Ibrutinib and Zanubrutinib as Potential Repurposed Drugs against COVID-19

Coronavirus disease (COVID)-19 is the leading global health threat to date caused by a severe acute respiratory syndrome coronavirus (SARS-CoV-2). Recent clinical trials reported that the use of Bruton’s tyrosine kinase (BTK) inhibitors to treat COVID-19 patients could reduce dyspnea and hypoxia, thromboinflammation, hypercoagulability and improve oxygenation. However, the mechanism of action remains unclear. Thus, this study employs structure-based virtual screening (SBVS) to repurpose BTK inhibitors acalabrutinib, dasatinib, evobrutinib, fostamatinib, ibrutinib, inositol 1,3,4,5-tetrakisphosphate, spebrutinib, XL418 and zanubrutinib against SARS-CoV-2. Molecular docking is conducted with BTK inhibitors against structural and nonstructural proteins of SARS-CoV-2 and host targets (ACE2, TMPRSS2 and BTK). Molecular mechanics-generalized Born surface area (MM/GBSA) calculations and molecular dynamics (MD) simulations are then carried out on the selected complexes with high binding energy. Ibrutinib and zanubrutinib are found to be the most potent of the drugs screened based on the results of computational studies. Results further show that ibrutinib and zanubrutinib could exploit different mechanisms at the viral entry and replication stage and could be repurposed as potential inhibitors of SARS-CoV-2 pathogenesis.

Key Interacting Residues Between RBD of SARS-CoV-2 and ACE2 Receptor: Combination of Molecular Dynamic Simulation and Density Functional Calculation

<p>The spike protein of SARS-CoV-2 binds to ACE2 receptor <i>via</i> its receptor-binding domain (RBD), with the RBD-ACE2 complex presenting an essential molecular target for vaccine development to stall the virus infection proliferation. The computational analysis at molecular, amino acid (AA) and atomic levels have been performed systematically to identify the key interacting AAs in the formation of the RBD-ACE2 complex, including the MD simulations with molecular mechanics generalized Born surface area (MM-GBSA) method to predict binding free energy (BFE) and to determine the actual interacting AAs, as well as two <i>ab initio</i> quantum chemical protocols based on the density functional theory (DFT) implementation. Based on MD results, Q⁴⁹³, Y⁵⁰⁵, Q⁴⁹⁸, N⁵⁰¹, T⁵⁰⁰, N⁴⁸⁷, Y⁴⁴⁹, F⁴⁸⁶, K⁴¹⁷, Y⁴⁸⁹, F⁴⁵⁶, Y⁴⁹⁵, and L⁴⁵⁵ have been identified as hotspots in RBD, while those in ACE2 are K³⁵³, K³¹, D³⁰, D³⁵⁵, H³⁴, D³⁸, Q²⁴, T²⁷, Y⁸³, Y⁴¹, E³⁵, and E³⁷. Both the electrostatic and hydrophobic interactions are the main driving force to form the AA-AA binding pairs. We confirm that Q⁴⁹³, N⁵⁰¹, F⁴⁸⁶, K⁴¹⁷, and F⁴⁵⁶ in RBD are the key residues responsible for the tight binding of SARS-CoV-2 with ACE2 compared to SARS-CoV. The DFT results reveal that N⁴⁸⁷, Q⁴⁹³, Y⁴⁴⁹, T⁵⁰⁰, G⁴⁹⁶, G⁴⁴⁶ and G⁵⁰² in RBD form pairs <i>via</i> specific hydrogen bonding with Q²⁴, H³⁴, E³⁵, D³⁸, Y⁴¹, Q⁴² and K³⁵³ in ACE2. </p>

Key Interacting Residues Between RBD of SARS-CoV-2 and ACE2 Receptor: Combination of Molecular Dynamic Simulation and Density Functional Calculation

<p>The spike protein of SARS-CoV-2 binds to ACE2 receptor <i>via</i> its receptor-binding domain (RBD), with the RBD-ACE2 complex presenting an essential molecular target for vaccine development to stall the virus infection proliferation. The computational analysis at molecular, amino acid (AA) and atomic levels have been performed systematically to identify the key interacting AAs in the formation of the RBD-ACE2 complex, including the MD simulations with molecular mechanics generalized Born surface area (MM-GBSA) method to predict binding free energy (BFE) and to determine the actual interacting AAs, as well as two <i>ab initio</i> quantum chemical protocols based on the density functional theory (DFT) implementation. Based on MD results, Q⁴⁹³, Y⁵⁰⁵, Q⁴⁹⁸, N⁵⁰¹, T⁵⁰⁰, N⁴⁸⁷, Y⁴⁴⁹, F⁴⁸⁶, K⁴¹⁷, Y⁴⁸⁹, F⁴⁵⁶, Y⁴⁹⁵, and L⁴⁵⁵ have been identified as hotspots in RBD, while those in ACE2 are K³⁵³, K³¹, D³⁰, D³⁵⁵, H³⁴, D³⁸, Q²⁴, T²⁷, Y⁸³, Y⁴¹, E³⁵, and E³⁷. Both the electrostatic and hydrophobic interactions are the main driving force to form the AA-AA binding pairs. We confirm that Q⁴⁹³, N⁵⁰¹, F⁴⁸⁶, K⁴¹⁷, and F⁴⁵⁶ in RBD are the key residues responsible for the tight binding of SARS-CoV-2 with ACE2 compared to SARS-CoV. The DFT results reveal that N⁴⁸⁷, Q⁴⁹³, Y⁴⁴⁹, T⁵⁰⁰, G⁴⁹⁶, G⁴⁴⁶ and G⁵⁰² in RBD form pairs <i>via</i> specific hydrogen bonding with Q²⁴, H³⁴, E³⁵, D³⁸, Y⁴¹, Q⁴² and K³⁵³ in ACE2. </p>