Ghosts of the past: Elemental composition, biosynthesis reactions and thermodynamic properties of Zeta P.2, Eta B.1.525, Theta P.3, Kappa B.1.617.1, Iota B.1.526, Lambda C.37 and Mu B.1.621 variants of SARS-CoV-2.

From the perspectives of molecular biology, genetics and biothermodynamics, SARS-CoV-2 is the among the best characterized viruses. Research on SARS-CoV-2 has shed a new light onto driving forces and molecular mechanisms of viral evolution. This paper reports results on empirical formulas, biosynthesis reactions and thermodynamic properties of biosynthesis (multiplication) for the Zeta P.2, Eta B.1.525, Theta P.3, Kappa B.1.617.1, Iota B.1.526, Lambda C.37 and Mu B.1.621 variants of SARS-CoV-2. Thermodynamic analysis has shown that the physical driving forces for evolution of SARS-CoV-2 are Gibbs energy of biosynthesis and Gibbs energy of binding. The driving forces have led SARS-CoV-2 through the evolution process from the original Hu-1 to the newest variants in accordance with the expectations of the evolution theory.

SARS-CoV-2 strain wars continues: Chemical and thermodynamic characterization of live matter and biosynthesis of Omicron BN.1, CH.1.1 and XBC variants.

SARS-CoV-2 has during the last 3 years mutated several dozen times. Most mutations in the newly formed variants have been chemically and thermodynamically characterized. New variants have been declared as variants under monitoring. The European Centre for Disease Prevention and Control has suggested the hypothesis that the new BN.1, CH.1.1 and XBC variants could have properties similar to those of VOC. Thermodynamic properties of new variants have been reported in this manuscript for the first time. Gibbs energy of biosynthesis, as the driving force for viral multiplication, is less negative for the new variants than for the earlier variants. This indicates that the virus has evolved towards decrease in pathogenicity, which leads to less severe forms of COVID-19.

Molecular modeling and simulations of some antiviral drugs, benzylisoquinoline alkaloid, and coumarin molecules to investigate the effects on Mpro main viral protease inhibition.

Background: SARS-CoV-2 is a deadly viral disease and uncounted deaths occurs since its first appearance in the year 2019. The antiviral drugs, benzylisoquinoline alkaloids, and coumarin molecules were searched using different online engines for drug repurposing with SARS-CoV-2 and to investigate the effects on main viral protease (Mpro) upon their bindings. Methods: A database composed of antiviral drugs, benzylisoquinoline alkaloids, and Coumarin molecules was screened through a molecular docking strategy to uncover the interactions of collected molecules with SARS-CoV-2 Mpro. Further, molecular dynamics simulations (MDS) were implemented for 100 ns to calculate the stability of the best complexed molecular scaffold with Mpro. The conformations of the simulated complexes were investigated by using principal component analysis (PCA) and Gibbs energy landscape (FEL) and DSSP together. Next, free binding energy (ΔGbind) was calculated using the mmpbsa method. Results: Molecular docking simulations demonstrate 17 molecules exhibited better binding affinity out of 99 molecules present in the database with the viral protease Mpro, followed ADMET properties and were documented. The Coumarin-EM04 molecular scaffold exhibited interactions with catalytical dyad HIS41, CYS145, and neighboring amino acids SER165 and GLN189 in the catalytical site. The crucial factor RMSD was calculated to determine the orientations of Coumarin-EM04. The Coumarin-EM04 complexed with Mpro was found stable in the binding site during MDS. Furthermore, the free energy binding ΔGbind of Coumarin-EM04 was found to be -187.471 ± 2.230 kJ/mol, and for Remdesivir ΔGbind was -171.926 ± 2.237 kJ/mol with SARS-CoV-2 Mpro. Conclusion: In this study, we identify potent molecules that exhibit interactions with catalytical dyad HIS41 and CYS145 amino acids and unravel Coumarin-EM04 exhibited ΔGbind higher than Remdesivir against Mpro and thus may serve better antiviral agent against SARS-CoV-2.

A computational approach to biological pathogenicity.

The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.

Why doesn't Ebola virus cause pandemics like SARS-CoV-2?

Ebola virus is among the most dangerous, contagious and deadly etiological causes of viral diseases. However, Ebola virus has never extensively spread in human population and never have led to a pandemic. Why? The mechanistic biophysical model revealing the biothermodynamic background of virus-host interaction) could help us to understand pathogenesis of Ebola virus disease (earlier known as the Ebola hemorrhagic fever). In this paper for the first time the empirical formula, thermodynamic properties of biosynthesis (including the driving force of virus multiplication in the susceptible host), binding constant and thermodynamic properties of binding are reported. Thermodynamic data for Ebola virus were compared with data for SARS-CoV-2 to explain why SARS-CoV-2 has caused a pandemic, while Ebola remains on local epidemic level. The empirical formula of the Ebola virus was found to be CH1.569O0.3281N0.2786P0.00173S0.00258. Standard Gibbs energy of biosynthesis of the Ebola virus nucleocapsid is -151.59 kJ/C-mol.

The SARS-CoV-2 Hydra, a tiny monster from the 21st century: Thermodynamics of the BA.5.2 and BF.7 variants.

SARS-CoV-2 resembles the ancient mythical creature Hydra. Just like with the Hydra, when one head is cut, it is followed by appearance of two more heads, suppression of one SARS-CoV-2 variant causes appearance of newer variants. Unlike Hydra that grows identical heads, newer SARS-CoV-2 variants are usually more infective, which can be observed as time evolution of the virus at hand, which occurs through acquisition of mutations during time. The appearance of new variants is followed by appearance of new COVID-19 pandemic waves. With the appearance of new pandemic waves and determining of sequences, in the scientific community and general public the question is always raised of whether the new variant will be more virulent and more pathogenic. The two variants characterized in this paper, BA.5.2 and BF.7, have caused a pandemic wave during the late 2022. This paper gives full chemical and thermodynamic characterization of the BA.5.2 and BF.7 variants of SARS-CoV-2. Having in mind that Gibbs energy of binding and biosynthesis represent the driving forces for the viral life cycle, based on the calculated thermodynamic properties we can conclude that the newer variants are more infective than earlier ones, but that their pathogenicity has not changed.

Biothermodynamics of Viruses from Absolute Zero (1950) to Virothermodynamics (2022).

Biothermodynamics of viruses is among the youngest but most rapidly developing scientific disciplines. During the COVID-19 pandemic, it closely followed the results published by molecular biologists. Empirical formulas were published for 50 viruses and thermodynamic properties for multiple viruses and virus variants, including all variants of concern of SARS-CoV-2, SARS-CoV, MERS-CoV, Ebola virus, Vaccinia and Monkeypox virus. A review of the development of biothermodynamics of viruses during the last several decades and intense development during the last 3 years is described in this paper.

Omicron BA.2.75 Subvariant of SARS-CoV-2 Is Expected to Have the Greatest Infectivity Compared with the Competing BA.2 and BA.5, Due to Most Negative Gibbs Energy of Binding.

Omicron BA.2.75 may become the next globally dominant strain of COVID-19 in 2022. The BA.2.75 sub-variant has acquired more mutations (9) in spike protein and other genes of SARS-CoV-2 than any other variant. Thus, its chemical composition and thermodynamic properties have changed compared with earlier variants. In this paper, the Gibbs energy of the binding and antigen-receptor binding rate was reported for the BA.2.75 variant. Gibbs energy of the binding of the Omicron BA.2.75 variant is more negative than that of the competing variants BA.2 and BA.5.

Beyond COVID-19: Do biothermodynamic properties allow predicting the future evolution of SARS-CoV-2 variants?

During the COVID-19 pandemic, many statistical and epidemiological studies have been published, trying to predict the future development of the SARS-CoV-2 pandemic. However, it would be beneficial to have a specific, mechanistic biophysical model, based on the driving forces of processes performed during virus-host interactions and fundamental laws of nature, allowing prediction of future evolution of SARS-CoV-2 and other viruses. In this paper, an attempt was made to predict the development of the pandemic, based on biothermodynamic parameters: Gibbs energy of binding and Gibbs energy of growth. Based on analysis of biothermodynamic parameters of various variants of SARS-CoV-2, SARS-CoV and MERS-CoV that appeared during evolution, an attempt was made to predict the future directions of evolution of SARS-CoV-2 and potential occurrence of new strains that could lead to new pandemic waves. Possible new mutations that could appear in the future could lead to changes in chemical composition, biothermodynamic properties (driving forces of new virus strains) and biological properties of SARS CoV-2 that represent a risk for humanity.

Strain wars 3: Differences in infectivity and pathogenicity between Delta and Omicron strains of SARS-CoV-2 can be explained by thermodynamic and kinetic parameters of binding and growth.

In this paper, for the first time, empirical formulas have been reported of the Delta and Omicron strains of SARS-CoV-2. The empirical formula of the Delta strain entire virion was found to be CH1.6383O0.2844N0.2294P0.0064S0.0042, while its nucleocapsid has the formula CH1.5692O0.3431N0.3106P0.0060S0.0043. The empirical formula of the Omicron strain entire virion was found to be CH1.6404O0.2842N0.2299P0.0064S0.0038, while its nucleocapsid has the formula CH1.5734O0.3442N0.3122P0.0060S0.0033. Based on the empirical formulas, standard thermodynamic properties of formation and growth have been calculated and reported for the Delta and Omicron strains. Moreover, standard thermodynamic properties of binding have been reported for Wild type (Hu-1), Alpha, Beta, Gamma, Delta and Omicron strains. For all the strains, binding phenomenological coefficients and antigen-receptor (SGP-ACE2) binding rates have been determined and compared, which are proportional to infectivity. The results show that the binding rate of the Omicron strain is between 1.5 and 2.5 times greater than that of the Delta strain. The Omicron strain is characterized by a greater infectivity, based on the epidemiological data available in the literature. The increased infectivity was explained in this paper using Gibbs energy of binding. However, no indications exist for decreased pathogenicity of the Omicron strain. Pathogenicity is proportional to the virus multiplication rate, while Gibbs energies of multiplication are very similar for the Delta and Omicron strains. Thus, multiplication rate and pathogenicity are similar for the Delta and Omicron strains. The lower number of severe cases caused by the Omicron strain can be explained by increased number of immunized people. Immunization does not influence the possibility of occurrence of infection, but influences the rate of immune response, which is much more efficient in immunized people. This leads to prevention of more severe Omicron infection cases.

Strain Wars: Competitive interactions between SARS-CoV-2 strains are explained by Gibbs energy of antigen-receptor binding.

Since the beginning of the COVID-19 pandemic, SARS-CoV-2 has mutated several times into new strains, with an increased infectivity. Infectivity of SARS-CoV-2 strains depends on binding affinity of the virus to its host cell receptor. In this paper, we quantified the binding affinity using Gibbs energy of binding and analyzed the competition between SARS-CoV-2 strains as an interference phenomenon. Gibbs energies of binding were calculated for several SARS-SoV-2 strains, including Hu-1 (wild type), B.1.1.7 (alpha), B.1.351 (beta), P.1 (Gamma), B.1.36 and B.1.617 (Delta). The least negative Gibbs energy of binding is that of Hu-1 strain, -37.97 kJ/mol. On the other hand, the most negative Gibbs energy of binding is that of the Delta strain, -49.50 kJ/mol. We used the more negative Gibbs energy of binding to explain the increased infectivity of newer SARS-CoV-2 strains compared to the wild type. Gibbs energies of binding was found to decrease chronologically, with appearance of new strains. The ratio of Gibbs energies of binding of mutated strains and wild type was used to define a susceptibility coefficient, which is an indicator of viral interference, where a virus can prevent or partially inhibit infection with another virus.

Coinfection and Interference Phenomena Are the Results of Multiple Thermodynamic Competitive Interactions.

Biological, physical and chemical interaction between one (or more) microorganisms and a host organism, causing host cell damage, represents an infection. Infection of a plant, animal or microorganism with a virus can prevent infection with another virus. This phenomenon is known as viral interference. Viral interference is shown to result from two types of interactions, one taking place at the cell surface and the other intracellularly. Various viruses use different receptors to enter the same host cell, but various strains of one virus use the same receptor. The rate of virus-receptor binding can vary between different viruses attacking the same host, allowing interference or coinfection. The outcome of the virus-virus-host competition is determined by the Gibbs energies of binding and growth of the competing viruses and host. The virus with a more negative Gibbs energy of binding to the host cell receptor will enter the host first, while the virus characterized by a more negative Gibbs energy of growth will overtake the host metabolic machine and dominate. Once in the host cell, the multiplication machinery is shared by the competing viruses. Their potential to utilize it depends on the Gibbs energy of growth. Thus, the virus with a more negative Gibbs energy of growth will dominate. Therefore, the outcome can be interference or coinfection, depending on both the attachment kinetics (susceptibility) and the intracellular multiplication machinery (permittivity). The ratios of the Gibbs energies of binding and growth of the competing viruses determine the outcome of the competition. Based on this, a predictive model of virus-virus competition is proposed.

Thermodynamic insight into viral infections 2: empirical formulas, molecular compositions and thermodynamic properties of SARS, MERS and SARS-CoV-2 (COVID-19) viruses.

The current situation with the SARS-CoV-2 pandemic indicates the importance of new approaches in vaccine design. In order to design new attenuated vaccines, to decrease virulence of virus wild types, it is important to understand what allows a virus to hijack its host cell's metabolism, a property of all viruses. RNA and protein sequences obtained from databases were used to count the number of atoms of each element in the virions of SARS, MERS and SARS-CoV-2. The number of protein copies and carbohydrate composition were taken from the literature. The number of lipid molecules was estimated from the envelope surface area. Based on elemental composition, growth equations were balanced, and thermodynamic properties of the viruses were determined using Patel-Erickson and Battley equations. Elemental and molecular compositions of SARS, MERS and SARS-CoV-2 were found, as well as their standard thermodynamic properties of formation and growth. Standard Gibbs energy of growth of virus nucleocapsids was found to be significantly more negative than that of their host tissue. The ratio of Gibbs energies of growth of virus nucleocapsids and host cell is greater than unity. The more negative Gibbs energy of growth of viruses implies that virus multiplication has a greater driving force than synthesis of host cell components, giving a physical explanation of why viruses are able to hijack their host cell's metabolism. Knowing the mechanism of viral metabolism hijacking can open new paths for vaccine design. By manipulating chemical composition of viruses, virulence can be decreased by making the Gibbs energy of their growth less negative, resulting in decreased multiplication rate, while preserving antigenic properties.