The 2019 novel coronavirus (SARS-CoV-2) epidemic, which was first reported in December 2019 in Wuhan, China, was declared a pandemic by the World Health Organization in March 2020. Genetically, SARS-CoV-2 is closely related to SARS-CoV, which caused a global epidemic with 8096 confirmed cases in more than 25 countries from 2002 to 2003. Given the significant morbidity and mortality rate, the current pandemic poses a danger to all of humanity, prompting us to understand the activity of SARS-CoV-2 at the atomic level. Experimental studies have revealed that spike proteins of both SARS-CoV-2 and SARS-CoV bind to angiotensin-converting enzyme 2 (ACE2) before entering the cell for replication. However, the binding affinities reported by different groups seem to contradict each other. Wrapp et al. ( Science 2020 , 367 , 1260–1263) showed that the spike protein of SARS-CoV-2 binds to the ACE2 peptidase domain (ACE2-PD) more strongly than does SARS-CoV, and this fact may be associated with a greater severity of the new virus. However, Walls et al. ( Cell 2020 , 181 , 281–292) reported that SARS-CoV-2 exhibits a higher binding affinity, but the difference between the two variants is relatively small. To understand the binding mechnism and experimental results, we investigated how the receptor binding domain (RBD) of SARS-CoV (SARS-CoV-RBD) and SARS-CoV-2 (SARS-CoV-2-RBD) interacts with a human ACE2-PD using molecular modeling. We applied a coarse-grained model to calculate the dissociation constant and found that SARS-CoV-2 displays a 2-fold higher binding affinity. Using steered all-atom molecular dynamics simulations, we demonstrate that, like a coarse-grained simulation, SARS-CoV-2-RBD was associated with ACE2-PD more strongly than was SARS-CoV-RBD, as evidenced by a higher rupture force and larger pulling work. We show that the binding affinity of both viruses to ACE2 is driven by electrostatic interactions.

A structural understanding of the mechanism by which antibodies bind SARS-CoV-2 at the atomic level is highly desirable as it can tell the development of more effective antibodies to treat Covid-19. Here, we use steered molecular dynamics (SMD) and coarse-grained simulations to estimate the binding affinity of the monoclonal antibodies CR3022 and 4A8 to the SARS-CoV-2 receptor-binding domain (RBD) and SARS-CoV-2 N-terminal domain (NTD). Consistent with experiments, our SMD and coarse-grained simulations both indicate that CR3022 has a higher affinity for SARS-CoV-2 RBD than 4A8 for the NTD, and the coarse-grained simulations indicate the former binds three times stronger to its respective epitope. This finding shows that CR3022 is a candidate for Covid-19 therapy and is likely a better choice than 4A8. Energetic decomposition of the interaction energies between these two complexes reveals that electrostatic interactions explain the difference in the observed binding affinity between the two complexes. This result could lead to a new approach for developing anti-Covid-19 antibodies in which good candidates must contain charged amino acids in the area of contact with the virus.

The impact of the quenched force on protein folding pathways and free energy landscape was studied in detail. Using the coarse-grain Go model, we have obtained the low, middle, and high force regimes for protein refolding under the quenched force. The folding pathways in the low force regime coincide with the thermal ones. A clear switch from thermal folding pathways to force-driven pathways in the middle force regime was observed. The distance between the denatured state and transition state x in the temperature-driven regime is smaller than in the force-driven one. The distance x obtained in the middle force regime is consistent with the available experimental data suggesting that atomic force microscopy experiments deal with the force-regime which is just above the thermal one.

A promising approach to combat Covid-19 infections is the development of effective antiviral antibodies that target the SARS-CoV-2 spike protein. Understanding the structures and molecular mechanisms underlying the binding of antibodies to SARS-CoV-2 can contribute to quickly achieving this goal. Recently, a cocktail of REGN10987 and REGN10933 antibodies was shown to be an excellent candidate for the treatment of Covid-19. Here, using all-atom steered molecular dynamics and coarse-grained umbrella sampling, we examine the interactions of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with REGN10987 and REGN10933 separately as well as together. Both computational methods show that REGN10933 binds to RBD more strongly than REGN10987. Importantly, the cocktail binds to RBD (simultaneous binding) more strongly than its components. The dissociation constants of REGN10987-RBD and REGN10933-RBD complexes calculated from the coarse-grained simulations are in good agreement with the experimental data. Thus, REGN10933 is probably a better candidate for treating Covid-19 than REGN10987, although the cocktail appears to neutralize the virus more efficiently than REGN10933 or REGN10987 alone. The association of REGN10987 with RBD is driven by van der Waals interactions, while electrostatic interactions dominate in the case of REGN10933 and the cocktail. We also studied the effectiveness of these antibodies on the two most dangerous variants Delta and Omicron. Consistent with recent experimental reports, our results confirmed that the Omicron variant reduces the neutralizing activity of REGN10933, REGN10987, and REGN10933+REGN10987 with the K417N, N440K, L484A, and Q498R mutations playing a decisive role, while the Delta variant slightly changes their activity.

Formation of intracellular plaques and small oligomeric species of amyloid β (Aβ) peptides inside neurons is a hallmark of Alzheimer's disease. The most abundant Aβ species in the brain are Aβ1-40 and Aβ1-42, which are composed, respectively, of 40 and 42 residues. Aβ1-42 differs from Aβ1-40 only in two residues, Ile41 and Ala42, yet it shows remarkably faster aggregation and greater neurotoxicity than Aβ1-40. Thus, it is crucial to understand the relative contributions of Ile41 and Ala42 to these distinct behaviors. To achieve this, secondary structures of the Aβ1-41 monomer, which contribute to aggregation propensity, were studied by all-atom molecular dynamics simulation in an implicit solvent and compared to those of Aβ1-40 and Aβ1-42. We find that the secondary structure populations of Aβ1-41 are much closer to those of Aβ1-40 than to those of Aβ1-42, suggesting that Aβ1-41 and Aβ1-40 are likely to have similar aggregation properties. This prediction was confirmed through a thioflavin-T aggregation assay. Thus, our finding indicates that the hydrophobic residue at position 42 is the major contributor to the increased fibril formation rates and consequently neurotoxicity of Aβ peptides.

The degradation of fibrils under the influence of thermal fluctuations was studied experimentally by various groups around the world. In the first set of experiments, it was shown that the decay of fibril content, which can be measured by the ThT fluorescence assay, obeys a bi-exponential function. In the second series of experiments, it was demonstrated that when the monomers separated from the aggregate are not recyclable, the time dependence of the number of monomers belonging to the dominant cluster is described by a single-exponential function if the fraction of bound chains becomes less than a certain threshold. Note that the time dependence of the fraction of bound chains can be measured by tryptophan fluorescence. To understand these interesting experimental results, we developed a phenomenological theory and performed molecular simulation. According to our theory and simulations using the lattice and all-atom models, the time dependence of bound chains is described by a logistic function, which slowly decreases at short time scales but becomes a single exponential function at large time scales. The results, obtained by using lattice and all-atom simulations, ascertained that the time dependence of the fibril content can be described by a bi-exponential function that decays faster than the logistic function on short time scales. We have uncovered the molecular mechanism for the distinction between the logistic and bi-exponential behavior. Since the dissociation of the chain from the fibrils requires the breaking of a greater number of inter-chain contacts as compared to the breaking of the beta sheet structure, the decrease in the number of connected chains is slower than the fibril content. Therefore, the time dependence of the aggregate size is logistic, while the two-exponential behavior is preserved for the content of fibrils. Our results are in agreement with the results obtained in both sets of experiments.

We studied the refolding free energy landscape of 26 proteins using the Go-like model. The distance between the denaturated state and the transition state, X F , was calculated using the Bell theory and the nonlinear Dudko-Hummer-Szabo theory, and its relation to the geometrical properties of the native state was considered in detail. We showed that none of the structural parameters, such as the contact order, protein length, and radius of cross section, correlate with X F for all classes of proteins. To overcome this problem, we have introduced the nematic order parameter P 02 , which describes the ordering of the structured elements of the native state. Due to its topologically global nature, P 02 is better than other structural parameters in describing the folding free energy landscape. In particular, P 02 displays a good correlation with X F extracted from the nonlinear theory for all three classes of proteins. Therefore, this parameter can be used to predict X F for any protein, if its native structure is known.