How helpful were molecular dynamics simulations in shaping our understanding of SARS-CoV-2 spike protein dynamics?

Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2

Molecular dynamics (MD) simulations produce a substantial volume of high-dimensional data, and traditional methods for analyzing these data pose significant computational demands. Advances in MD simulation analysis combined with deep learning-based approaches have led to the understanding of specific structural changes observed in MD trajectories, including those induced by mutations. In this study, we model the trajectories resulting from MD simulations of the SARS-CoV-2 spike protein-ACE2, specifically the receptor-binding domain (RBD), as interresidue distance maps, and use deep convolutional neural networks to predict the functional impact of point mutations, related to the virus's infectivity and immunogenicity. Our model was successful in predicting mutant types that increase the affinity of the S protein for human receptors and reduce its immunogenicity, both based on MD trajectories (precision = 0.718; recall = 0.800; [Formula: see text] = 0.757; MCC = 0.488; AUC = 0.800) and their centroids. In an additional analysis, we also obtained a strong positive Pearson's correlation coefficient equal to 0.776, indicating a significant relationship between the average sigmoid probability for the MD trajectories and binding free energy (BFE) changes. Furthermore, we obtained a coefficient of determination of 0.602. Our 2D-RMSD analysis also corroborated predictions for more infectious and immune-evading mutants and revealed fluctuating regions within the receptor-binding motif (RBM), especially in the [Formula: see text] loop. This region presented a significant standard deviation for mutations that enable SARS-CoV-2 to evade the immune response, with RMSD values of 5Å in the simulation. This methodology offers an efficient alternative to identify potential strains of SARS-CoV-2, which may be potentially linked to more infectious and immune-evading mutations. Using clustering and deep learning techniques, our approach leverages information from the ensemble of MD trajectories to recognize a broad spectrum of multiple conformational patterns characteristic of mutant types. This represents a strategic advantage in identifying emerging variants, bypassing the need for long MD simulations. Furthermore, the present work tends to contribute substantially to the field of computational biology and virology, particularly to accelerate the design and optimization of new therapeutic agents and vaccines, offering a proactive stance against the constantly evolving threat of COVID-19 and potential future pandemics.

Mechanistic study of the transmission pattern of the SARS-CoV-2 omicron variant

The omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) characterized by 30 mutations in its spike protein, has rapidly spread worldwide since November 2021, significantly exacerbating the ongoing COVID-19 pandemic. In order to investigate the relationship between these mutations and the variant's high transmissibility, we conducted a systematic analysis of the mutational effect on spike-angiotensin-converting enzyme-2 (ACE2) interactions and explored the structural/energy correlation of key mutations, utilizing a reliable coarse-grained model. Our study extended beyond the receptor-binding domain (RBD) of spike trimer through comprehensive modeling of the full-length spike trimer rather than just the RBD. Our free-energy calculation revealed that the enhanced binding affinity between the spike protein and the ACE2 receptor is correlated with the increased structural stability of the isolated spike protein, thus explaining the omicron variant's heightened transmissibility. The conclusion was supported by our experimental analyses involving the expression and purification of the full-length spike trimer. Furthermore, the energy decomposition analysis established those electrostatic interactions make major contributions to this effect. We categorized the mutations into four groups and established an analytical framework that can be employed in studying future mutations. Additionally, our calculations rationalized the reduced affinity of the omicron variant towards most available therapeutic neutralizing antibodies, when compared with the wild type. By providing concrete experimental data and offering a solid explanation, this study contributes to a better understanding of the relationship between theories and observations and lays the foundation for future investigations.

A slow but steady nanoLuc: R162A mutation results in a decreased, but stable, nanoLuc activity

NanoLuc (NLuc) luciferase has found extensive application in designing a range of biological assays, including gene expression analysis, protein-protein interaction, and protein conformational changes due to its enhanced brightness and small size. However, questions related to its mechanism of interaction with the substrate, furimazine, as well as bioluminescence activity remain elusive. Here, we combined molecular dynamics (MD) simulation and mutational analysis to show that the R162A mutation results in a decreased but stable bioluminescence activity of NLuc in living cells and in vitro. Specifically, we performed multiple, all-atom, explicit solvent MD simulations of the apo and furimazine-docked (holo) NLuc structures revealing differential dynamics of the protein in the absence and presence of the ligand. Further, analysis of trajectories for hydrogen bonds (H-bonds) formed between NLuc and furimazine revealed substantial H-bond interaction between R162 and Q32 residues. Mutation of the two residues in NLuc revealed a decreased but stable activity of the R162A, but not Q32A, mutant NLuc in live cell and in vitro assays performed using lysates prepared from cells expressing the proteins and with the furimazine substrate. In addition to highlighting the role of the R162 residue in NLuc activity, we believe that the mutant NLuc will find wide application in designing in vitro assays requiring extended monitoring of NLuc bioluminescence activity. SIGNIFICANCE: Bioluminescence has been extensively utilized in developing a variety of biological and biomedical assays. In this regard, engineering of brighter bioluminescent proteins, i.e. luciferases, has played a significant role. This is acutely exemplified by the engineering of the NLuc luciferase, which is small in size and displays much enhanced bioluminescence and thermal stability compared to previously available luciferases. While enhanced bioluminescent activity is desirable in a multitude of biological and biomedical assays, it would also be useful to develop variants of the protein that display a prolonged bioluminescence activity. This is specifically relevant in designing assays that require bioluminescence for extended periods, such as in the case of biosensors designed for monitoring slow enzymatic or cellular signaling reactions, without necessitating multiple rounds of luciferase substrate addition or any specialized reagents that result in increased assay costs. In the current manuscript, we report a mutant NLuc that possesses a stable and prolonged bioluminescence activity, albeit lower than the wild-type NLuc, and envisage a wider application of the mutant NLuc in designing biosensors for monitoring slower biological and biomedical events.

Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 spike (S) protein. With this approach, we first identified candidate adaptive polymorphisms (CAPs) of the SARS-CoV-2 S protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of the S protein compared to the closed form. In particular, the CAP sites control the dynamics of binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly additive mutations. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Prediction of the binding location between the nuclear inhibitor of DNA binding and differentiation 2 (ID2) and HSPA5

Glucose-regulated protein 78 (GRP78), also termed HSPA5, was widely studied in cancer. It was recently approved that GRP78 has nuclear localization potential that sheds light on its role in cancer development. The inhibitor of DNA binding and differentiation 2 (ID2) is the nuclear component that associates with GRP78. The interaction between these two proteins is not understood clearly. In the current study, the binding pattern of GRP78/ID2 is predicted using computational methods. Protein-protein docking is used along with molecular dynamics simulation. The substrate binding domain β of GRP78 can stably interact with the loop region (C42-S60) of ID2 as predicted in this study. This paves the way for a possible destabilizer for this association and cancer eradication.

Novel sofosbuvir derivatives against SARS-CoV-2 RNA-dependent RNA polymerase: an in silico perspective

The human coronavirus, SARS-CoV-2, had a negative impact on both the economy and human health, and the emerging resistant variants are an ongoing threat. One essential protein to target to prevent virus replication is the viral RNA-dependent RNA polymerase (RdRp). Sofosbuvir, a uridine nucleotide analog that potently inhibits viral polymerase, has been found to help treat SARS-CoV-2 patients. This work combines molecular docking and dynamics simulation (MDS) to test 14 sofosbuvir-based modifications against SARS-CoV-2 RdRp. The results reveal comparable (slightly better) average binding affinity of five modifications (compounds 3, 4, 11, 12, and 14) to the parent molecule, sofosbuvir. Compounds 3 and 4 show the best average binding affinities against SARS-CoV-2 RdRp (- 16.28 ± 5.69 and - 16.25 ± 5.78 kcal/mol average binding energy compared to - 16.20 ± 6.35 kcal/mol for sofosbuvir) calculated by Molecular Mechanics Generalized Born Surface Area (MM-GBSA) after MDS. The present study proposes compounds 3 and 4 as potential SARS-CoV-2 RdRp blockers, although this has yet to be proven experimentally.