Point mutations in SARS-CoV-2 variants induce long-range dynamical perturbations in neutralizing antibodies

Simulation-driven design of stabilized SARS-CoV-2 spike S2 immunogens

The full-length prefusion-stabilized SARS-CoV-2 spike (S) is the principal antigen of COVID-19 vaccines. Vaccine efficacy has been impacted by emerging variants of concern that accumulate most of the sequence modifications in the immunodominant S1 subunit. S2, in contrast, is the most evolutionarily conserved region of the spike and can elicit broadly neutralizing and protective antibodies. Yet, S2's usage as an alternative vaccine strategy is hampered by its general instability. Here, we use a simulation-driven approach to design S2-only immunogens stabilized in a closed prefusion conformation. Molecular simulations provide a mechanistic characterization of the S2 trimer's opening, informing the design of tryptophan substitutions that impart kinetic and thermodynamic stabilization. Structural characterization via cryo-EM shows the molecular basis of S2 stabilization in the closed prefusion conformation. Informed by molecular simulations and corroborated by experiments, we report an engineered S2 immunogen that exhibits increased protein expression, superior thermostability, and preserved immunogenicity against sarbecoviruses.

Integration of a Randomized Sequence Scanning Approach in AlphaFold2 and Local Frustration Profiling of Conformational States Enable Interpretable Atomistic Characterization of Conformational Ensembles and Detection of Hidden Allosteric States in the ABL1 Protein Kinase

Despite the success of AlphaFold methods in predicting single protein structures, these methods showed intrinsic limitations in the characterization of multiple functional conformations of allosteric proteins. The recent NMR-based structural determination of the unbound ABL kinase in the active state and discovery of the inactive low-populated functional conformations that are unique for ABL kinase present an ideal challenge for the AlphaFold2 approaches. In the current study, we employ several adaptations of the AlphaFold2 methodology to predict protein conformational ensembles and allosteric states of the ABL kinase including randomized alanine sequence scanning combined with the multiple sequence alignment subsampling proposed in this study. We show that the proposed new AlphaFold2 adaptation combined with local frustration profiling of conformational states enables accurate prediction of the protein kinase structures and conformational ensembles, also offering a robust approach for interpretable characterization of the AlphaFold2 predictions and detection of hidden allosteric states. We found that the large high frustration residue clusters are uniquely characteristic of the low-populated, fully inactive ABL form and can define energetically frustrated cracking sites of conformational transitions, presenting difficult targets for AlphaFold2. The results of this study uncovered previously unappreciated fundamental connections between local frustration profiles of the functional allosteric states and the ability of AlphaFold2 methods to predict protein structural ensembles of the active and inactive states. This study showed that integration of the randomized sequence scanning adaptation of AlphaFold2 with a robust landscape-based analysis allows for interpretable atomistic predictions and characterization of protein conformational ensembles, providing a physical basis for the successes and limitations of current AlphaFold2 methods in detecting functional allosteric states that play a significant role in protein kinase regulation.

Probing allosteric communication with combined molecular dynamics simulations and network analysis

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource. Network analysis built on Molecular Dynamics simulation data is particularly suited in this respect and is gradually becoming of routine use. Herein, we collect the recent literature in the field, discussing different aspects and available options for network construction and analysis. We further highlight interesting refinements and extensions, eventually providing our perspective on this topic.

Single-Molecule Investigation of the Binding Interface Stability of SARS-CoV-2 Variants with ACE2

The SARS-CoV-2 pandemic spurred numerous research endeavors to comprehend the virus and mitigate its global severity. Understanding the binding interface between the virus and human receptors is pivotal to these efforts and paramount to curbing infection and transmission. Here we employ atomic force microscopy and steered molecular dynamics simulation to explore SARS-CoV-2 receptor binding domain (RBD) variants and angiotensin-converting enzyme 2 (ACE2), examining the impact of mutations at key residues upon binding affinity. Our results show that the Omicron and Delta variants possess strengthened binding affinity in comparison to the Mu variant. Further, using sera from individuals either vaccinated or with acquired immunity following Delta strain infection, we assess the impact of immunity upon variant RBD/ACE2 complex formation. Single-molecule force spectroscopy analysis suggests that vaccination before infection may provide stronger protection across variants. These results underscore the need to monitor antigenic changes in order to continue developing innovative and effective SARS-CoV-2 abrogation strategies.

Binding order of substrate and cofactor in sulfonamide monooxygenase during sulfa drug degradation: in silico studies

For decades, sulfonamide antibiotics have been used across industries such as agriculture and animal husbandry. However, the use and inadvertent misuse of these antibiotics have resulted in the advent of sulfonamide-drug-resistant strains due to antibiotic pollution. Enzymatic bioremediation of antibiotics remains a potential emerging solution to combat antibiotic pollution. Here, we propose an enzymatic model for the degradation of sulfonamides by Microbacterium sp. We have employed a multi-pronged computational strategy involving - protein structure modelling, ligand docking and molecular dynamics simulations to decipher a plausible binding order for the enzymatic degradation of sulfonamides by the bacterial sulfonamide monooxygenase, SulX. Our results enable us to predict that this degradation is achieved through the sequential binding of the antibiotic sulfonamide followed by the reduced flavin cofactor FMNH2, thereby laying the computational foundation for further advancements in enzyme-mediated degradation of the antibiotic. We also provide a list of experiments which may be performed to verify and follow-up on our in-silico studies.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 variant evasion of monoclonal antibodies based on in vitro studies

Monoclonal antibodies (mAbs) offer a treatment option for individuals with severe COVID-19 and are especially important in high-risk individuals where vaccination is not an option. Given the importance of understanding the evolution of resistance to mAbs by SARS-CoV-2, we reviewed the available in vitro neutralization data for mAbs against live variants and viral constructs containing spike mutations of interest. Unfortunately, evasion of mAb-induced protection is being reported with new SARS-CoV-2 variants. The magnitude of neutralization reduction varied greatly among mAb-variant pairs. For example, sotrovimab retained its neutralization capacity against Omicron BA.1 but showed reduced efficacy against BA.2, BA.4 and BA.5, and BA.2.12.1. At present, only bebtelovimab has been reported to retain its efficacy against all SARS-CoV-2 variants considered here. Resistance to mAb neutralization was dominated by the action of epitope single amino acid substitutions in the spike protein. Although not all observed epitope mutations result in increased mAb evasion, amino acid substitutions at non-epitope positions and combinations of mutations also contribute to evasion of neutralization. This Review highlights the implications for the rational design of viral genomic surveillance and factors to consider for the development of novel mAb therapies.

The effect of mutations on binding interactions between the SARS-CoV-2 receptor binding domain and neutralizing antibodies B38 and CB6

SARS-CoV-2 is the pathogen responsible for COVID-19 that has claimed over six million lives as of July 2022. The severity of COVID-19 motivates a need to understand how it could evolve to escape potential treatments and to find ways to strengthen existing treatments. Here, we used the molecular modeling methods MD + FoldX and PyRosetta to study the SARS-CoV-2 spike receptor binding domain (S-RBD) bound to two neutralizing antibodies, B38 and CB6 and generated lists of antibody escape and antibody strengthening mutations. Our resulting watchlist contains potential antibody escape mutations against B38/CB6 and consists of 211/186 mutations across 35/22 S-RBD sites. Some of these mutations have been identified in previous studies as being significant in human populations (e.g., N501Y). The list of potential antibody strengthening mutations that are predicted to improve binding of B38/CB6 to S-RBD consists of 116/45 mutations across 29/13 sites. These mutations could be used to improve the therapeutic value of these antibodies.