Computational optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics

The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".

Genomic and structural mechanistic insight to reveal the differential infectivity of omicron and other variants of concern

BACKGROUND

The genome of SARS-CoV-2, is mutating rapidly and continuously challenging the management and preventive measures adopted and recommended by healthcare agencies. The spike protein is the main antigenic site that binds to the host receptor hACE-2 and is recognised by antibodies. Hence, the mutations in this site were analysed to assess their role in differential infectivity of lineages having these mutations, rendering the characterisation of these lineages as variants of concern (VOC) and variants of interest (VOI).

METHODS

In this work, we examined the genome sequence of SARS-CoV-2 VOCs and their phylogenetic relationships with the other PANGOLIN lineages. The mutational landscape of WHO characterized variants was determined and mutational diversity was compared amongst the different severity groups. We then computationally studied the structural impact of the mutations in receptor binding domain of the VOCs. The binding affinity was quantitatively determined by molecular dynamics simulations and free energy calculations.

RESULTS

The mutational frequency, as well as phylogenetic distance, was maximum in the case of omicron followed by the delta variant. The maximum binding affinity was for delta variant followed by the Omicron variant. The increased binding affinity of delta strain followed by omicron as compared to other variants and wild type advocates high transmissibility and quick spread of these two variants and high severity of delta variant.

CONCLUSION

This study delivers a foundation for discovering the improved binding knacks and structural features of SARS-CoV-2 variants to plan novel therapeutics and vaccine candidates against the virus.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Molecular variants of SARS-CoV-2: antigenic properties and current vaccine efficacy

An ongoing pandemic of newly emerged SARS-CoV-2 has puzzled many scientists and health care policymakers around the globe. The appearance of the virus was accompanied by several distinct antigenic changes, specifically spike protein which is a key element for host cell entry of virus and major target of currently developing vaccines. Some of these mutations enable the virus to attach to receptors more firmly and easily. Moreover, a growing number of trials are demonstrating higher transmissibility and, in some of them, potentially more serious forms of illness related to novel variants. Some of these lineages, especially the Beta variant of concern, were reported to diminish the neutralizing activity of monoclonal and polyclonal antibodies present in both convalescent and vaccine sera. This could imply that these independently emerged variants could make antiviral strategies prone to serious threats. The rapid changes in the mutational profile of new clades, especially escape mutations, suggest the convergent evolution of the virus due to immune pressure. Nevertheless, great international efforts have been dedicated to producing efficacious vaccines with cutting-edge technologies. Despite the partial decrease in vaccines efficacy against worrisome clades, most current vaccines are still effective at preventing mild to severe forms of disease and hospital admission or death due to coronavirus disease 2019 (COVID-19). Here, we summarize existing evidence about newly emerged variants of SARS-CoV-2 and, notably, how well vaccines work against targeting new variants and modifications of highly flexible mRNA vaccines that might be required in the future.

A computational protein design protocol for optimization of the SARS-CoV-2 receptor-binding-motif affinity for human ACE2

The present protocol describes the computational design of the SARS-CoV-2 receptor binding motif (RBD) to identify mutations that can potentially improve binding affinity for the human ACE2 (hACE2) receptor. We focus on four positions located at the interface with the hACE2 receptor in the RBD:hACE2 complex. We conduct the design with a high-throughput computational protein design (CPD) program, Proteus, incorporating an adaptive Monte Carlo (MC) protocol that promotes the selection of sequences with good binding affinities.

For complete details on the use and execution of this protocol, please refer to Polydorides and Archontis (2021).

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SARS-CoV-2 positions 455, 493, 494, and 501 at the interface with hACE2 are designed

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The design uses Proteus, a high-throughput computational protein design program

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A physics-based energy function ranks sequences and conformations

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An adaptive Monte Carlo protocol promotes the selection of good affinity sequences

Computational Insights on Allosteric Effect and Dynamic Structural Feature of SARS-COV-2 Spike Protein

COVID-19 caused by SARS-COV-2 is currently continuing to surge globally. The spike (S) protein is the key protein of SARS-COV-2 that recognizes and binds to the host target ACE2. In this study, molecular dynamics simulation was used to elucidate the allosteric effect of S protein. The binding of ACE2 caused a centripetal movement of the receptor-binding domain of the S protein. The dihedral changes of Phe329 and Phe515 played a key role in this process. Two potential cleavage sites S1/S2 and S2' were exposed on the surface after the binding of ACE2. The binding affinity of SARS-COV-2 S protein and ACE2 was significantly higher than that of SARS-COV. This was mainly due to the mutation of Asp480 in SARS-COV to Ser494 in SARS-COV-2, which greatly weakened the electrostatic repulsion. The result provides theoretical basis for the SARS-COV-2 infection and aids the development of biosensors and detection reagents.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

Intranasal gene therapy to prevent infection by SARS-CoV-2 variants

SARS-CoV-2 variants have emerged with enhanced pathogenicity and transmissibility, and escape from pre-existing immunity, suggesting first-generation vaccines and monoclonal antibodies may now be less effective. Here we present an approach for preventing clinical sequelae and the spread of SARS-CoV-2 variants. First, we affinity matured an angiotensin-converting enzyme 2 (ACE2) decoy protein, achieving 1000-fold binding improvements that extend across a wide range of SARS-CoV-2 variants and distantly related, ACE2-dependent coronaviruses. Next, we demonstrated the expression of this decoy in proximal airway when delivered via intranasal administration of an AAV vector. This intervention significantly diminished clinical and pathologic consequences of SARS-CoV-2 challenge in a mouse model and achieved therapeutic levels of decoy expression at the surface of proximal airways when delivered intranasally to nonhuman primates. Importantly, this long-lasting, passive protection approach is applicable in vulnerable populations such as the elderly and immune-compromised that do not respond well to traditional vaccination. This approach could be useful in combating COVID-19 surges caused by SARS-CoV-2 variants and should be considered as a countermeasure to future pandemics caused by one of the many pre-emergent, ACE2-dependent CoVs that are poised for zoonosis.

Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays

An attractive approach to minimize human and animal exposures to toxic environmental contaminants is the use of safe and effective sorbent materials to sequester them. Montmorillonite clays have been shown to tightly bind diverse toxic chemicals. Due to their promise as sorbents to mitigate chemical exposures, it is important to understand their function and rapidly screen and predict optimal clay-chemical combinations for further testing. We derived adsorption free-energy values for a structurally and physicochemically diverse set of toxic chemicals using experimental adsorption isotherms performed in the current and previous studies. We studied the diverse set of chemicals using minimalistic MD simulations and showed that their interaction energies with calcium montmorillonite clays calculated using simulation snapshots in combination with their net charge and their corresponding solvent's dielectric constant can be used as inputs to a minimalistic model to predict adsorption free energies in agreement with experiments. Additionally, experiments and computations were used to reveal structural and physicochemical properties associated with chemicals that can be adsorbed to calcium montmorillonite clay. These properties include positively charged groups, phosphine groups, halide-rich moieties, hydrogen bond donor/acceptors, and large, rigid structures. The combined experimental and computational approaches used in this study highlight the importance and potential applicability of analogous methods to study and design novel advanced sorbent systems in the future, broadening their applicability for environmental contaminants.