AI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2 Spike Dynamics

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Targeted protein S-nitrosylation of ACE2 inhibits SARS-CoV-2 infection

Prevention of infection and propagation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a high priority in the Coronavirus Disease 2019 (COVID-19) pandemic. Here we describe S-nitrosylation of multiple proteins involved in SARS-CoV-2 infection, including angiotensin-converting enzyme 2 (ACE2), the receptor for viral entry. This reaction prevents binding of ACE2 to the SARS-CoV-2 spike protein, thereby inhibiting viral entry, infectivity and cytotoxicity. Aminoadamantane compounds also inhibit coronavirus ion channels formed by envelope (E) protein. Accordingly, we developed dual-mechanism aminoadamantane nitrate compounds that inhibit viral entry and, thus, the spread of infection by S-nitrosylating ACE2 via targeted delivery of the drug after E protein channel blockade. These non-toxic compounds are active in vitro and in vivo in the Syrian hamster COVID-19 model and, thus, provide a novel avenue to pursue therapy.

Molecular dynamics studies reveal structural and functional features of the SARS-CoV-2 spike protein

The SARS-CoV-2 virus is responsible for the COVID-19 pandemic the world experience since 2019. The protein responsible for the first steps of cell invasion, the spike protein, has probably received the most attention in light of its central role during infection. Computational approaches are among the tools employed by the scientific community in the enormous effort to study this new affliction. One of these methods, namely molecular dynamics (MD), has been used to characterize the function of the spike protein at the atomic level and unveil its structural features from a dynamic perspective. In this review, we focus on these main findings, including spike protein flexibility, rare S protein conformational changes, cryptic epitopes, the role of glycans, drug repurposing, and the effect of spike protein variants.

Targeted protein S-nitrosylation of ACE2 as potential treatment to prevent spread of SARS-CoV-2 infection

Prevention of infection and propagation of SARS-CoV-2 is of high priority in the COVID-19 pandemic. Here, we describe S-nitrosylation of multiple proteins involved in SARS-CoV-2 infection, including angiotensin converting enzyme 2 (ACE2), the receptor for viral entry. This reaction prevents binding of ACE2 to the SARS-CoV-2 Spike protein, thereby inhibiting viral entry, infectivity, and cytotoxicity. Aminoadamantane compounds also inhibit coronavirus ion channels formed by envelope (E) protein. Accordingly, we developed dual-mechanism aminoadamantane nitrate compounds that inhibit viral entry and thus spread of infection by S-nitrosylating ACE2 via targeted delivery of the drug after E-protein channel blockade. These non-toxic compounds are active in vitro and in vivo in the Syrian hamster COVID-19 model, and thus provide a novel avenue for therapy.

Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.

AI-driven prediction of SARS-CoV-2 variant binding trends from atomistic simulations

We present a novel technique to predict binding affinity trends between two molecules from atomistic molecular dynamics simulations. The technique uses a neural network algorithm applied to a series of images encoding the distance between two molecules in time. We demonstrate that our algorithm is capable of separating with high accuracy non-hydrophobic mutations with low binding affinity from those with high binding affinity. Moreover, we show high accuracy in prediction using a small subset of the simulation, therefore requiring a much shorter simulation time. We apply our algorithm to the binding between several variants of the SARS-CoV-2 spike protein and the human receptor ACE2.

SARS-CoV-2 Spike Protein Mutations and Escape from Antibodies: A Computational Model of Epitope Loss in Variants of Concern

The SARS-CoV-2 spike (S) protein is exposed on the viral surface and is the first point of contact between the virus and the host. For these reasons it represents the prime target for Covid-19 vaccines. In recent months, variants of this protein have started to emerge. Their ability to reduce or evade recognition by S-targeting antibodies poses a threat to immunological treatments and raises concerns for their consequences on vaccine efficacy. To develop a model able to predict the potential impact of S-protein mutations on antibody binding sites, we performed unbiased multi-microsecond molecular dynamics of several glycosylated S-protein variants and applied a straightforward structure-dynamics-energy based strategy to predict potential changes in immunogenic regions on each variant. We recover known epitopes on the reference D614G sequence. By comparing our results, obtained on isolated S-proteins in solution, to recently published data on antibody binding and reactivity in new S variants, we directly show that modifications in the S-protein consistently translate into the loss of potentially immunoreactive regions. Our findings can thus be qualitatively reconnected to the experimentally characterized decreased ability of some of the Abs elicited against the dominant S-sequence to recognize variants. While based on the study of SARS-CoV-2 spike variants, our computational epitope-prediction strategy is portable and could be applied to study immunoreactivity in mutants of proteins of interest whose structures have been characterized, helping the development/selection of vaccines and antibodies able to control emerging variants.

Predicted pH-dependent stability of SARS-CoV-2 spike protein trimer from interfacial acidic groups

Transition between receptor binding domain (RBD) up and down forms of the SARS-CoV-2 spike protein trimer is coupled to receptor binding and is one route by which variants can alter viral properties. It is becoming apparent that key roles in the transition are played by pH and a more compact closed form, termed locked. Calculations of pH-dependence are made for a large set of spike trimers, including locked form trimer structures that have recently become available. Several acidic sidechains become sufficiently buried in the locked form to give a predicted pH-dependence in the mild acidic range, with stabilisation of the locked form as pH reduces from 7.5 to 5, consistent with emerging characterisation by cryo-electron microscopy. The calculated pH effects in pre-fusion spike trimers are modulated mainly by aspartic acid residues, rather than the more familiar histidine role at mild acidic pH. These acidic sidechains are generally surface located and weakly interacting when not in a locked conformation. According to this model, their replacement (perhaps with asparagine) would remove the pH-dependent destabilisation of locked spike trimer conformations, and increase their recovery at neutral pH. This would provide an alternative or supplement to the insertion of disulphide linkages for stabilising spike protein trimers, with potential relevance for vaccine design.

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.

Exploring the Minimum-Energy Pathways and Free-Energy Profiles of Enzymatic Reactions with QM/MM Calculations

Understanding molecular mechanisms of enzymatic reactions is of vital importance in biochemistry and biophysics. Here, we introduce new functions of hybrid quantum mechanical/molecular mechanical (QM/MM) calculations in the GENESIS program to compute the minimum-energy pathways (MEPs) and free-energy profiles of enzymatic reactions. For this purpose, an interface in GENESIS is developed to utilize a highly parallel electronic structure program, QSimulate-QM (https://qsimulate.com), calling it as a shared library from GENESIS. Second, algorithms to search the MEP are implemented, combining the string method (E et al. J. Chem. Phys. 2007, 126, 164103) with the energy minimization of the buffer MM region. The method implemented in GENESIS is applied to an enzyme, triosephosphate isomerase, which converts dihyroxyacetone phosphate to glyceraldehyde 3-phosphate in four proton-transfer processes. QM/MM-molecular dynamics simulations show performances of greater than 1 ns/day with the density functional tight binding (DFTB), and 10-30 ps/day with the hybrid density functional theory, B3LYP-D3. These performances allow us to compute not only MEP but also the potential of mean force (PMF) of the enzymatic reactions using the QM/MM calculations. The barrier height obtained as 13 kcal mol-1 with B3LYP-D3 in the QM/MM calculation is in agreement with the experimental results. The impact of conformational sampling in PMF calculations and the level of electronic structure calculations (DFTB vs B3LYP-D3) suggests reliable computational protocols for enzymatic reactions without high computational costs.

HIV-1 Envelope Conformation, Allostery, and Dynamics

The HIV-1 envelope glycoprotein (Env) mediates host cell fusion and is the primary target for HIV-1 vaccine design. The Env undergoes a series of functionally important conformational rearrangements upon engagement of its host cell receptor, CD4. As the sole target for broadly neutralizing antibodies, our understanding of these transitions plays a critical role in vaccine immunogen design. Here, we review available experimental data interrogating the HIV-1 Env conformation and detail computational efforts aimed at delineating the series of conformational changes connecting these rearrangements. These studies have provided a structural mapping of prefusion closed, open, and transition intermediate structures, the allosteric elements controlling rearrangements, and state-to-state transition dynamics. The combination of these investigations and innovations in molecular modeling set the stage for advanced studies examining rearrangements at greater spatial and temporal resolution.

Potent neutralizing nanobodies resist convergent circulating variants of SARS-CoV-2 by targeting novel and conserved epitopes

There is an urgent need to develop effective interventions resistant to the evolving variants of SARS-CoV-2. Nanobodies (Nbs) are stable and cost-effective agents that can be delivered by novel aerosolization route to treat SARS-CoV-2 infections efficiently. However, it remains unknown if they possess broadly neutralizing activities against the prevalent circulating strains. We found that potent neutralizing Nbs are highly resistant to the convergent variants of concern that evade a large panel of neutralizing antibodies (Abs) and significantly reduce the activities of convalescent or vaccine-elicited sera. Subsequent determination of 9 high-resolution structures involving 6 potent neutralizing Nbs by cryoelectron microscopy reveals conserved and novel epitopes on virus spike inaccessible to Abs. Systematic structural comparison of neutralizing Abs and Nbs provides critical insights into how Nbs uniquely target the spike to achieve high-affinity and broadly neutralizing activity against the evolving virus. Our study will inform the rational design of novel pan-coronavirus vaccines and therapeutics.

Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches

The rapidly growing body of structural and biochemical studies of the SARS-CoV-2 spike glycoprotein has revealed a variety of distinct functional states with radically different arrangements of the receptor-binding domain, highlighting a remarkable function-driven conformational plasticity and adaptability of the spike proteins. In this study, we examined molecular mechanisms underlying conformational and dynamic changes in the SARS-CoV-2 spike mutant trimers through the lens of dynamic analysis of allosteric interaction networks and atomistic modeling of signal transmission. Using an integrated approach that combined coarse-grained molecular simulations, protein stability analysis, and perturbation-based modeling of residue interaction networks, we examined how mutations in the regulatory regions of the SARS-CoV-2 spike protein can differentially affect dynamics and allosteric signaling in distinct functional states. The results of this study revealed key functional regions and regulatory centers that govern collective dynamics, allosteric interactions, and control signal transmission in the SARS-CoV-2 spike proteins. We found that the experimentally confirmed regulatory hotspots that dictate dynamic switching between conformational states of the SARS-CoV-2 spike protein correspond to the key hinge sites and global mediating centers of the allosteric interaction networks. The results of this study provide a novel insight into allosteric regulatory mechanisms of SARS-CoV-2 spike proteins showing that mutations at the key regulatory positions can differentially modulate distribution of states and determine topography of signal communication pathways operating through state-specific cascades of control switch points. This analysis provides a plausible strategy for allosteric probing of the conformational equilibrium and therapeutic intervention by targeting specific hotspots of allosteric interactions and communications in the SARS-CoV-2 spike proteins.

Neuropilin-1 Assists SARS-CoV-2 Infection by Stimulating the Separation of Spike Protein Domains S1 and S2

The cell surface receptor Neuropilin-1 (Nrp1) was recently identified as a host factor for SARS-CoV-2 entry. As the Spike protein of SARS-CoV-2 is cleaved into the S1 and the S2 domain by furin protease, Nrp1 binds to the newly created C-terminal RRAR amino acid sequence of the S1 domain. In this study, we model the association of a Nrp1 (a2-b1-b2) protein with the Spike protein computationally and analyze the topological constraints in the SARS-CoV-2 Spike protein for binding with Nrp1 and ACE2. Importantly, we study the exit mechanism of S2 from the S1 domain with the assistance of ACE2 as well as Nrp1 by molecular dynamics pulling simulations. In the presence of Nrp1, by binding the S1 more strongly to the host membrane, there is a high probability of S2 being pulled out, rather than S1 being stretched. Thus, Nrp1 binding could stimulate the exit of S2 from the S1 domain, which will likely increase virus infectivity as the liberated S2 domain mediates the fusion of virus and host membranes. Understanding of such a Nrp1-assisted viral infection opens the gate for the generation of protein-protein inhibitors, such as antibodies, which could attenuate the infection mechanism and protect certain cells in a future combination therapy.

Protein–ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange–correlation functional

Quantum mechanical binding free energies based on thousands of full-protein DFT calculations are tractable, reproducible and converge well.

SARS-CoV-2 escape in vitro from a highly neutralizing COVID-19 convalescent plasma

To investigate the evolution of SARS-CoV-2 in the immune population, we co-incubated authentic virus with a highly neutralizing plasma from a COVID-19 convalescent patient. The plasma fully neutralized the virus for 7 passages, but after 45 days, the deletion of F140 in the spike N-terminal domain (NTD) N3 loop led to partial breakthrough. At day 73, an E484K substitution in the receptor-binding domain (RBD) occurred, followed at day 80 by an insertion in the NTD N5 loop containing a new glycan sequon, which generated a variant completely resistant to plasma neutralization. Computational modeling predicts that the deletion and insertion in loops N3 and N5 prevent binding of neutralizing antibodies. The recent emergence in the United Kingdom and South Africa of natural variants with similar changes suggests that SARS-CoV-2 has the potential to escape an effective immune response and that vaccines and antibodies able to control emerging variants should be developed.

ONE SENTENCE SUMMARY

Three mutations allowed SARS-CoV-2 to evade the polyclonal antibody response of a highly neutralizing COVID-19 convalescent plasma.