Pulmonary Delivery of Aerosolized Chloroquine and Hydroxychloroquine to Treat COVID-19: In Vitro Experimentation to Human Dosing Predictions.

In vitro screening for pharmacological activity of existing drugs showed chloroquine and hydroxychloroquine to be effective against severe acute respiratory syndrome coronavirus 2. Oral administration of these compounds to obtain desired pulmonary exposures resulted in dose-limiting systemic toxicity in humans. However, pulmonary drug delivery enables direct and rapid administration to obtain higher local tissue concentrations in target tissue. In this work, inhalable formulations for thermal aerosolization of chloroquine and hydroxychloroquine were developed, and their physicochemical properties were characterized. Thermal aerosolization of 40 mg/mL chloroquine and 100 mg/mL hydroxychloroquine formulations delivered respirable aerosol particle sizes with 0.15 and 0.33 mg per 55 mL puff, respectively. In vitro toxicity was evaluated by exposing primary human bronchial epithelial cells to aerosol generated from Vitrocell. An in vitro exposure to 7.24 µg of chloroquine or 7.99 µg hydroxychloroquine showed no significant changes in cilia beating, transepithelial electrical resistance, and cell viability. The pharmacokinetics of inhaled aerosols was predicted by developing a physiologically based pharmacokinetic model that included a detailed species-specific respiratory tract physiology and lysosomal trapping. Based on the model predictions, inhaling emitted doses comprising 1.5 mg of chloroquine or 3.3 mg hydroxychloroquine three times a day may yield therapeutically effective concentrations in the lung. Inhalation of higher doses further increased effective concentrations in the lung while maintaining lower systemic concentrations. Given the theoretically favorable risk/benefit ratio, the clinical significance for pulmonary delivery of aerosolized chloroquine and hydroxychloroquine to treat COVID-19 needs to be established in rigorous safety and efficacy studies. Graphical abstract.

Evidence for a semisolid phase state of aerosols and droplets relevant to the airborne and surface survival of pathogens.

The phase state of respiratory aerosols and droplets has been linked to the humidity-dependent survival of pathogens such as SARS-CoV-2. To inform strategies to mitigate the spread of infectious disease, it is thus necessary to understand the humidity-dependent phase changes associated with the particles in which pathogens are suspended. Here, we study phase changes of levitated aerosols and droplets composed of model respiratory compounds (salt and protein) and growth media (organic-inorganic mixtures commonly used in studies of pathogen survival) with decreasing relative humidity (RH). Efflorescence was suppressed in many particle compositions and thus unlikely to fully account for the humidity-dependent survival of viruses. Rather, we identify organic-based, semisolid phase states that form under equilibrium conditions at intermediate RH (45 to 80%). A higher-protein content causes particles to exist in a semisolid state under a wider range of RH conditions. Diffusion and, thus, disinfection kinetics are expected to be inhibited in these semisolid states. These observations suggest that organic-based, semisolid states are an important consideration to account for the recovery of virus viability at low RH observed in previous studies. We propose a mechanism in which the semisolid phase shields pathogens from inactivation by hindering the diffusion of solutes. This suggests that the exogenous lifetime of pathogens will depend, in part, on the organic composition of the carrier respiratory particle and thus its origin in the respiratory tract. Furthermore, this work highlights the importance of accounting for spatial heterogeneities and time-dependent changes in the properties of aerosols and droplets undergoing evaporation in studies of pathogen viability.

Distant residues modulate conformational opening in SARS-CoV-2 spike protein.

Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves the attachment of the receptor-binding domain (RBD) of its spike proteins to the ACE2 receptors on the peripheral membrane of host cells. Binding is initiated by a down-to-up conformational change in the spike protein, the change that presents the RBD to the receptor. To date, computational and experimental studies that search for therapeutics have concentrated, for good reason, on the RBD. However, the RBD region is highly prone to mutations, and is therefore a hotspot for drug resistance. In contrast, we here focus on the correlations between the RBD and residues distant to it in the spike protein. This allows for a deeper understanding of the underlying molecular recognition events and prediction of the highest-effect key mutations in distant, allosteric sites, with implications for therapeutics. Also, these sites can appear in emerging mutants with possibly higher transmissibility and virulence, and preidentifying them can give clues for designing pan-coronavirus vaccines against future outbreaks. Our model, based on time-lagged independent component analysis (tICA) and protein graph connectivity network, is able to identify multiple residues that exhibit long-distance coupling with the RBD opening. Residues involved in the most ubiquitous D614G mutation and the A570D mutation of the highly contagious UK SARS-CoV-2 variant are predicted ab initio from our model. Conversely, broad-spectrum therapeutics like drugs and monoclonal antibodies can target these key distant-but-conserved regions of the spike protein.

Cryo-EM structures of the SARS-CoV-2 endoribonuclease Nsp15 reveal insight into nuclease specificity and dynamics.

Nsp15, a uridine specific endoribonuclease conserved across coronaviruses, processes viral RNA to evade detection by host defense systems. Crystal structures of Nsp15 from different coronaviruses have shown a common hexameric assembly, yet how the enzyme recognizes and processes RNA remains poorly understood. Here we report a series of cryo-EM reconstructions of SARS-CoV-2 Nsp15, in both apo and UTP-bound states. The cryo-EM reconstructions, combined with biochemistry, mass spectrometry, and molecular dynamics, expose molecular details of how critical active site residues recognize uridine and facilitate catalysis of the phosphodiester bond. Mass spectrometry revealed the accumulation of cyclic phosphate cleavage products, while analysis of the apo and UTP-bound datasets revealed conformational dynamics not observed by crystal structures that are likely important to facilitate substrate recognition and regulate nuclease activity. Collectively, these findings advance understanding of how Nsp15 processes viral RNA and provide a structural framework for the development of new therapeutics.

Intermolecular Interaction Analyses on SARS-CoV-2 Spike Protein Receptor Binding Domain and Human Angiotensin-Converting Enzyme 2 Receptor-Blocking Antibody/Peptide Using Fragment Molecular Orbital Calculation.

The spike glycoprotein (S-protein) mediates SARS-CoV-2 entry via intermolecular interaction with human angiotensin-converting enzyme 2. The receptor binding domain (RBD) of the S-protein has been considered critical for this interaction and acts as the target of numerous neutralizing antibodies and antiviral peptides. This study used the fragment molecular orbital method to analyze the interactions between the RBD and antibodies/peptides and extracted crucial residues that can be used as epitopes. The interactions evaluated as interfragment interaction energy values between the RBD and 12 antibodies/peptides showed a fairly good correlation with the experimental activity pIC50 (R2 = 0.540). Nine residues (T415, K417, Y421, F456, A475, F486, N487, N501, and Y505) were confirmed as being crucial. Pair interaction energy decomposition analyses showed that hydrogen bonds, electrostatic interactions, and π-orbital interactions are important. Our results provide essential information for understanding SARS-CoV-2-antibody/peptide binding and may play roles in future antibody/antiviral drug design.

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.

Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

Probing nano-QSAR to assess the interactions between carbon nanoparticles and a SARS-CoV-2 RNA fragment.

The coronavirus disease-19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is rampant in the world and is a serious threat to global health. The SARS-CoV-2 RNA has been detected in various environmental media, which speeds up the pace of the virus becoming a global biological pollutant. Because many engineered nanomaterials (ENMs) are capable of inducing anti-microbial activity, ENMs provide excellent solutions to overcome the virus pandemic, for instance by application as protective coatings, biosensors, or nano-agents. To tackle some mechanistic issues related to the impact of ENMs on SARS-CoV-2, we investigated the molecular interactions between carbon nanoparticles (CNPs) and a SARS-CoV-2 RNA fragment (i.e., a model molecule of frameshift stimulation element from the SARS-CoV-2 RNA genome) using molecular mechanics simulations. The interaction affinity between the CNPs and the SARS-CoV-2 RNA fragment increased in the order of fullerenes < graphenes < carbon nanotubes. Furthermore, we developed quantitative structure-activity relationship (QSAR) models to describe the interactions of 17 different types of CNPs from three dimensions with the SARS-CoV-2 RNA fragment. The QSAR models on the interaction energies of CNPs with the SARS-CoV-2 RNA fragment show high goodness-of-fit and robustness. Molecular weight, surface area, and the sum of degrees of every carbon atom were found to be the primary structural descriptors of CNPs determining the interactions. Our research not only offers a theoretical insight into the adsorption/separation and inactivation of SARS-CoV-2, but also allows to design novel ENMs which act efficiently on the genetic material RNA of SARS-CoV-2. This contributes to minimizing the challenge of time-consuming and labor-intensive virus experiments under high risk of infection, whilst meeting our precautionary demand for options to handle any new versions of the coronavirus that might emerge in the future.

Designing potential siRNA molecule for the nucleocapsid(N) gene silencing of different SARS-CoV-2 strains of Bangladesh: Computational approach.

SARS-CoV-2 is a single-stranded RNA (+) virus first identified in China and then became an ongoing global outbreak. In most cases, it is fatal in humans due to respiratory malfunction. Extensive researches are going to find an effective therapeutic technique for the treatment of SARS-CoV-2 infected individuals. In this study, we attempted to design a siRNA molecule to silence the most suitable nucleocapsid(N) gene of SARS-CoV-2, which play a major role during viral pathogenesis, replication, encapsidation and RNA packaging. At first, 270 complete N gene sequences of different strains in Bangladesh of these viruses were retrieved from the NCBI database. Different computational methods were used to design siRNA molecules. A siRNA molecule was built against these strains using the SiDirect 2.0 server. Using Mfold and the OligoCalc server, the siRNA molecule was tested for its secondary structure and GC material. The Clustal Omega tool was employed to evaluate any off-target harmony of the planned siRNA molecule. Herein, we proposed a duplex siRNA molecule that does not fit any off-target sequences for the gene silencing of SARS-CoV-2. To treat SARS-CoV-2 infections, currently, any effective therapy is not available. Our engineered siRNA molecule could give an alternative therapeutic approach against various sequenced SARS-CoV-2 strains in Bangladesh.

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering, and structural motif identification and discovery of protein kinases, hydrolases, and SARS-CoV-2's protein via the application of amino acid grouping.

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.

Chemodynamic features of nanoparticles: Application to understanding the dynamic life cycle of SARS-CoV-2 in aerosols and aqueous biointerfacial zones.

We review concepts involved in describing the chemodynamic features of nanoparticles and apply the framework to gain physicochemical insights into interactions between SARS-CoV-2 virions and airborne particulate matter (PM). Our analysis is highly pertinent given that the World Health Organisation acknowledges that SARS-CoV-2 may be transmitted by respiratory droplets, and the US Center for Disease Control and Prevention recognises that airborne transmission of SARS-CoV-2 can occur. In our theoretical treatment, the virion is assimilated to a core-shell nanoparticle, and contributions of various interaction energies to the virion-PM association (electrostatic, hydrophobic, London-van der Waals, etc.) are generically included. We review the limited available literature on the physicochemical features of the SARS-CoV-2 virion and identify knowledge gaps. Despite the lack of quantitative data, our conceptual framework qualitatively predicts that virion-PM entities are largely able to maintain equilibrium on the timescale of their diffusion towards the host cell surface. Comparison of the relevant mass transport coefficients reveals that virion biointernalization demand by alveolar host cells may be greater than the diffusive supply. Under such conditions both the free and PM-sorbed virions may contribute to the transmitted dose. This result points to the potential for PM to serve as a shuttle for delivery of virions to host cell targets. Thus, our critical review reveals that the chemodynamics of virion-PM interactions may play a crucial role in the transmission of COVID-19, and provides a sound basis for explaining reported correlations between episodes of air pollution and outbreaks of COVID-19.

Importance of glutamine 189 flexibility in SARS-CoV-2 main protease: Lesson learned from in silico virtual screening of ChEMBL database and molecular dynamics.

The current global pandemic outbreak of COVID-19, caused by the SARS-CoV-2, strikes an invincible damage to both daily life and the global economy. WHO guidelines for COVID-19 clinical management includes infection control and prevention, social distancing and supportive care using supplemental oxygen and mechanical ventilator support. Currently, evolving researches and clinical reports regarding infected patients with SARS-CoV-2 suggest a potential list of repurposed drugs that may produce appropriate pharmacological therapeutic efficacies in treating COVID-19 infected patients. In this study, we performed virtual screening and evaluated the obtained results of US-FDA approved small molecular database library (302 drug molecule) against two important different protein targets in COVID-19. Best compounds in molecular docking were used as a training set for generation of two different pharmacophores. The obtained pharmacophores were employed for virtual screening of ChEMBL database. The filtered compounds were clustered using Finger print model to obtain two compounds that will be subjected to molecular docking simulations against the two targets. Compounds complexes with SARS-CoV-2 main protease and S-protein were studied using molecular dynamics (MD) simulation. MD simulation studies suggest the potential inhibitory activity of ChEMBL398869 against SARS-CoV-2 main protease and restress the importance of Gln189 flexibility in inhibitors recognition through increasing S2 subsite plasticity.

Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

Structure and inhibition of the SARS-CoV-2 main protease reveal strategy for developing dual inhibitors against Mpro and cathepsin L.

The main protease (Mpro) of SARS-CoV-2 is a key antiviral drug target. While most Mpro inhibitors have a γ-lactam glutamine surrogate at the P1 position, we recently found that several Mpro inhibitors have hydrophobic moieties at the P1 site, including calpain inhibitors II and XII, which are also active against human cathepsin L, a host protease that is important for viral entry. In this study, we solved x-ray crystal structures of Mpro in complex with calpain inhibitors II and XII and three analogs of GC-376 The structure of Mpro with calpain inhibitor II confirmed that the S1 pocket can accommodate a hydrophobic methionine side chain, challenging the idea that a hydrophilic residue is necessary at this position. The structure of calpain inhibitor XII revealed an unexpected, inverted binding pose. Together, the biochemical, computational, structural, and cellular data presented herein provide new directions for the development of dual inhibitors as SARS-CoV-2 antivirals.

Role of different tautomers in the base-pairing abilities of some of the vital antiviral drugs used against COVID-19.

Repurposed drugs are now considered as attractive therapeutics against COVID-19. It is shown that Remdesivir, a nucleoside drug that was originally invented for the Ebola virus, is effective in suppressing the replication of SARS-CoV-2 that causes COVID-19. Similarly, Galidesivir, Favipiravir, Ribavirin, N4-hydroxycytidine (EIDD-1931), and EIDD-2801 (a prodrug of EIDD-1931) were also found to be effective against COVID-19. However, the mechanisms of action of these drugs are not yet fully understood. For example, in some experimental studies, these drugs were proposed to act as a RNA-chain terminator, while in other studies, these were proposed to induce base-pair mutations above the error catastrophe limit to stall the replication of the viral RNA. To understand the mutagenic effects of these drugs, the role of different tautomers in their base-pairing abilities is studied here in detail by employing a reliable dispersion-corrected density functional theoretic method. It is found that Remdesivir and Galidesivir can adopt both amino and imino tautomeric conformations to base-pair with RNA bases. While the insertions of G and U are preferred against the amino tautomers of these drugs, the insertion of C is mainly possible against the imino tautomers. However, although Favipiravir and Ribavirin can make stable base pair interactions by using their keto and enol tautomers, the formation of the latter pairs would be less probable due to the endothermic nature of the products. Interestingly, the insertions of all of the RNA bases are found to be possible against the keto tautomer of Favipiravir, while the keto tautomer of Ribavirin has a clear preference for G. Remarkably, due to the negligible difference in the stability of EIDD-2801 and EIDD-1931, these tautomers would coexist in the biological environment. The insertion of G is found to be preferred against EIDD-1931 and the incorporations of U, A, and G are preferred opposite EIDD-2801. These findings suggest that base-pair mutations are the main causes of the antiviral properties of these drugs.

Revealing the Inhibition Mechanism of RNA-Dependent RNA Polymerase (RdRp) of SARS-CoV-2 by Remdesivir and Nucleotide Analogues: A Molecular Dynamics Simulation Study.

Antiviral drug therapy against SARS-CoV-2 is not yet established and posing a serious global health issue. Remdesivir is the first antiviral compound approved by the US FDA for the SARS-CoV-2 treatment for emergency use, targeting RNA-dependent RNA polymerase (RdRp) enzyme. In this work, we have examined the action of remdesivir and other two ligands screened from the library of nucleotide analogues using docking and molecular dynamics (MD) simulation studies. The MD simulations have been performed for all the ligand-bound RdRp complexes for the 30 ns time scale. This is one of the earlier reports to perform the MD simulations studies using the SARS-CoV-2 RdRp crystal structure (PDB ID 7BTF). The MD trajectories were analyzed and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations were performed to calculate the binding free energy. The binding energy data reveal that compound-17 (-59.6 kcal/mol) binds more strongly as compared to compound-8 (-46.3 kcal/mol) and remdesivir (-29.7 kcal/mol) with RdRp. The detailed analysis of trajectories shows that the remdesivir binds in the catalytic site and forms a hydrogen bond with the catalytic residues from 0 to 0.46 ns. Compound-8 binds in the catalytic site but does not form direct hydrogen bonds with catalytic residues. Compound-17 showed the formation of hydrogen bonds with catalytic residues throughout the simulation process. The MD simulation results such as hydrogen bonding, the center of mass distance analysis, snapshots at a different time interval, and binding energy suggest that compound-17 binds strongly with RdRp of SARS-CoV-2 and has the potential to develop as a new antiviral against COVID-19. Further, the frontier molecular orbital analysis and molecular electrostatic potential (MESP) iso-surface analysis using DFT calculations shed light on the superior binding of compound-17 with RdRp compared to remdesivir and compound-8. The computed as well as the experimentally reported pharmacokinetics and toxicity parameters of compound-17 is encouraging and therefore can be one of the potential candidates for the treatment of COVID-19.

Myoglobin molecule charging in electrolyte solutions.

All atom molecular dynamic modeling was applied in order to determine water molecule and electrolyte ion concentration profiles around and inside the myoglobin molecule at various pH values. Significant penetration of counter ions into the molecule was confirmed. The electric potential distribution within and outside the molecule was quantitatively described using the non-linear Poisson-Boltzmann (PB) approach. Using this model, calculations were performed, yielding the surface and zeta potential for various physicochemical parameters, comprising pH, the electric permittivity, the ion penetration depth and the protein volume fraction (crowding effect). The theoretical results were used for the interpretation of experimental data acquired under different ionic strengths and temperatures by electrophoretic mobility measurements. It is confirmed that the experimental data are adequately reflected for acidic pH values by the non-linear PB model where the nominal molecule charge was calculated from the H++ model. The deviations occurring for larger pH values were accounted for by considering additional non-electrostatic interactions stemming from the van der Waals and ion-induced dipole forces. In this way, it is both experimentally and theoretically confirmed that the effective charge of the myoglobin molecule in electrolyte solutions is considerably smaller than the nominal, structure-based, predicted charge. As a result, under physiological conditions prevailing, e.g. in skeletal muscles, the effective charge of the myoglobin molecule should practically vanish. One can expect that the approach developed in this work can be applied for predicting charging mechanisms of other protein molecules characterized by an analogous charge vs. pH characteristic, e.g., the SARS-CoV-2 virus spike proteins, and for soft particles with pH responsive characteristics.

Identification of potential COVID-19 main protease inhibitors using structure-based pharmacophore approach, molecular docking and repurposing studies.

The current outbreak of novel coronavirus (COVID-19) infections urges the need to identify potential therapeutic agents. Therefore, the repurposing of FDA-approved drugs against today's diseases involves the use of de-risked compounds with potentially lower costs and shorter development timelines. In this study, the recently resolved X-ray crystallographic structure of COVID-19 main protease (Mpro) was used to generate a pharmacophore model and to conduct a docking study to capture antiviral drugs as new promising COVID-19 main protease inhibitors. The developed pharmacophore successfully captured five FDA-approved antiviral drugs (lopinavir, remdesivir, ritonavir, saquinavir and raltegravir). The five drugs were successfully docked into the binding site of COVID-19 Mpro and showed several specific binding interactions that were comparable to those tying the co-crystallized inhibitor X77 inside the binding site of COVID-19 Mpro. Three of the captured drugs namely, remdesivir, lopinavir and ritonavir, were reported to have promising results in COVID-19 treatment and therefore increases the confidence in our results. Our findings suggest an additional possible mechanism of action for remdesivir as an antiviral drug inhibiting COVID-19 Mpro. Additionally, a combination of structure-based pharmacophore modeling with a docking study is expected to facilitate the discovery of novel COVID-19 Mpro inhibitors.

Combining fragment docking with graph theory to improve ligand docking for homology model structures.

Computational protein-ligand docking is well-known to be prone to inaccuracies in input receptor structures, and it is challenging to obtain good docking results with computationally predicted receptor structures (e.g. through homology modeling). Here we introduce a fragment-based docking method and test if it reduces requirements on the accuracy of an input receptor structures relative to non-fragment docking approaches. In this method, small rigid fragments are docked first using AutoDock Vina to generate a large number of favorably docked poses spanning the receptor binding pocket. Then a graph theory maximum clique algorithm is applied to find combined sets of docked poses of different fragment types onto which the complete ligand can be properly aligned. On the basis of these alignments, possible binding poses of complete ligand are determined. This docking method is first tested for bound docking on a series of Cytochrome P450 (CYP450) enzyme-substrate complexes, in which experimentally determined receptor structures are used. For all complexes tested, ligand poses of less than 1 Å root mean square deviations (RMSD) from the actual binding positions can be recovered. Then the method is tested for unbound docking with modeled receptor structures for a number of protein-ligand complexes from different families including the very recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease. For all complexes, poses with RMSD less than 3 Å from actual binding positions can be recovered. Our results suggest that for docking with approximately modeled receptor structures, fragment-based methods can be more effective than common complete ligand docking approaches.

Template-dependent inhibition of coronavirus RNA-dependent RNA polymerase by remdesivir reveals a second mechanism of action.

Remdesivir (RDV) is a direct-acting antiviral agent that is used to treat patients with severe coronavirus disease 2019 (COVID-19). RDV targets the viral RNA-dependent RNA polymerase (RdRp) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We have previously shown that incorporation of the active triphosphate form of RDV (RDV-TP) at position i causes delayed chain termination at position i + 3. Here we demonstrate that the S861G mutation in RdRp eliminates chain termination, which confirms the existence of a steric clash between Ser-861 and the incorporated RDV-TP. With WT RdRp, increasing concentrations of NTP pools cause a gradual decrease in termination and the resulting read-through increases full-length product formation. Hence, RDV residues could be embedded in copies of the first RNA strand that is later used as a template. We show that the efficiency of incorporation of the complementary UTP opposite template RDV is compromised, providing a second opportunity to inhibit replication. A structural model suggests that RDV, when serving as the template for the incoming UTP, is not properly positioned because of a significant clash with Ala-558. The adjacent Val-557 is in direct contact with the template base, and the V557L mutation is implicated in low-level resistance to RDV. We further show that the V557L mutation in RdRp lowers the nucleotide concentration required to bypass this template-dependent inhibition. The collective data provide strong evidence to show that template-dependent inhibition of SARS-CoV-2 RdRp by RDV is biologically relevant.

An online repository of solvation thermodynamic and structural maps of SARS-CoV-2 targets.

SARS-CoV-2 recently jumped species and rapidly spread via human-to-human transmission to cause a global outbreak of COVID-19. The lack of effective vaccine combined with the severity of the disease necessitates attempts to develop small molecule drugs to combat the virus. COVID19_GIST_HSA is a freely available online repository to provide solvation thermodynamic maps of COVID-19-related protein small molecule drug targets. Grid inhomogeneous solvation theory maps were generated using AmberTools cpptraj-GIST, 3D reference interaction site model maps were created with AmberTools rism3d.snglpnt and hydration site analysis maps were created using SSTMap code. The resultant data can be applied to drug design efforts: scoring solvent displacement for docking, rational lead modification, prioritization of ligand- and protein- based pharmacophore elements, and creation of water-based pharmacophores. Herein, we demonstrate the use of the solvation thermodynamic mapping data. It is hoped that this freely provided data will aid in small molecule drug discovery efforts to defeat SARS-CoV-2.