Mechanistic Insights into Targeting SARS-CoV-2 Papain-like Protease in the Evolution and Management of COVID-19
Abstract
:1. Introduction
2. Implications for Viral Evolution (Target Enzymes and Receptors)
3. Structure and Function of the SARS-CoV-2 PLpro
4. Multifaceted Approach with MD Simulations Targeting SARS-CoV-2 Papain-like Protease (PLpro)
5. Implications of PLpro Targeting beyond COVID-19, Exploration of Its Role in Other Diseases, and Potential as a Target for Broader Antiviral Strategies
6. Clinical and Preclinical Studies: Integrating MD Simulation Insights
7. Challenges and Future Directions: Guided by MD Simulations
8. Conclusions and Author Insights into Targeting SARS-CoV-2 Papain-like Protease
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Enzyme/Receptor (Crystal Structure) | Function/Role | Targets for Therapeutic Development | Virus–Host Interaction | Implications for Viral Evolution and SARS-CoV-2 Management | References |
---|---|---|---|---|---|
ACE2 | Viral entry, lung protection | Therapeutic target | Facilitates viral entry and crosstalk with host cells | Dual role in COVID-19 infections; potential protection from acute lung injury and ARDS; increased susceptibility due to high ACE2 expression | [10,11] |
TMPRSS2 | Viral entry, priming | Potential inhibitor | Mediates viral entry and spike protein cleavage | Important for viral entry; inhibitors may prevent infection and reduce viral spread | [12] |
DPP4 | Possible receptor | Role uncertain | Possible binding to SARS-CoV-2 | Role in virus–host interaction needs further investigation; may be involved in viral entry | [13] |
PLpro (Protease) | Viral replication, polyprotein cleavage | Drug target for inhibition | Essential for viral replication | Critical for cleaving polyproteins; potential drug target for inhibiting viral replication | [14] |
3CLpro (Mpro) | Polyprotein cleavage, replication | Drug target for inhibition | Essential for viral replication | Key for cleaving polyproteins; promising target for antiviral strategies | [15,16] |
RdRp (RNA Polymerase) | RNA synthesis, replication | Potential drug target | Crucial for viral genome replication | Required for replication; promising drug target for antiviral strategies | [17] |
Helicase (NSP13) | RNA unwinding, replication | Potential target | Facilitates RNA unwinding and replication | Important for viral genome replication; potential therapeutic target for anti-COVID-19 strategies | [18] |
Cathepsin B/L | Viral entry | Target for inhibiting entry | Involved in viral entry | Blocking cathepsin activity can prevent viral entry | [19,20,58] |
Furin | S protein cleavage | Potential drug target | Cleavage of S protein and virus entry | Promising target for inhibiting viral entry and spread | [22,58] |
Inhibitor/Drug Candidate and Chemical Structure | Simulation Method | Key/Type of Interaction | Simulation Length | Force Field Used | Binding Free Energy | Mechanism of Action | Ref. |
---|---|---|---|---|---|---|---|
GRL-0617 | Molecular dynamics simulation | Non-covalent binding | 100 ns | AMBER | −21.5 kcal/mol | Noncovalent inhibition of PLpro | [103] |
VIR250 and VIR251 | Molecular dynamics simulation | Irreversible binding | 50 ns | OPLS-AA | Not available | Irreversible inhibitors of PLpro | [104,105] |
Neobavaisoflavone | Molecular dynamics simulation | Low-energy binding | 75 ns | CHARMM36 | Not available | Binding to the catalytic triad of PLpro | [106] |
Ritonavir | Molecular dynamics simulation | Binding analysis | 50 ns | GROMOS | −8.2 kcal/mol | Investigated potential PLpro inhibition | [107] |
Dasabuvir (A17) | Molecular dynamics simulation | Stable binding | 100 ns | CHARMM27 | −11.7 kcal/mol | Stable binding with PLpro | [108] |
Methisazone (A34) | Molecular dynamics simulation | Stable binding | 75 ns | AMBER | −12.3 kcal/mol | Exhibits stable dynamic behavior in a complex | [108] |
Vaniprevir (A53) | Molecular dynamics simulation | High binding affinity | 100 ns | OPLS-AA | Not available | Shows high binding affinity for PLpro | [108] |
Baicalein | Molecular dynamics simulation | Binding to the active site | 50 ns | CHARMM36 | −12.8 kcal/mol | Binds to the active site of PLpro | [109] |
Disulfiram | Molecular dynamics simulation | Inhibition analysis | 75 ns | GROMOS | Not available | Repurposed for potential PLpro inhibition | [110] |
Carmofur | Molecular dynamics simulation | Binding to PLpro | 100 ns | AMBER | −10.5 kcal/mol | Demonstrates binding to PLpro. | [111] |
Ebselen | Molecular dynamics simulation | Antiviral activity | 75 ns | CHARMM27 | Not available | Investigated for its antiviral activity | [69] |
Tideglusib | Molecular dynamics simulation | Potential inhibitor | 50 ns | CHARMM36 | Not available | Explored for its potential as an inhibitor | [111] |
Shikonin | Molecular dynamics simulation | Active site binding | 100 ns | AMBER | −15.6 kcal/mol | Binds to the active site of PLpro | [112] |
PX-12 (Belinostat) | Molecular dynamics simulation | Inhibition potential | 75 ns | GROMOS | Not available | Investigated for its inhibition potential | [101] |
Computational Technique | Applicability of the Technique to SARS-CoV-2 | Advantages | Limitations | Ref. |
---|---|---|---|---|
Molecular Dynamics (MD) | For an atomistic analysis of the dynamic interactions between inhibitors and PLpro or other enzymes in SARS-CoV-2, MD simulations are a good choice. They could record conformational changes over time, offering insights into the kinetics and binding mechanisms of the creation of protein–ligand complexes. MD simulations provide useful information regarding the flexibility and stability of the binding site, thereby making structure-based drug design easier. | By thoroughly examining binding events, MD simulations help scientists understand the intricate relationships between inhibitors and target enzymes. They offer an atomistic-level understanding of the dynamic behavior of the protein–ligand complex, which helps to rationally design new inhibitors with a higher selectivity and affinity. Furthermore, MD simulations can capture the impacts of ions and solvent molecules on the binding process, thereby improving the precision of binding free energy estimates. | MD simulations are computationally demanding despite their high level of realism, especially when extended periods are required to observe uncommon binding events. The selection of force field parameters and simulation techniques, which may induce bias or inaccuracies, also affects the accuracy of MD simulations. Furthermore, the predictive power of MD simulations may be limited by their inability to adequately mimic specific features of protein dynamics, such as extensive conformational changes or allosteric effects. | [189,190,191] |
Molecular Docking | One popular technique for estimating the binding affinities and mechanisms of small-molecule inhibitors to PLpro or other SARS-CoV-2 enzymes is molecular docking. Large compound libraries can be virtually screened to identify possible inhibitors and rank lead compounds for additional experimental confirmation. By offering insightful information about the interactions between inhibitors and target enzymes, molecular docking helps optimize lead compounds for structure-based drug design strategies. | High-throughput screening capabilities provided by molecular docking enable researchers to quickly assess how well various possible inhibitors bind to PLpro or other enzymes. This makes it easier to rationally design new inhibitors with an improved potency and specificity by precisely predicting the binding modes and poses of ligands within the active site of the enzyme. Moreover, researchers with limited computer resources can access molecular docking because it is comparatively less expensive to compute than other approaches. | The precision with which molecular docking techniques can capture protein flexibility may be limited, which could result in estimates of the ligand binding affinity that are either falsely positive or falsely negative. They depend on stiff receptor architectures, which may not accurately capture the dynamic character of the interaction between a protein and a ligand. The consideration of solvent effects and conformational changes in the binding site may be limited in molecular docking predictions of binding free energies. Furthermore, the quality of the protein structure and the scoring function employed affect the accuracy of molecular docking results, which could add uncertainty to the predictions. | [76,192,193,194] |
Free Energy Perturbation (FEP) | FEP calculations provide a quantitative assessment of the binding free energies between inhibitors and PLpro or other enzymes in SARS-CoV-2. By shedding light on the thermodynamic stability of the protein–ligand complex, it is possible to rank lead compounds according to how well they are projected to bind. FEP calculations can capture minor energy differences between ligands, guiding sensible tuning of inhibitor potency and selectivity. | By providing researchers with a thorough and quantitative evaluation of binding affinities, FEP calculations enable them to rank potential inhibitors for additional experimental validation and compare the effectiveness of various inhibitors. By taking solvent effects, entropy fluctuations, and molecule flexibility into consideration, they provide insightful information about the energetics of ligand binding. Furthermore, by highlighting important interactions between inhibitors and target enzymes, FEP calculations can direct structure-based drug design efforts and help rationalize the development of innovative treatments with increased efficacy. | FEP computations are computationally and time intensive because they require meticulous equilibration and substantial sampling to obtain accurate findings. To guarantee the validity of the forecasts, they depend on precise force field parameters and simulation techniques, which, if improperly calibrated, may introduce uncertainties or inaccuracies. Furthermore, some features of protein–ligand interactions, such as conformational changes or solvent effects, may be difficult for FEP calculations to adequately simulate, which could result in inaccurate binding free energy predictions. Furthermore, the computing expense of FEP computations can restrict their suitability for intricate biological systems or sizable compound libraries, necessitating cautious evaluation of available resources. | [190,195,196] |
MM/PBSA and MM/GBSA | When determining the binding free energies between inhibitors and PLpro or other enzymes in SARS-CoV-2, the MM/PBSA and MM/GBSA approaches work well, especially in large systems with many flexible regions or binding sites. They provide a computationally affordable substitute for explicit solvent simulations, facilitating the expeditious identification of putative inhibitors and the order of importance of lead compounds. The energetics of protein–ligand interactions are better understood using the MM/PBSA and MM/GBSA techniques, which help in the logical adjustment of inhibitor potency and selectivity. | Because the MM/PBSA and MM/GBSA techniques are less computationally expensive than explicit solvent simulations, even researchers with limited computing resources can use them. They provide a useful and effective method for calculating binding free energies in large systems, making it possible to quickly screen for possible inhibitors and identify lead compounds for additional experimental confirmation. Furthermore, the contributions of certain residues to ligand binding can be captured using the MM/PBSA and MM/GBSA techniques, offering important insights into the major interactions influencing inhibitor efficacy and selectivity. | The MM/PBSA and MM/GBSA techniques depend on implicit solvent representations and simplified energy models, which may not be sufficiently accurate to capture intricate protein–ligand interactions. They are susceptible to the selection of force field parameters, and if not calibrated correctly, may yield erratic results. Furthermore, the projected binding free energies may contain errors or uncertainties due to the inability of the MM/PBSA and MM/GBSA methodologies to consider solvent effects and conformational changes in the binding site. Furthermore, in systems with high protein flexibility or allosteric effects, the accuracy of MM/PBSA and MM/GBSA calculations may be impaired, necessitating rigorous validation and interpretation of the results. | [76,190,197] |
Quantum Mechanics/Molecular Mechanics (QM/MM) | Enzyme–substrate interactions in PLpro or other SARS-CoV-2 enzymes can be studied with a high accuracy using QM/MM simulations, which consider the chemical reactivity and electronic effects during binding. They offer thorough insights into the mechanisms underlying inhibitor binding and enzyme catalysis, which help to rationally develop innovative medicines with increased selectivity and efficacy. QM/MM simulations can capture the effects of substrate changes or active site mutations on the enzyme activity and inhibitor binding, making them an excellent tool for investigating complex biological systems. | A thorough understanding of enzyme–substrate interactions at the quantum mechanical level is possible with QM/MM simulations, which also provide important insights into the underlying chemical mechanisms of inhibitor binding and enzyme catalysis. They enable the logical optimization of inhibitor potency and selectivity by enabling researchers to investigate the energetics of bond creation and breaking in enzyme processes. Furthermore, by highlighting important interactions between inhibitors and target enzymes, QM/MM simulations can direct structure-based drug design efforts and help develop innovative therapies with improved specificity and efficacy. | The usefulness of QM/MM simulations is limited to tiny systems or short timelines because of their high processing costs and requirements. To guarantee the veracity of the forecasts, they depend on precise force field parameters and quantum mechanical techniques, which, if improperly calibrated, may introduce uncertainties or inaccuracies. Furthermore, the effects of solvent molecules and protein flexibility on the interactions between the enzyme and substrate may be difficult to simulate in QM/MM simulations, which could result in inaccurate binding free energy predictions. In addition, the intricacy of quantum mechanical computations and the requirement for specific knowledge in computational chemistry may make it difficult to interpret the outcomes of QM/MM simulations. | [198,199,200] |
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Magwaza, N.N.; Mushebenge, A.G.-A.; Ugbaja, S.C.; Mbatha, N.A.; Khan, R.B.; Kumalo, H.M. Mechanistic Insights into Targeting SARS-CoV-2 Papain-like Protease in the Evolution and Management of COVID-19. BioChem 2024, 4, 268-299. https://doi.org/10.3390/biochem4030014
Magwaza NN, Mushebenge AG-A, Ugbaja SC, Mbatha NA, Khan RB, Kumalo HM. Mechanistic Insights into Targeting SARS-CoV-2 Papain-like Protease in the Evolution and Management of COVID-19. BioChem. 2024; 4(3):268-299. https://doi.org/10.3390/biochem4030014
Chicago/Turabian StyleMagwaza, Nonjabulo Ntombikhona, Aganze Gloire-Aimé Mushebenge, Samuel Chima Ugbaja, Nonkululeko Avril Mbatha, Rene B. Khan, and Hezekiel M. Kumalo. 2024. "Mechanistic Insights into Targeting SARS-CoV-2 Papain-like Protease in the Evolution and Management of COVID-19" BioChem 4, no. 3: 268-299. https://doi.org/10.3390/biochem4030014