Identification of SARS‐CoV‐2 Papain‐like Protease (PLpro) Inhibitors Using Combined Computational Approach**

Senćanski, Milan; Perovic, Vladimir; Мilićević, Jelena; Todorović, Tamara R.; Prodanović, Radivoje; Veljković, Veljko; Paessler, Slobodan; Glišić, Sanja

doi:10.1002/open.202100248

Cited by 12 publications

(11 citation statements)

References 60 publications

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“…Consequently, this observation has given rise to the EIIP/AQVN criteria, which are employed for the virtual screening of molecular libraries in search of compounds exhibiting similar therapeutic properties [33]. Previous investigations have validated the effectiveness of the EIIP/AQVN approach in identifying inhibitors against a range of viral targets, including influenza [34,35]. These findings were further verified experimentally [34,36,37].…”

Section: Discussionmentioning

confidence: 92%

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach

Perovic,

Stevanovic,

Bukreyeva

et al. 2024

IJMS

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The influenza A virus nonstructural protein 1 (NS1), which is crucial for viral replication and immune evasion, has been identified as a significant drug target with substantial potential to contribute to the fight against influenza. The emergence of drug-resistant influenza A virus strains highlights the urgent need for novel therapeutics. This study proposes a combined theoretical criterion for the virtual screening of molecular libraries to identify candidate NS1 inhibitors. By applying the criterion to the ZINC Natural Product database, followed by ligand-based virtual screening and molecular docking, we proposed the most promising candidate as a potential NS1 inhibitor. Subsequently, the selected natural compound was experimentally evaluated, revealing measurable virus replication inhibition activity in cell culture. This approach offers a promising avenue for developing novel anti-influenza agents targeting the NS1 protein.

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Section: Discussionmentioning

confidence: 92%

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach

Perovic,

Stevanovic,

Bukreyeva

et al. 2024

IJMS

Self Cite

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“…Compound 30, Sacubitril, was proposed as one of the 44 candidates for experimental assay from a molecular docking calculation. 37) Compound 48, Enasidenib, showed a high binding score with 147 other candidates in a docking simulation. 38) Therefore, previous screening studies on the approved drugs were limited to the PL protease inhibitors.…”

Section: Discussionmentioning

confidence: 99%

<i>In Silico</i> Identification of Inhibitory Compounds for SARS-Cov-2 Papain-Like Protease

Miwa,

Guo,

Hata

et al. 2023

CHEMICAL & PHARMACEUTICAL BULLETIN

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Virtual screening with high-performance computers is a powerful and cost-effective technique in drug discovery. A chemical database is searched to find candidate compounds firmly bound to a target protein, judging from the binding poses and/or binding scores. The severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) infectious disease has spread worldwide for the last three years, causing severe slumps in economic and social activities. SARS-Cov-2 has two viral proteases: 3-chymotrypsin-like (3CL) and papain-like (PL) protease. While approved drugs have already been released for the 3CL protease, no approved agent is available for PL protease. In this work, we carried out in silico screening for the PL protease inhibitors, combining docking simulation and molecular mechanics calculation. Docking simulations were applied to 8,820 molecules in a chemical database of approved and investigational compounds. Based on the binding poses generated by the docking simulations, molecular mechanics calculations were performed to optimize the binding structures and to obtain the binding scores. Based on the binding scores, 57 compounds were selected for in vitro assay of the inhibitory activity. Five inhibitory compounds were identified from the in vitro measurement. The predicted binding structures of the identified five compounds were examined, and the significant interaction between the individual compound and the protease catalytic site was clarified. This work demonstrates that computational virtual screening by combining docking simulation with molecular mechanics calculation is effective for searching candidate compounds in drug discovery.

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“…Such small-molecule drugs would act in synergy with vaccination, stopping viral replication, and in turn either treating an established disease or acting as prophylaxis [ 27 ]. The performed study draws inspiration from previous work conducted by Jukič et al on 3CL pro of SARS-CoV-2, as well as Ghosh et al and Senčanski et al on SARS-CoV-2 PL pro , as these approaches yielded promising results [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 87%

Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PLpro) Inhibitor

Kralj,

Jukič,

Bahun

et al. 2024

Pharmaceutics

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The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its companion disease, COVID-19, has reminded us of the importance of basic coronaviral research. In this study, a comprehensive approach using molecular docking, in vitro assays, and molecular dynamics simulations was applied to identify potential inhibitors for SARS-CoV-2 papain-like protease (PLpro), a key and underexplored viral enzyme target. A focused protease inhibitor library was initially created and molecular docking was performed using CmDock software (v0.2.0), resulting in the selection of hit compounds for in vitro testing on the isolated enzyme. Among them, compound 372 exhibited promising inhibitory properties against PLpro, with an IC50 value of 82 ± 34 μM. The compound also displayed a new triazolopyrimidinyl scaffold not yet represented within protease inhibitors. Molecular dynamics simulations demonstrated the favorable binding properties of compound 372. Structural analysis highlighted its key interactions with PLpro, and we stress its potential for further optimization. Moreover, besides compound 372 as a candidate for PLpro inhibitor development, this study elaborates on the PLpro binding site dynamics and provides a valuable contribution for further efforts in pan-coronaviral PLpro inhibitor development.

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Identification of SARS‐CoV‐2 Papain‐like Protease (PLpro) Inhibitors Using Combined Computational Approach**

Cited by 12 publications

References 60 publications

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach

<i>In Silico</i> Identification of Inhibitory Compounds for SARS-Cov-2 Papain-Like Protease

Identification of Triazolopyrimidinyl Scaffold SARS-CoV-2 Papain-Like Protease (PLpro) Inhibitor

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