Searching for potential drugs against SARS-CoV-2 through virtual screening on several molecular targets

Almeida, Joyce S. F. D. de; Botelho, Fernanda D.; Souza, Felipe Rodrigues de; Santos, Marcelo C. dos; Gonçalves, Arlan da Silva; Rodrigues, Rodrigo Leonard Barboza; Cardozo, Monique; Kitagawa, Daniel A. S.; Vieira, Leandro A.; Silva, Raphael S. F.; Cavalcante, Samir F. A.; Bastos, Leonardo da Costa; Nogueira, Mariana de Oliveira Tonelli; Santana, Priscila Ivo Rubim de; Brum, Juliana de O. C.; Nepovimová, Eugenie; Kuča, Kamil; LaPlante, Steven R.; Galante, Erick; França, Tanos C. C.

doi:10.1080/07391102.2020.1869096

Cited by 5 publications

(6 citation statements)

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“…Remdesivir is a phosphoramide which, although reported as targeting RNA polymerase (RdRp) [42], is predicted by our model to have moderate M pro inhibitory activity (IC 50 = 34.4uM). This seems to be consistent with previous docking [9] and Molecular Dynamics studies [43] indicating that Remdesivir is a multi-target drug and does in some degree interact with M pro . Finally, for Omipalisib high activity is predicted (IC 50 = 11uM) by the model, which is in agreement with previous works reporting that this molecule targets M pro exhibiting strong inhibition of viral replication [16].…”

Section: Modelssupporting

confidence: 92%

Predicting the Activity of Molecules against SARS-CoV-2 using Machine Learning Techniques and Stacked Generalization

Gousiadou

Sarimveis

2022

Preprint

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As the pandemic caused by the virus SARS-CoV-2 was rapidly evolving, efforts for the design of new, target-specific molecules active against the viral replication-related enzymes were intensified. On these grounds, the Covid-Moonshot project was launched in March 2020 as a worldwide scientific collaboration aiming to design strong inhibitors against the conserved coronavirus protease Mpro, and subsequently to measure their activity through High Throughput Screening (HTS) assays. As a result, more than 21000 structurally diverse molecules exploring a large chemical space were submitted to the Moonshot project’s website and approximately 10% were evaluated for their inhibitory potency against Mpro. In the present work, profiting from the large chemical space made available through the project and with the advantage of using activity data measured with consistent experimental protocols, we curated informative datasets with which we built and vigorously validated a QSAR stacked regression model as an ensemble of Extreme Gradient Boosting (XGB) and Neural Network (NN) algorithms for estimating the magnitude of the inhibitory potency of molecules against Mpro. The data used for model development and validation contained IC50 values measured with the Fluorescence assay and reported in the project’s on-line database. Our QSAR regression ensemble showed good predictive performance and generalization. Additionally, a set of 78 highly relevant features– including two new engineered - were identified and successfully linked to the inhibitory activity of the modelled compounds. Our findings further suggested that the presence of a halogen-acetamide moiety strongly affected the inhibitory potency of the molecules considered in the present study.

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Section: Modelssupporting

confidence: 92%

Predicting the Activity of Molecules against SARS-CoV-2 using Machine Learning Techniques and Stacked Generalization

Gousiadou

Sarimveis

2022

Preprint

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“…As in previous studies [ 22 , 23 ], the search space for molecular docking was defined to include all residues of the RBD of SC2Spike that interact with Hss ACE-2: Lys417, Gly446, Tyr449, Tyr453, Leu455, Phe456, Ala475, Phe486, Asn487, Tyr489, Gln493, Gly496, Gln498, Thr500, Asn501, Gly502, and Tyr505 [ 3 , 7 ]. The search space was a sphere with a radius of 23 Å and its center located at coordinates x = 215, y = 211, and z = 217.25.…”

Section: Methodsmentioning

confidence: 99%

“…The search space was a sphere with a radius of 23 Å and its center located at coordinates x = 215, y = 211, and z = 217.25. Each of the 10 ligands shown in Table S1 was docked into this search space using the same docking protocol as before [ 22 ], with 6 molecular docking runs conducted and the best 30 poses per run returned, resulting in 180 poses per ligand to be analyzed. The best-ranked poses, based on their MolDock scores (which estimate protein–ligand interaction energy) and the establishment of intermolecular interactions with residues of the RBD, were selected for molecular dynamics (MD) simulations.…”

Section: Methodsmentioning

confidence: 99%

Molecular modeling study of natural products as potential bioactive compounds against SARS-CoV-2

et al. 2023

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Context The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the COVID-19 infection and responsible for millions of victims worldwide, remains a significant threat to public health. Even after the development of vaccines, research interest in the emergence of new variants is still prominent. Currently, the focus is on the search for effective and safe drugs, given the limitations and side effects observed for the synthetic drugs administered so far. In this sense, bioactive natural products that are widely used in the pharmaceutical industry due to their effectiveness and low toxicity have emerged as potential options in the search for safe drugs against COVID-19. Following this line, we screened 10 bioactive compounds derived from cholesterol for molecules capable of interacting with the receptor-binding domain (RBD) of the spike protein from SARS-CoV-2 (SC2Spike), responsible for the virus’s invasion of human cells. Rounds of docking followed by molecular dynamics simulations and binding energy calculations enabled the selection of three compounds worth being experimentally evaluated against SARS-CoV-2. Methods The 3D structures of the cholesterol derivatives were prepared and optimized using the Spartan 08 software with the semi-empirical method PM3. They were then exported to the Molegro Virtual Docking (MVD®) software, where they were docked onto the RBD of a 3D structure of the SC2Spike protein that was imported from the Protein Data Bank (PDB). The best poses obtained from MVD® were subjected to rounds of molecular dynamics simulations using the GROMACS software, with the OPLS/AA force field. Frames from the MD simulation trajectories were used to calculate the ligand’s free binding energies using the molecular mechanics – Poisson-Boltzmann surface area (MM-PBSA) method. All results were analyzed using the xmgrace and Visual Molecular Dynamics (VMD) software. Supplementary information The online version contains supplementary material available at 10.1007/s00894-023-05586-5.

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“…The information about the 3D structures of these proteins retrieved from the Protein Data Bank (PDB) [25] for this study are summarized in Table 1. The software used was Molegro Virtual Docker® (MVD), and the protocol was the same validated before [26,27]. The 3D structures of the ligands were constructed in the Spartan 08 software [28], using the semi-empirical Parametric Method 3 (PM3) [29] for geometry optimization and the Natural Population Analysis (NPA) [30] method for atomic charges calculations.…”

Section: Docking Studiesmentioning

confidence: 99%

“…As reported before [26,27], we estimated the binding free energies of each ligand in the respective protein binding pocket after the MD simulations through the MM-PBSA approach [41], which consider the vacuum potential energy, including bonded and non-bonded interactions and free energy of solvation. The g_mmpbsa tool [41] suited for the GROMACS package [40] was used for this task.…”

Section: Molecular Dynamic Simulationsmentioning

confidence: 99%

Modeling studies on the role of vitamins B1 (thiamin), B3 (nicotinamide), B6 (pyridoxamine), and caffeine as potential leads for the drug design against COVID-19

Aghamohammadi

Sirouspour²,

Gonçalves

et al. 2022

J Mol Model

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In response to the COVID-19 pandemic, and the lack of effective and safe antivirals against it, we adopted a new approach in which food supplements with vital antiviral characteristics, low toxicity, and fast excretion have been targeted. The structures and chemical properties of the food supplements were compared to the promising antivirals against SARS-COV-2. Our goal was to exploit the food supplements to mimic the topical antivirals' functions but circumventing their severe side effects, which has limited the necessary dosage needed to exhibit the desired antiviral activity. On this line, after a comparative structural analysis of the chemicals mentioned above, and investigation of their potential mechanisms of action, we selected caffeine and some compounds of the vitamin B family and further applied molecular modeling techniques to evaluate their interactions with the RDB domain of the Spike protein of SARS-CoV-2 (SC2Spike) and its corresponding binding site on human ACE-2 (HssACE2). Our results pointed to vitamins B1 and B6 in the neutral form as potential binders to the HssACE2 RDB binding pocket that might be able to impair the SARS-CoV-2 mechanism of cell invasion, qualifying as potential leads for experimental investigation against COVID-19.

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Searching for potential drugs against SARS-CoV-2 through virtual screening on several molecular targets

Cited by 5 publications

References 50 publications

Predicting the Activity of Molecules against SARS-CoV-2 using Machine Learning Techniques and Stacked Generalization

Predicting the Activity of Molecules against SARS-CoV-2 using Machine Learning Techniques and Stacked Generalization

Molecular modeling study of natural products as potential bioactive compounds against SARS-CoV-2

Modeling studies on the role of vitamins B1 (thiamin), B3 (nicotinamide), B6 (pyridoxamine), and caffeine as potential leads for the drug design against COVID-19

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