Exploration and evaluation of bioactive phytocompounds against BRCA proteins by<i>in silico</i>approach

Prabhavathi, H; Dasegowda, K. R.; Renukananda, K. H.; Lingaraju, K.; Naika, H. Raja

doi:10.1080/07391102.2020.1790424

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…The average drug-likeness score was found to be 0.663 ± 0.118, and the scores of the generated ligands were found to be in the range with FDA reference drugs used by MolSoft. Drugs that have drug–likeness scores greater than 0 are considered to have drug-like properties ( Prabhavathi et al, 2020 ). In Figure 8B , ligands that fall in the green region have a binding affinity greater than −7.9 kcal/mol and have a drug-likeness score greater than 0.…”

Section: Resultsmentioning

confidence: 99%

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Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning

Prabhakaran

Hebbani²,

Menon

et al. 2023

Front. Microbiol.

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The recent emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing the coronavirus disease (COVID-19) has become a global public health crisis, and a crucial need exists for rapid identification and development of novel therapeutic interventions. In this study, a recurrent neural network (RNN) is trained and optimized to produce novel ligands that could serve as potential inhibitors to the SARS-CoV-2 viral protease: 3 chymotrypsin-like protease (3CLpro). Structure-based virtual screening was performed through molecular docking, ADMET profiling, and predictions of various molecular properties were done to evaluate the toxicity and drug-likeness of the generated novel ligands. The properties of the generated ligands were also compared with current drugs under various phases of clinical trials to assess the efficacy of the novel ligands. Twenty novel ligands were selected that exhibited good drug-likeness properties, with most ligands conforming to Lipinski’s rule of 5, high binding affinity (highest binding affinity: −9.4 kcal/mol), and promising ADMET profile. Additionally, the generated ligands complexed with 3CLpro were found to be stable based on the results of molecular dynamics simulation studies conducted over a 100 ns period. Overall, the findings offer a promising avenue for the rapid identification and development of effective therapeutic interventions to treat COVID-19.

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Section: Resultsmentioning

confidence: 99%

“…These bioactivity scores provide an additional metric for evaluating the drug-like properties of the ligands ( Vardhan and Sahoo, 2020 ). Another online tool Molsoft 10 was used to evaluate the drug-likeness score of the ligands ( Prabhavathi et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning

Prabhakaran

Hebbani²,

Menon

et al. 2023

Front. Microbiol.

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“…To understand the catabolic activity of the enzymes toward lignin and cellulose substrates, depending on their binding affinity and RMSD values, molecular docking was conducted individually [ 42 ] following the optimization of all the substrates and enzymes. The “2BOQ-lignin” and “1OA9-cellulose gel” resulted in the best fitting scores of −8.00 and −6.7 (kcal/mol), respectively ( Table 2 ).…”

Section: Discussionmentioning

confidence: 99%

Catabolic profiling of selective enzymes in the saccharification of non-food lignocellulose parts of biomass into functional edible sugars and bioenergy: An in silico bioprospecting

Paul¹,

Azad²,

Rahman³

et al. 2022

J Adv Vet Anim Res

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“…Mutations can cause changes in the structure and stability of drug target proteins, resulting in changes in substrate and ligand affinity ( Akbulut, 2020 b; Xavier et al, 2021 ). In silico studies have achieved great success in recent years due to their contributions to the design and development of effective drug molecules in a short time, taking these changes into account ( Prabhavathi et al, 2020 ; Das et al, 2021 ). In this study, the data obtained showed that the decrease in protein stability due to mutations increased the affinity for the substrate and VR4 ligand, a cysteine protease inhibitor.…”

Section: Discussionmentioning

confidence: 99%

Investigation of changes in protein stability and substrate affinity of 3CL-protease of SARS-CoV-2 caused by mutations

Akbulut

2022

Genet. Mol. Biol.

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3CL pro of SARS-CoV-2 is one of the enzymes required for the replication process of the virus responsible for the COVID-19 pandemic. In this study, changes in protein stability and substrate affinity caused by mutations were investigated to stir the development of potent inhibitors. Sequence data of samples were obtained from the NCBI Virus database. Mutation analyses were performed with RDP4 and MegaX. 3CL pro tertiary models were created using Robetta. Molecular docking for peptidomimetic substrate and inhibitor ligand was done with Autodock v4.2 and Haddock v2.4. Protein stability analysis was performed using mCSM stability and DynaMut2. Twenty-four missense mutations in 3CL pro were identified in this study. Changes in the 3CL pro structure induced by the mutations Met49Thr, Leu167Ser, and Val202Ala resulted in significant levels of instability (-2.029,-2.612,-2.177 kcal.mol -1 , respectively). The lowest interaction energy for substrate was -58.7 kcal.mol -1 and -62.6 kcal.mol -1 in wild-type and mutant, respectively. The lowest docking energy for ligand was -6.19 and -9.52 kcal.mol -1 for wild-type and mutant, respectively. This study reports for the first time that mutations cause increased substrate affinity of 3CL pro from SARS-CoV-2. This research provides important data for the development of potent peptidomimetic inhibitors for the treatment of COVID-19.

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Exploration and evaluation of bioactive phytocompounds against BRCA proteins byin silicoapproach

Cited by 14 publications

References 41 publications

Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning

Insilico generation of novel ligands for the inhibition of SARS-CoV-2 main protease (3CLpro) using deep learning

Catabolic profiling of selective enzymes in the saccharification of non-food lignocellulose parts of biomass into functional edible sugars and bioenergy: An in silico bioprospecting

Investigation of changes in protein stability and substrate affinity of 3CL-protease of SARS-CoV-2 caused by mutations

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