Classification of VUS and unclassified variants in BRCA1 BRCT repeats by molecular dynamics simulation

Sinha, Siddharth; Wang, San Ming

doi:10.1016/j.csbj.2020.03.013

Cited by 67 publications

(39 citation statements)

References 59 publications

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“…The results showed that the majority of P53 variants had similar structure dynamics for VUS. Using the cut-offs (H bond < 300, RMSD > 0.3 nm, RMSF > 0.25 nm, Gyration (Rg) > 1.7, SASA > 100 nm 3 ) that successfully differentiated between deleterious and non-deleterious variants in BRCA1 BRCT domain [18] , we observed that all except RMSF classified P53 variants into deleterious variants ( Supplementary Table S2 ). In total, MDS (H Bond and RMSD only) was about to identify 8 Pathogenic variants with structural impact.…”

Section: Resultsmentioning

confidence: 99%

“…2c–4c ). Our previous study demonstrated that MDS was sensitive in classifying the VUS in the BRCA1 BRCT domain [18] . The discrepancy is likely due to the size-difference between P53 DBD (213 residues) and BRCA1 BRCT domain (95 residues) that MDS has limited power to differentiate the structural changes in a larger protein structure.…”

Section: Discussionmentioning

confidence: 99%

“…( Table 1 ). Variants higher than 3.376 were set as the cut-off for deleterious, lower than 3.376 as ‘undefined’ [18] .…”

Section: Methodsmentioning

confidence: 99%

“…It analyzes physical movement of atoms and molecules after interacting for a fixed time period, and the trajectories are used to determine macroscopic thermodynamics properties of the targeted molecular structure. MDS has been widely used to analyze protein structural dynamics [15] , [16] , [17] , and we also successfully applied MDS to characterize the genetic variants in BRCA1 BRCT domain [18] . Although MDS or Ramachandran plot alone provides an independent measure in protein structure, we reasoned that combination of Ramachandran Plot and MDS, we named it RP-MDS, could enhance the capacity of detecting the impact of genetic variants on protein structure.…”

Section: Introductionmentioning

confidence: 99%

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Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Tam

Sinha

Wang

2020

Computational and Structural Biotechnology Journal

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…( Table 1 ). Variants higher than 3.376 were set as the cut-off for deleterious, lower than 3.376 as ‘undefined’ [18] .…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Tam

Sinha

Wang

2020

Computational and Structural Biotechnology Journal

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“…The loss of compactness affects the stability of the complex by introducing weak intermolecular bonds. When the Rg of a complex is relatively steady, the compactness of the protein–ligand complex is high, and the protein is folded well, whereas the Rg value changes over time if the protein unfolds [ 45 , 116 ].…”

Section: Methodsmentioning

confidence: 99%

Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin

et al. 2021

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The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2) has impacted negatively on public health and socioeconomic status, globally. Although, there are currently no specific drugs approved, several existing drugs are being repurposed, but their successful outcomes are not guaranteed. Therefore, the search for novel therapeutics remains a priority. We screened for inhibitors of the SARS-CoV-2 main protease and the receptor-binding domain of the spike protein from an integrated library of African natural products, compounds generated from machine learning studies and antiviral drugs using AutoDock Vina. The binding mechanisms between the compounds and the proteins were characterized using LigPlot+ and molecular dynamics simulations techniques. The biological activities of the hit compounds were also predicted using a Bayesian-based approach. Six potential bioactive molecules NANPDB2245, NANPDB2403, fusidic acid, ZINC000095486008, ZINC0000556656943 and ZINC001645993538 were identified, all of which had plausible binding mechanisms with both viral receptors. Molecular dynamics simulations, including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations revealed stable protein-ligand complexes with all the compounds having acceptable free binding energies <−15 kJ/mol with each receptor. NANPDB2245, NANPDB2403 and ZINC000095486008 were predicted as antivirals; ZINC000095486008 as a membrane permeability inhibitor; NANPDB2403 as a cell adhesion inhibitor and RNA-directed RNA polymerase inhibitor; and NANPDB2245 as a membrane integrity antagonist. Therefore, they have the potential to inhibit viral entry and replication. These drug-like molecules were predicted to possess attractive pharmacological profiles with negligible toxicity. Novel critical residues identified for both targets could aid in a better understanding of the binding mechanisms and design of fragment-based de novo inhibitors. The compounds are proposed as worthy of further in vitro assaying and as scaffolds for the development of novel SARS-CoV-2 therapeutic molecules.

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Computational Screening of Neuropilin‐1 Unveils Novel Potential Anti‐SARS‐CoV‐2 Therapeutics

Afiadenyo,

Adams,

Agoni

et al. 2023

Chemistry & Biodiversity

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Neuropilin 1 (NRP‐1) inhibition has shown promise in reducing the infectivity of severe acute respiratory syndrome‐coronavirus‐2 (SARS‐CoV‐2) and preventing the virus entry into nerve tissues, thereby mitigating neurological symptoms in COVID‐19 patients. In this study, we employed virtual screening, including molecular docking, Molecular Dynamics (MD) simulation, and Molecular Mechanics‐Poisson Boltzmann Surface Area (MM‐PBSA) calculations, to identify potential NRP‐1 inhibitors. From a compendium of 1930 drug‐like natural compounds, we identified five potential leads: CNP0435132, CNP0435311, CNP0424372, CNP0429647, and CNP0427474, displaying robust binding energies of ‐8.2,‐8.1,‐10.7,‐8.2, and ‐8.2 kcal/mol, respectively. These compounds demonstrated interactions with critical residues Tyr297, Trp301, Thr316, Asp320, Ser346, Thr349, and Tyr353 located within the b1 subdomain of NRP‐1. Furthermore, MD simulations and MM‐PBSA calculations affirmed the stability of the complexes formed, with average root mean square deviation, radius of gyration, and solvent accessible surface area values of 0.118 nm,1.516 nmand 88.667nm² respectively. Notably, these lead compounds were estimated to penetrate the blood‐brain barrier and displayed antiviral properties, with Pa values ranging from 0.414 to 0.779. The antagonistic effects of these lead compounds merit further investigation, as they hold the potential to serve as foundational scaffolds for the development of innovative therapeutics aimed at reducing the neuroinfectivity of SARS‐CoV‐2.

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Classification of VUS and unclassified variants in BRCA1 BRCT repeats by molecular dynamics simulation

Cited by 67 publications

References 59 publications

Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Combining Ramachandran plot and molecular dynamics simulation for structural-based variant classification: Using TP53 variants as model

Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin

Computational Screening of Neuropilin‐1 Unveils Novel Potential Anti‐SARS‐CoV‐2 Therapeutics

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