Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods

Gasparini, P.; Philot, Eric Allison; Pantaleão, Simone Queiroz; Torres-Bonfim, N.E.S.M.; Kliousoff, A.; Quiroz, R.C.N.; Pérahia, David; Simões, Rafael Plana; Magro, Ângelo J.; Scott, Ana Lígia

doi:10.1016/j.jmgm.2023.108443

Cited by 3 publications

(1 citation statement)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The evaluation of molecular oxidation susceptibility was accomplished with Condensedto-Atoms Fukui Indexes (CAFIs): (i) f k + = q k (N + 1) − q k (N) for nucleophilic attack on atom k; (ii) f k − = q k (N) − q k (N − 1) for electrophilic attack on atom k; and (iii) f k 0 = 1 / 2 [q k (N + 1) − q k (N − 1)] for radical attack on atom k. Furthermore, q k (N + 1), q k (N), and q k (N − 1) represent the electronic Hirshfeld population on the k-th atom of anionic, cationic, and neutral species, respectively, of the studied compound [48][49][50]. CAFIs have been successfully used to identify oxidation, reduction, and charge exchange mechanisms in chemical reactions [51][52][53]. In addition, the location and spatial distribution of frontier molecular orbitals, specifically the highest occupied molecular orbital (HOMO), was illustrated using the Gabedit computational package [54].…”

Section: Theoretical Analysismentioning

confidence: 99%

Determination of Fluoxetine in Weight Loss Herbal Medicine Using an Electrochemical Sensor Based on rGO-CuNPs

Melaré,

Barreto,

Silva

et al. 2023

Molecules

View full text Add to dashboard Cite

The rising popularity of herbal medicine as a weight loss remedy, fueled by misleading propaganda, raises concerns about the manufacturing processes and potential inclusion of controlled substances such as fluoxetine (FLU). The objective of this work is to develop and evaluate the performance of an electrochemical device by modifying a glassy carbon electrode (GC) with a nanocomposite based on reduced graphene oxide (rGO) and copper nanoparticles (CuNPs) for detecting FLU in manipulated herbal medicines. Scanning electron microscopy (FEG-SEM) and cyclic voltammetry (CV) were applied for morphological and electrochemical characterization and analysis of the composite’s electrochemical behavior. Under optimized conditions, the proposed sensor successfully detected FLU within the range of 0.6 to 1.6 µmol L−1, showing a limit of detection (LOD) of 0.14 µmol L−1. To determine the presence of FLU in herbal samples, known amounts of the analytical standard were added to the sample, and the analyses were performed using the standard addition method, yielding recoveries between −2.13 and 2.0%.

show abstract

Section: Theoretical Analysismentioning

confidence: 99%

Determination of Fluoxetine in Weight Loss Herbal Medicine Using an Electrochemical Sensor Based on rGO-CuNPs

Melaré,

Barreto,

Silva

et al. 2023

Molecules

View full text Add to dashboard Cite

show abstract

Exploring the antiviral activity of α-ketoamides compounds through electronic structure calculations: a structure-activity relationship study

Deienno,

Gomes,

Rossi

et al. 2023

Journal of Biomolecular Structure and Dynamics

View full text Add to dashboard Cite

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

et al. 2023

View full text Add to dashboard Cite

This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission’s stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.

show abstract

Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods

Cited by 3 publications

References 66 publications

Determination of Fluoxetine in Weight Loss Herbal Medicine Using an Electrochemical Sensor Based on rGO-CuNPs

Determination of Fluoxetine in Weight Loss Herbal Medicine Using an Electrochemical Sensor Based on rGO-CuNPs

Exploring the antiviral activity of α-ketoamides compounds through electronic structure calculations: a structure-activity relationship study

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

Contact Info

Product

Resources

About