The effect of β-glucan and its potential analog on the structure of Dectin-1 receptor

et al. 2020

Cells

Self Cite

The S100A1 protein, involved in various physiological activities through the binding of calcium ions (Ca2+), participates in several protein-protein interaction (PPI) events after Ca2+-dependent activation. The present work investigates Ca2+-dependent conformational changes in the helix-EF hand-helix using the molecular dynamics (MD) simulation approach that facilitates the understanding of Ca2+-dependent structural and dynamic distinctions between the apo and holo forms of the protein. Furthermore, the process of ion binding by inserting Ca2+ into the bulk of the apo structure was simulated by molecular dynamics. Expectations of the simulation were demonstrated using cluster analysis and a variety of structural metrics, such as interhelical angle estimation, solvent accessible surface area, hydrogen bond analysis, and contact analysis. Ca2+ triggered a rise in the interhelical angles of S100A1 on the binding site and solvent accessible surface area. Significant configurational regulations were observed in the holo protein. The findings would contribute to understanding the molecular basis of the association of Ca2+ with the S100A1 protein, which may be an appropriate study to understand the Ca2+-mediated conformational changes in the protein target. In addition, we investigated the expression profile of S100A1 in myoblast differentiation and muscle regeneration. These data showed that S100A1 is expressed in skeletal muscles. However, the expression decreases with time during the process of myoblast differentiation.

Section: Discussionsupporting

confidence: 85%

Understanding Calcium-Dependent Conformational Changes in S100A1 Protein: A Combination of Molecular Dynamics and Gene Expression Study in Skeletal Muscle

Chaturvedi

Ahmad

et al. 2020

Cells

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“…In our previous study, we have performed similar docking and molecular dynamics approaches (Chaturvedi et al, 2017). In addition, for securing convergence, clusters distribution was calculated, which is characterized by a sequence of conformations, each persisting longer time, suggesting a stable distribution of both systems; (a) CPS free, and (b) CPS loaded IL-18 protein.…”

Section: Discussionmentioning

confidence: 99%

“…The binding free energy of the IL‐8‐CPS complex molecules was computed using molecular mechanics Poisson–Boltzman surface area (MM‐PBSA) method adopted from Kumari, Kumar, Open Source Drug Discovery, and Lynn () and Chaturvedi et al ().…”

Section: Methodsmentioning

confidence: 99%

“…Previously, several attempts were used for molecular docking, molecular dynamics, and other state of the art in silico approaches towards drug designing and development (Ávila et al, 2014;Chaturvedi, Yadav, Pandey, & Tripathi, 2017;Gonzalez & Barreto, 2013;Martín-Jiménez, Salazar-Barreto, Barreto, & González, 2017;Pillai et al, 2017;Shakour et al, 2018;Sierra Bello, Gonzalez, Capani, & Barreto, 2012;B. S. Yadav et al, 2012;P.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Protein modeling, molecular network and molecular dynamics study of newly sequenced interleukin‐18 (IL‐18) gene in Mus musculus

Chaturvedi

Journal Cellular Physiology

et al. 2019

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Interleukin‐18 (IL‐18) belongs to the superfamily of IL‐1 protein and exerts a pleiotropic pro‐inflammatory effect on the body. Generally, this protein is significantly involved in immune defense during infection in cells, but sometimes its anomalous activities produce some inflammatory diseases like rheumatoid arthritis and Crohn’s disease. In the present study, the IL‐18 gene was isolated from mice and was subsequently cloned and sequenced. Further, the network analysis was carried out to explore the functional role of IL‐18 protein in animals. The 3D protein structure of the IL‐18 protein was generated and docked with appropriate 3‐([3‐cholamidopropyl]dimethylammonio)‐1‐propanesulfonate (CPS) ligand. Later the complex structure of the protein was subjected to molecular dynamics simulation (MDS) for 50 ns to determine the effect of ligand on protein. The network analysis explored the correlation of IL‐18 protein with others proteins and their involvement in the different significant pathway to defend the cell from various diseases. As confirmed by MDS, the CPS:IL‐18 complex was found to be highly stable. Our results further indicated that CPS ligand has the potential to act as a drug molecule, in future, for counteracting IL‐18 activity. To date, no structural details were available for animal IL‐18. Hence, the finding of this study will be useful in broadening the horizon towards a better understanding of the functional and structural aspects of IL‐18 in animals.

“…Dimensionality reduction of MD data with the use of PCA was also first used in the early 90s ( Ichiye and Karplus, 1991 ; Amadei et al, 1993 ) and since that time its application in MD output analysis has been constantly growing ( Das and Mukhopadhyay, 2007 ; Chiappori et al, 2010 ; Kim et al, 2010 ; Casoni et al, 2013 ; Ng et al, 2013 ; Novikov et al, 2013 ; Bhakat et al, 2014 ; Sittel et al, 2014 ; Ernst et al, 2015 ; Chaturvedi et al, 2017 ; Cossio-Pérez et al, 2017 ; Fakhar et al, 2017 ; Chen, 2018 ; Cholko et al, 2018 ; An et al, 2019 ; Barletta et al, 2019 ; Girdhar et al, 2019 ; Karnati and Wang, 2019 ; Lipiński et al, 2019 ; Martínez-Archundia et al, 2019 ; Wu et al, 2019 ; Magudeeswaran and Poomani, 2020 ; David et al, 2021 ; Majumder and Giri, 2021 ). Although PCA is the most popular approach applied to handle MD trajectories, other data dimensionality reduction methods are also used in the MD field.…”

Section: Clustering and Reduction Of Data Dimensionalitymentioning

confidence: 99%

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

Baltrukevich

Podlewska

2022

Front. Pharmacol.

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.