scholarly journals Prolactin-Releasing Peptide Differentially Regulates Gene Transcriptomic Profiles in Mouse Bone Marrow-Derived Macrophages

2021 ◽  
Vol 22 (9) ◽  
pp. 4456
Author(s):  
Yulong Sun ◽  
Zhuo Zuo ◽  
Yuanyuan Kuang
Keyword(s):  
Bone Marrow ◽  
Rna Sequencing ◽  
Three Dimensional ◽  
Dynamics Simulation ◽  
Mouse Bone Marrow ◽  
Binding Modes ◽  
Hub Genes ◽  
Noticeable Effect ◽  
Prolactin Releasing Peptide ◽  
Hub Proteins

Prolactin-releasing Peptide (PrRP) is a neuropeptide whose receptor is GPR10. Recently, the regulatory role of PrRP in the neuroendocrine field has attracted increasing attention. However, the influence of PrRP on macrophages, the critical housekeeper in the neuroendocrine field, has not yet been fully elucidated. Here, we investigated the effect of PrRP on the transcriptome of mouse bone marrow-derived macrophages (BMDMs) with RNA sequencing, bioinformatics, and molecular simulation. BMDMs were exposed to PrRP (18 h) and were subjected to RNA sequencing. Differentially expressed genes (DEGs) were acquired, followed by GO, KEGG, and PPI analysis. Eight qPCR-validated DEGs were chosen as hub genes. Next, the three-dimensional structures of the proteins encoded by these hub genes were modeled by Rosetta and Modeller, followed by molecular dynamics simulation by the Gromacs program. Finally, the binding modes between PrRP and hub proteins were investigated with the Rosetta program. PrRP showed no noticeable effect on the morphology of macrophages. A total of 410 DEGs were acquired, and PrRP regulated multiple BMDM-mediated functional pathways. Besides, the possible docking modes between PrRP and hub proteins were investigated. Moreover, GPR10 was expressed on the cell membrane of BMDMs, which increased after PrRP exposure. Collectively, PrRP significantly changed the transcriptome profile of BMDMs, implying that PrRP may be involved in various physiological activities mastered by macrophages.

scholarly journals Transcriptomic Changes in Mouse Bone Marrow-Derived Macrophages Exposed to Neuropeptide FF

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 705
Author(s):  
Yulong Sun ◽  
Yuanyuan Kuang ◽  
Zhuo Zuo
Keyword(s):  
Bone Marrow ◽  
Rna Sequencing ◽  
Biological Activities ◽  
Three Dimensional ◽  
Enrichment Analysis ◽  
Raw 264.7 Cells ◽  
Mouse Bone Marrow ◽  
Binding Modes ◽  
Neuropeptide Ff ◽  
Hub Proteins

Neuropeptide FF (NPFF) is a neuropeptide that regulates various biological activities. Currently, the regulation of NPFF on the immune system is an emerging field. However, the influence of NPFF on the transcriptome of primary macrophages has not been fully elucidated. In this study, the effect of NPFF on the transcriptome of mouse bone marrow-derived macrophages (BMDMs) was explored by RNA sequencing, bioinformatics, and molecular simulation. BMDMs were treated with 1 nM NPFF for 18 h, followed by RNA sequencing. Differentially expressed genes (DEGs) were obtained, followed by GO, KEGG, and PPI analysis. A total of eight qPCR-validated DEGs were selected as hub genes. Subsequently, the three-dimensional (3-D) structures of the eight hub proteins were constructed by Modeller and Rosetta. Next, the molecular dynamics (MD)-optimized 3-D structure of hub protein was acquired with Gromacs. Finally, the binding modes between NPFF and hub proteins were studied by Rosetta. A total of 2655 DEGs were obtained (up-regulated 1442 vs. down-regulated 1213), and enrichment analysis showed that NPFF extensively regulates multiple functional pathways mediated by BMDMs. Moreover, the 3-D structure of the hub protein was obtained after MD-optimization. Finally, the docking modes of NPFF-hub proteins were predicted. Besides, NPFFR2 was expressed on the cell membrane of BMDMs, and NPFF 1 nM significantly activated NPFFR2 protein expression. In summary, instead of significantly inhibiting the expression of the immune-related gene transcriptome of RAW 264.7 cells, NPFF simultaneously up-regulated and down-regulated the gene expression profile of a large number of BMDMs, hinting that NPFF may profoundly affect a variety of cellular processes dominated by BMDMs. Our work provides transcriptomics clues for exploring the influence of NPFF on the physiological functions of BMDMs.

scholarly journals 3D-QSAR, Molecular Docking, and Molecular Dynamics Simulation Study of Thieno[3,2-b]Pyrrole-5- Carboxamide Derivatives as LSD1 Inhibitors

2019 ◽  
Author(s):  
Meilan Huang ◽  
yongtao Xu ◽  
Zihao He ◽  
Min Yang ◽  
Hongyi Liu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Binding Interaction ◽  
Contour Maps

Histone Lysine Specific Demethylase 1 (LSD1) is overexpressed in many cancers and become a new target for anticancer drugs. In recent years, the small molecule inhibitors with various structures targeting LSD1 have been reported. Here we report the binding interaction modes of a series of thieno[3,2-b]pyrrole-5-carboxamides LSD1 inhibitors using molecular docking, three dimensional quantitative structure-activity relationship (3D-QSAR). Comparative molecular field analysis (CoMFA q2=0.783, r2=0.944, r2pred=0.851) and Comparative molecular similarity indices analysis (CoMSIA q2=0.728, r2=0.982, r2pred=0.814) were used to establish 3D-QSAR models, which had good verification and prediction capabilities. Based on the contour maps and the information of molecular docking, 8 novel small molecules were designed in silico, among which compounds D4, D5 and D8 with high predictive activity were subjected to further molecular dynamics simulations (MD), and their possible binding modes were explored. It was found that Asn535 plays a crucial role in stabilizing the inhibitors. Furthermore, the ADME and bioavailability prediction for D4, D5 and D8 were carried out. The results would provide valuable guidance for designing new reversible LSD1 inhibitors in the future.

Characteristics of three-dimensional prospectively isolated mouse bone marrow mesenchymal stem/stromal cell aggregates on nanoculture plates

2016 ◽  
Vol 366 (1) ◽  
pp. 113-127 ◽  
Author(s):  
Chizuka Obara ◽  
Ken-ichi Tomiyama ◽  
Kazuya Takizawa ◽  
Rafiqul Islam ◽  
Takeshi Yasuda ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Stromal Cell ◽  
Three Dimensional ◽  
Mouse Bone Marrow ◽  
Cell Aggregates

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes.</div><div><br></div><div>In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.</div>

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