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Computer codes (in Python) for 'The reproduction number and its probability distribution for stochastic viral dynamics'

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opencc-zeroFeb 2019View details →

Supplementary material 1 from: Solé R, Gripenberg S, Lewis OT, Markesteijn L, Barrios H, Ratz T, Ctvrtecka R, Butterill PT, Segar ST, Metz MA, Dahl C, Rivera M, Viquez K, Ferguson W, Guevara M, Basset Y (2019) The role of herbivorous insects and pathogens in the regeneration dynamics of Guazuma ulmifolia in Panama. Nature Conservation 32: 81-101. https://doi.org/10.3897/natureconservation.32.30108

Supplementary tables S1-S4, Appendix S1, Plate S1

Figure 3 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501

Figure 3 A. Visualization of interactions between docosahexaenoic acid and receptors (GPR120 (4GRV)) and B. Visualization of molecular docking between the receptor GPR120 (4GRV) and docosahexaenoic acid.

Figure 2 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501

Figure 2 A. Visualization of interactions between Neurotensin 8–13 and receptors (4GRV) and B. Visualization of molecular docking between the receptor 4GRV and natural ligand.

Figure 1 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501

Figure 1 A. Neurotensin receptor (4GRV) and B. Overlay of the docked pose of Neurotensin 8–13 with the co-crystallized ligand 4GRV.

Figure 4 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501

Figure 4 RMSD (a) and RMSF (b) value of docosahexaenoic acid (blue), eicosapentaenoic acid (maroon), and heneicosapentaenoic acid (green).

Figure 5 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501

Figure 5 SASA plot of docosahexaenoic acid (blue), eicosapentaenoic acid (maroon), and heneicosapentaenoic acid (green).

openDec 2023View details →

Dynamic profiling and binding affinity prediction of NBTI antibacte-rials against DNA gyrase enzyme by multidimensional machine learning and molecular dynamics simulations

The chemical libraries used in this study comprised of 199 and 133 structurally diverse novel bacterial topoisomerase inhibitors (alias NBTIs), with experimentally determined in vitro antibacterial potencies against Staphylococcus aureus DNA gyrase (IC50=0.007-50 µM) and Escherichia coli DNA gyrase (IC50=0.020-100 µM), respectively (named as NBTISA and NBTIEC), were compiled from the literature as *.sdf file format. The chemical structures comprising both NBTI libraries were initially sketched by using ChemDraw Professional 20.1.1 suite and subsequently energetically minimized utilizing Discovery Studio’s integrated Merck Molecular Force Field (MMFF) module. Moreover 4D ligands ensembles of both libraries ready to be used for multidimensional QSAR modeling are available, as well.

Codes and images associated with "Neuronal dynamics orchestrate cerebrospinal fluid perfusion and brain clearance"

MATLAB scripts and representative images associated with the publication.

SOAPy: a Python package to dissect spatial architecture, dynamics and communication

Datasets used in the SOAPy article and tutorial could be referred in data sources.xlsx. You could download the source data by accessing the download links provided in the file.

Figure 4 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967

Figure 4 Number of HBs and RMSD between N1-H274Y-oseltamivir protein (PDB ID: 3CL0) with three flavones 428, 581, and 864 (Fig. A–C, respectively) during dynamic simulation time 500 ns.

Figure 5 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967

Figure 5 The proposed semi-synthesis of compound 864. (i) Column chromatography (70% EtOH: 18.64 mg.g−1) (ii) ClCH2COCl, K2CO3, DCM, rt, 8–24h; (iii) K2CO3/KI, acetone, reflux, 25–30 h.

Figure 2 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967

Figure 2 2D interaction diagrams and 3D molecular docking of N1-H274Y-oseltamivir protein (PDB ID: 3CL0) with four flavones 428, 581, 864, and 948 (A–D, respectively) and two flavonols 162 and 218, (E, F, respectively).

Figure 3 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967

Figure 3 3D molecular docking of bifunctional N inhibitor (compound 864) binding into the sialic acid binding cavity and the 430-cavity through the unique arginine triad Arg118-Arg292-Arg371 of N1-H274Y-oseltamivir protein (PDB ID: 3CL0).

Figure 1 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967

Figure 1 Procedure of molecular docking screening and molecular dynamics of flavones and flavonols from COCONUT database.

opencc-zeroJan 2024View details →

Supplementary material 1 from: Ha T-K-Q, Pham-Khanh N-H, Nguyen T-K (2024) Molecular docking screening, dynamics simulations, ADMET, and semi-synthesis prediction of flavones and flavonols from the COCONUT database as potent bifunctional neuraminidase inhibitors. Pharmacia 71: 1-10. https://doi.org/10.3897/pharmacia.71.e114967

Supporting data

Spastin locally amplifies microtubule dynamics to pattern the axon for presynaptic cargo delivery.

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Figure 7 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 7 Results of implementing different scouting and foraging strategies on the performance of model colonies in pollen collection. Three different foraging strategies (i.e. distance, quality or random) were tested for each scouting strategy (i.e. distance, quantity and random). The total amount of collected pollen, the mean number of daily foraging flights, the number of foraging flights and their success were evaluated for all combinations of scouting and foraging strategies.

Figure 5 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 5 Scout, recruit and foragers behaviour rules. Without private and social information, model bees become scout bees. When there is no private information because they never performed a foraging flight or because the flight was unsuccessful, model bees become recruits and will search for social information. If model bees have private information, they are considered forager bees even if no social information is available in the colony. In the presence of social information, scout and forager bees can change foraging locations (50% chance).

Figure 4 from: Capela N, Duan X, Ziółkowska EM, Topping CJ (2024) Modelling foraging strategies of honey bees as agents in a dynamic landscape representation. Food and Ecological Systems Modelling Journal 5: e99103. https://doi.org/10.3897/fmj.5.99103

Figure 4 Available foraging hours and weather variables (temperature and solar radiation) for each simulation day throughout the year. Rain and wind variables are not shown, but were used to calculate the number of available foraging hours.