Flora Of Bangladesh Pdf

[Octaviano Collars]

Bangladesh is a rich land of biodiversity. About 6000 species of plants are gathered in such a small area of 147570 sq km. I am trying to introduce the flora of Bangladesh in a pack from the naturalist view, not from the eye of plant-expert. For this, there will be some unwanted mistakes.Needless to say, pics used in this site are all original and snapped by me. The information are gathered here from the personal notes, collected books and from different websites.

Addition to the angiosperm flora provides essential insights into the biodiversity of a region, contributing to ecological understanding and conservation planning. Gafargaon subdistrict under Mymensingh district in Bangladesh represents a diverse population of angiosperms with a multifaceted ecosystem that demands re-evaluation of the existing angiosperm diversity of Gafargaon to update the status of angiosperm taxa and facilitate their conservation efforts. With this endeavor, a total of 100 angiosperm taxa belonging to 90 genera and 46 families were uncovered as additional occurrence in Gafargaon. The species in the area showcased a variety of life forms, including 63 herbs, 14 shrubs, 14 trees, and 9 climbers. Among the recorded taxa, Chamaecostus cuspidatus (Nees & Mart.) C.D. Specht & D.W. Stev. was selected for antidiabetic drug design endeavor based on citation frequency and ethnomedicinal evidence. A total of 41 phytochemicals of C. cuspidatus were screened virtually, targeting the Dipeptidyl peptidase 4 protein through structure-based drug design approach, which unveiled two lead compounds, such as Tigogenin (-9.0 kcal/mol) and Diosgenin (-8.5 kcal/mol). The lead candidates demonstrated favorable pharmacokinetic and pharmacodynamic properties with no major side effects. Molecular dynamics simulation revealed notable stability and structural compactness of the lead compounds. Principal component analysis and Gibbs free energy landscape further supported the results of molecular dynamics simulation. Molecular mechanics-based MM/GBSA approach unraveled higher free binding energies of Diosgenin (-47.36 kcal/mol) and Tigogenin (-46.70 kcal/mol) over Alogliptin (-46.32 kcal/mol). The outcome of the present investigation would enrich angiosperm flora of Gafargaon and shed light on the role of C. cuspidatus to develop novel antidiabetic therapeutics to combat diabetes.

Copyright: 2024 Ahmed, Rahman. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Floristics is instrumental in uncovering medicinal species across diverse plant groups by examining plant diversity and grasping the interconnections among different plant species. Precise species identification becomes imperative before revealing potential drug candidates sourced from plants, and this essential identification process is facilitated by comprehensive taxonomic studies [7]. The wealth of plant biodiversity cataloged through floristics, therefore, may serve as a foundation for sourcing potential drug candidates, bridging the gap between traditional botanical knowledge and cutting-edge pharmaceutical research. This interdisciplinary approach harnesses the traditional knowledge encoded in floristics to inform and direct the Structure Based Drug Design (SBDD) strategies, fostering a more comprehensive and sustainable exploration of medicinal resources within natural plant ecosystems [8].

The present investigation sought to revisit the angiosperm diversity within Gafargaon subdistrict, with a specific focus on identifying previously undiscovered taxa, particularly those with medicinal significance. Additionally, the objective was to put forth potential antidiabetic drug candidates derived from the selected medicinal species. To achieve this, a Structure Based Drug Design (SBDD) approach was employed, targeting the DPP4 protein. This dual-purpose investigation aimed not only to contribute to the current understanding of the angiosperm diversity in Gafargaon region but also to explore novel avenues for developing antidiabetic medications based on the identified medicinal plant.

Based on field observation, ethnomedicinal importance, consent of local people and novelty, Chamaecostus cuspidatus (Nees & Mart.) C.D. Specht & D.W. Stev. was selected for designing antidiabetic drug candidates with its bioactive phytocompounds. A comprehensive structure-based drug design (SBDD) protocol was employed in silico to explore antidiabetic potential of this medicinal herb.

The target protein was subjected to active site prediction to conduct a site-specific docking. PrankWeb server was utilized for determining active site in default settings [40]. The prediction was made by focusing on points positioned on the solvent-accessible surface of proteins. The PDB file of the target protein was uploaded as a custom structure in the PrankWeb server, with the conservation box checked. After generating output, pocket rank, pocket score, confidence level and conservation scores of various output options were analyzed and compared to determine the optimal selection.

Prior to conducting site-specific docking, a grid box was generated using AutoDockTools v.1.5.6 incorporating the binding site residues predicted by the PrankWeb server. The size coordinates in the grid box were 54 56 62, and the center coordinates were 45.589 -17.354 -31.654. The grid box covered the active sites of the DPP4 protein. Subsequently, molecular docking analysis was performed with AutoDock Vina [41]. The resulting docked complexes were analyzed for molecular interactions via PyMol and BIOVIA Discovery Studio Visualizer v21.1.0.20298 [42].

ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics play a crucial role in influencing the pharmacological effects and overall effectiveness of drug candidates, making them vital factors in the selection of lead compounds. SwissADME server was utilized to assess ADME properties of the selected phytochemicals after molecular docking analysis [43]. Subsequently, ProToxII and STopTox servers were utilized for toxicity evaluation [44, 45].

For MDS in GROMACS v.2020.6, ligand topology files were generated using the CgenFF server. All the systems were solvated with the TIP3P (Transferable Intermolecular Potential with 3 Points) water model, employing a triclinic box positioned 1 nm away from the protein surface [46]. Neutralization was accomplished by adding sufficient sodium and chloride ions (0.15 M salt). Energy minimization was carried out using the CHARMM36m force field with 5000 steps. In the process of system equilibration and molecular dynamics (MD) simulations, the NVT/NPT ensemble was applied, keeping the pressure and temperature at 1 bar and 300K, respectively. Particle Mesh Ewald (PME) was utilized to calculate long-range interactions. Subsequently, a 100 ns MD production run was executed, targeting approximately 1000 frames per simulation. The time integration step was set to 2fs, and the snapshot interval was configured at 100 ps [42]. For trajectory analyses, GROMACS utilities were employed, such as gmx rms, gmx rmsf, gmx gyrate and gmx sasa, which produced trajectory results formatted in CSV (Comma-Separated Values). Subsequently, all the CSV data were visualized using Microsoft Excel v.2013 to assess various parameters including Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (Rg), and total Solvent Accessible Surface Area (SASA). These analyses were conducted to evaluate the dynamic stability between the DPP4 receptor and potential lead candidates derived from C. cuspidatus.

Principal Component Analysis (PCA) is a widely employed analytical method for depicting the slow and functional motions of biomolecules [47]. To obtain the principal components of the protein-ligand complexes, the eigenvalue and eigenvectors of the covariance matrix were calculated and diagonalized. The eigenvectors indicate the direction of motion, while the eigenvalues illustrate both the direction and magnitude of motion. The covariance matrix for PCA was computed for backbone C alpha atoms using the GROMACS analysis tool, gmx covar, which both constructs and diagonalizes the covariance matrix. Additionally, another GROMACS pre-built tool, gmx anaeig, was utilized to assess the overlap between principal components and trajectory coordinates.

The free energy landscape (FEL) serves as a representation of potential conformations assumed by a protein during a molecular dynamics simulation, incorporating Gibbs free energy. The FEL elucidates two variables that capture specific system properties and assess conformational variability. It was generated using the probability distribution derived from the essential plane formed by the first two eigenvectors. The construction of the FEL was carried out using the gmx sham tool. Afterwards, two Python scripts were employed to visualize the results and produce 2D and 3D images. The Python scripts can be found in the S1 File.

The Prime package of the Schrdinger v.2020-3 software was utilized for MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) calculations [48]. The OPLS2005 force field and VSGB continuum solvation model were selected to estimate the free binding energies [49] utilizing the formula:(1)

Where, ΔG(solv) denotes variation in GBSA solvation energy between protein-inhibitor complex and the total solvation energies of the unbound inhibitor and protein; ΔE(MM) signifies the discrepancy in minimized energies between the protein-inhibitor complex and total energies of unbound inhibitor and protein; ΔG(SA) represents variation in surface area energies of the protein-inhibitor complex and the aggregated surface area energies of the individual components.

Drug target class was predicted using SwissTargetPrediction server [50]. Canonical SMILES was uploaded in the server checking Homo sapiens datasets. Structurally similar analogs were predicted using SwissSimilarity server [51]. In target class prediction, the Homo sapiens dataset is prioritized due to its exclusive focus on human proteins. This selection is crucial, especially in pharmaceutical applications, as it ensures that the predicted drug targets are pertinent to human biology. By employing the Homo sapiens dataset, the predictions are customized to human-specific targets, thus enhancing the probability of identifying drug candidates with therapeutic significance in humans. This approach not only increases the likelihood of discovering effective and safe drugs for human use but also ensures that the predictions remain clinically relevant and aligned with human biology.

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