Bangladesh J. Plant Taxon. 31(2): 239-264, 2024 (December) DOI: https://doi.org/10.3329/bjpt.v31i2.78751 © 2024 Bangladesh Association of Plant Taxonomists INTEGRATING TAXONOMY AND DRUG DISCOVERY: LILIOPSIDA FLORA OF RAJBARI, BANGLADESH TARGETING AMORPHOPHALLUS PAEONIIFOLIUS FOR COLORECTAL CANCER THERAPY MIRUNA BANU, SHEIKH SUNZID AHMED, MOMTAZ BEGUM AND M. OLIUR RAHMAN* Department of Botany, University of Dhaka, Dhaka 1000, Bangladesh Keywords: Liliopsida; Amorphophallus paeoniifolius; MMP-9; Molecular docking; Dynamics simulation; MM/GBSA; Bioinformatics. Abstract The present study explores the angiosperm flora belonging to the class Liliopsida in Rajbari district, seamlessly integrating taxonomy with phytocompound-based drug discovery through advanced computational biology approaches. The study covered all five upazilas (sub-districts) of the district. A total of 201 taxa across 118 genera and 24 families of Liliopsida were identified. The flora is predominantly composed of herbs (79.06%), followed by climbers (7.96%), trees (7.46%), shrubs (2.98%), and a minimal occurrence of epiphytes (1.99%). Poaceae emerged as the largest family, comprising 58 taxa across 36 genera, followed by Araceae (26 taxa) and Cyperaceae (17 taxa). Notably, the study identified 25 medicinal plant species under Liliopsida. Some rare species within Liliopsida, such as Coix aquatica, Wolffia arrhiza, Typha domingensis, and Schumannianthus benthamianus were also recorded in the study area. Among the medicinal plants identified, Amorphophallus paeoniifolius (Dennst.) Nicolson was selected for further investigation into colorectal cancer drug discovery. The computational therapeutics design endeavor unveiled two lead compounds: Riboflavin (- 7.9 kcal/mol) and Lupeol (-6.1 kcal/mol), both of which demonstrated promising favorable drug-likeness properties. Molecular dynamics simulation spanning 100 ns revealed structural stability of the identified leads. PCA and Gibbs free energy landscape study further corroborated the drug-candidacy of the leads. DFT-based molecular reactivity study unveiled Lupeol as the most kinetically stable compound (6.915 eV). The findings highlight the significance of multi-disciplinary approach integrating classical taxonomy with bioinformatics and pave the way for future colorectal cancer therapeutics. Introduction The Convention on Biological Diversity (CBD) has underscored the pivotal role of taxonomic and vegetation studies in ensuring effective biodiversity conservation. Such studies provide fundamental data on species identification, distribution, and classification, which are crucial for crafting well-informed conservation strategies. The CBD highlights that a lack of comprehensive taxonomic knowledge, coupled with a shortage of trained taxonomists and inadequate infrastructure, creates a significant "taxonomic impediment" that hampers efforts to assess and safeguard global biodiversity. Addressing this impediment is vital for achieving the CBD’s objectives, as it facilitates precise documentation of species diversity, helps identification of conservation priorities, and allows for effective monitoring of ecosystem changes over time. The CBD thus advocates for enhanced investment in taxonomic research and capacity building to support sustainable biodiversity management and policy development (Heywood, 2004). Rajbari district is geographically positioned between 22°40` and 23°50` N latitudes and between 89°19` and 90°40` E longitudes, covering an area of 1,119 sq. km. The district enjoys a *Corresponding author: oliur.bot@du.ac.bd https://doi.org/10.3329/bjpt.v31i2.78751 mailto:oliur.bot@du.ac.bd 240 BANU et al. moderate tropical monsoon climate characterized by three distinct seasons: a hot summer, a rainy season, and a dry winter. The annual average temperature ranges from a minimum of 9.8°C to a maximum of 30.1°C. Relative humidity remains fairly consistent throughout year, fluctuating from 77 to 79%. The annual rainfall is approximately 3742 mm (BBS, 2022). Rajbari district comprises 5 upazilas, namely Rajbari Sadar, Pangsha, Baliakandi, Kalukhali and Goalanda with an area of 347.1, 313, 242.53, 157.14 and 149 sq. km, respectively. Rajbari district encompasses a variety of habitats, including wetlands, cultivated land, charland, fallow land, scrub jungles and homestead areas. As an agriculturally rich region, its plant genetic, species and ecosystem diversity significantly influence the local environment. However, the floristic compositions are declining due to increasing urbanization, industrialization, habitat fragmentation, road construction, agricultural expansion, mismanaged brickfields as well as other human activities. Given the ongoing trend of habitat degradation and fragmentation, many species could disappear from the region before they are even documented and studied. Building upon the foundational works of Hooker (1872–1897) and Prain (1903), numerous floristic endeavors have been conducted within the present political boundaries of Bangladesh, including different upazilas and protected areas (Rahman et al., 2012, 2013, 2019a,b; Rahman and Alam, 2013; Sarker et al., 2013; Rahman and Hassan, 1995; Islam et al., 2009; Uddin and Hassan 2010, Arefin et al., 2011; Rahman et al., 2015; Haque et al., 2018). Despite these efforts, only a few district-level floras have been produced, such as those for Gazipur (Tabassum 2015), Patuakhali (Sultana, 2012), Bagerhat (Hossain et al., 2022), Satkhira (Hossain et al., 2021) and Narsingdhi (Khanam and Khan, 2020; Khanam et al., 2020). However, the floral diversity of Rajbari district has yet to be explored through detailed field inventories and specimen examination, leaving much of its flora unexplored. Plant taxonomy and floristics are essential for the precise detection of medicinal taxa, forming the foundation for exploring their therapeutic potential. By systematically classifying plants and understanding their distribution, taxonomists can identify species traditionally used in medicine or those possessing bioactive compounds, thus providing a gateway to drug discovery. This taxonomic accuracy is critical in ensuring the correct selection of plants for phytochemical analysis, driving the development of novel drugs through natural compounds. Compared to synthetic drugs, natural products offer several advantages, such as greater structural diversity, better biocompatibility, lower toxicity, and improved efficacy in targeting biological systems. These compounds, refined by evolution over thousands of years, are inherently optimized for biological interactions, making them a valuable resource in modern drug discovery (Ahmed and Rahman, 2024; Ahmed et al., 2024). Structure-based drug design (SBDD) integrates this taxonomic knowledge by leveraging advanced computational techniques to accelerate the drug discovery process. SBDD focuses on analyzing the three-dimensional structure of target proteins and identifying compounds, such as phytochemicals, that can effectively bind to them. This approach greatly minimizes the trial-and- error nature of traditional drug development by allowing precise predictions of compound-protein interactions. Key techniques in SBDD include molecular docking, which predicts the binding affinity and orientation of drug candidates targeting key protein; ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, which assesses the pharmacokinetic and safety profiles of compounds; and molecular dynamics (MD) simulation, which evaluates the stability and flexibility of compound-receptor interactions over time. Additionally, DFT (Density functional theory)-based molecular reactivity analysis aids in understanding the electronic structure and kinetic stability of the lead compounds. Together, these methods streamline the drug discovery process, reducing time and costs, while enhancing the precision of selecting potential drug candidates from natural sources (Bajad et al., 2021; Ahmed et al., 2023a). INTEGRATING TAXONOMY AND DRUG DISCOVERY 241 MMP-9 (Matrix Metalloproteinase-9) is a crucial enzyme involved in the degradation of the extracellular matrix (ECM), and plays a significant role in cancer progression, invasion, and metastasis across various types of cancers, including colorectal cancer (CRC) (Bendardaf et al., 2010; Said et al., 2014). MMP-9 is frequently overexpressed in CRC and is associated with poor prognosis due to its involvement in tumor growth, angiogenesis, and the spread of cancer cells to other tissues. Inhibiting MMP-9 has been suggested to reduce tumor invasiveness and slow metastasis, making it a viable target for therapeutic agents aimed at improving CRC outcomes (Rashid and Bardaweel, 2023; Sarkar et al., 2024). Therefore, this MMP-9 protein serves as a promising target for structure-based drug design endeavors. The study aims to identify, document, and analyze the angiosperm flora, particularly Liliopsida taxa of Rajbari district, assessing their current distribution, and medicinal significance. Consequently, it is crucial to identify, and document the plant species, providing a comprehensive taxonomic treatment of the angiosperm flora of Rajbari district, and to implement conservation measures to safeguard the region’s plant resources for the benefit of future generations. In addition, the study further aims to bridge the gap between taxonomy and drug design endeavor by identifying potential colorectal cancer drug candidates targeting MMP-9 protein from a medicinal plant of Rajbari district. This multi-disciplinary endeavor, therefore aims not only to deepen the understanding of Liliopsida diversity in Rajbari district but also to investigate novel anticancer therapeutics derived from the selected medicinal plant. Materials and Methods Botanical expedition, plant sample collection and identification A total of 128 field expeditions were conducted between 2019 to 2023 to collect plant specimens from Rajbari district covering all five upazilas: Rajbari Sadar, Pangsha, Baliakandi, Kalukhali and Goalanda (Fig. 1). Fig. 1. Map of Rajbari district showing the area of investigation (Source: Banglapedia). 242 BANU et al. The collected plant samples were processed following standard herbarium procedures (Singh and Subramaniam, 2008) and underwent thorough examination and identification at the Dhaka University Salar Khan Herbarium (DUSH). Identifications were ensured by consulting standard literatures (Khan and Alam, 1977; Khan and Halim, 1985; Ara and Hassan, 2019; Siddiqui et al., 2007; Ahmed et al., 2008) and were cross-referenced with previously identified specimens housed at DUSH and DACB. For updated nomenclature, the authoritative database Plants of the World Online (POWO, 2024) was consulted. Local names were sourced from Huq (2019), and the families were arranged following Cronquist (1981). The voucher specimens for the identified taxa are deposited at DUSH. Drug Design endeavor Amorphophallus paeoniifolius (Dennst.) Nicolson was chosen for designing colorectal cancer drug candidates due to its novelty, ethnomedicinal significance, and consent of local population in the study area. The drug design endeavor was accomplished in the following steps: Preparation of receptor macromolecule The structure of the Matrix Metalloproteinase 9 (MMP-9) protein, identified by the PDB ID “1GKC,” was retrieved from the Protein Data Bank (Rowsell et al., 2002). Receptor preparation was carried out using AutoDockTools v.1.5.6 and SWISS-PDB Viewer v.4.10. Subsequently, OpenBabel v.3.1.1.1 was employed to convert the energy-minimized protein from PDB to PDBQT format for further analysis (Guex and Peitsch, 1997; O'Boyle et al., 2008; Rizvi et al., 2013). Preparation of ligands Phytochemicals from A. paeoniifolius were identified and retrieved in 3D SDF format from relevant literature and the IMPPAT database (Shrivastava et al., 2023; Vivek-Ananth et al., 2023). Doxycycline, a known inhibitor of the MMP-9 receptor, was selected as the control drug and obtained from the PubChem database (Kim et al., 2005). All ligands were then energy-minimized and converted to PDBQT format using OpenBabel v.3.1.1.1 for further analysis. Active site determination For site-specific molecular docking, the receptor's active site was determined via the CASTp v.3.0 (Tian et al., 2018). The protein, uploaded in PDB format, was analyzed, and the active site with the highest surface area and volume was selected as the optimal site for docking simulations. Molecular docking A grid box for molecular docking was defined using the output from CASTp v.3.0, with dimensions of 68 × 64 × 66 and center coordinates set to 61.125 × 29.614 × 113.283 along the X, Y, and Z axes, respectively. Molecular docking was conducted using EasyDock Vina v.2.237 (Minibaeva et al., 2023). The receptor-ligand complexes were visualized with Discovery Studio (Islam et al., 2023). Following docking, the selected phytocompounds were evaluated through ADMET analysis for further assessment. ADMET properties evaluation The ADMET evaluation was performed using SwissADME to evaluate the drug-likeness of the compounds (Daina et al., 2017). Toxicity parameters were then analyzed using the STopTox server (Borba et al., 2022). For both analyses, the compounds were provided in SMILES format. Molecular dynamics (MD) simulation To examine the thermodynamic behavior of the control drug and lead compounds, molecular dynamics (MD) simulations were performed on an Ubuntu 22.04 (Jammy Jellyfish) operating INTEGRATING TAXONOMY AND DRUG DISCOVERY 243 system using the Desmond module of the Schrödinger 2020-1 package, over a duration of 100 ns (Rahman et al., 2024). The simulated systems were solvated with the SPC water model in orthorhombic periodic boundary boxes. The OPLS4 force field was applied for energy optimization of the solvated framework, with the default settings in Desmond. Simulations were processed using the NPT ensemble, with Nose–Hoover temperature coupling and isotropic pressure scaling. The trajectories were sampled at 100 ps intervals, resulting in approximately 1000 frames for subsequent analysis, while energy data were recorded at 1.2 ps intervals. Principal component analysis and Gibbs FEL To analyze the essential dynamics of the top selected leads and the control drug, principal component analysis (PCA) was conducted using the Statistics Kingdom server (https://www.statskingdom.com/). RMSD and Rg coordinates for all simulated frames were input as two series to perform PCA using a covariance matrix. For Gibbs free energy landscape (FEL) analysis, a Python script was employed on Ubuntu Focal Fossa 20.04.6 LTS. The PCA data was saved in a CSV file for easy manipulation via the Pandas library. The script utilized essential libraries such as NumPy for numerical operations, facilitating the efficient computation of statistical metrics, and Matplotlib for data visualization. A 2D histogram of the PCA results was generated to estimate the probability distribution of data points, enabling the calculation of Gibbs free energy based on Boltzmann statistics (Ahmed and Rahman, 2024). Molecular reactivity analysis Quantum mechanics-based DFT calculation was performed to estimate molecular reactivity for the lead compounds and control drug employing Avogadro and ORCA v.4.1.1 software packages (Snyder and Kucukkal, 2021; Paul et al., 2023). Input files were prepared in Avogadro for subsequent processing in ORCA. Geometry optimization was performed, employing the B3LYP-D3 functional and the 6-31G (d, p) basis set to estimate the HOMO-LUMO (Highest Occupied Molecular Orbital-Lowest Unoccupied Molecular Orbital) energy gap. Results and Discussion Angiosperm flora: Annotation of Liliopsida The present study identified 201 taxa across 46 genera and 25 families within the class Liliopsida (monocotyledons) from Rajbari district (Table 1). Among the families, Poaceae emerged as the largest, comprising 58 taxa under 36 genera, followed by Araceae (26 species) and Cyperaceae (17 species). Figure 2 illustrates the ten largest families along with the number of genera and species. Agavaceae and Dioscoraceae each contribute 9 species, while the Liliaceae includes 8 species. The families Aponogetonaceae, Heliconiaceae, Lemnaceae, Orchidaceae, and Pontederiaceae each contain 3 species. Eight families, including Aloaceae, Cannaceae, Costaceae, Marantaceae, Musaceae, Smilacaceae, Strelitziaceae and Typhaceae are represented by a single species each. Among the genera, Cyperus stands out as the largest with 17 species, followed by Dioscorea with 10 species. The genera Colocasia, Commelina and Digitaria each contain 5 species, while Alocasia, Bambusa, Eragrostis, Fimbristylis and Paspalum are represented by 4 species each. Vegetation analysis shows that the majority of the species are herbs, representing 79.6% (140 species) of the total, followed by climbers (7.96%), trees (7.46%), shrubs (2.98%), and epiphytes (1.99%). Habitat analysis reveals that fallow lands (open fields) constitute 24.38% of the identified species, followed by homestead (22.89%), scrub jungles (16.91%), agricultural fields (14.93%), aquatic (11.44%), and road sides (9.45%). https://www.statskingdom.com/). 244 BANU et al. Table 1. List of Liliopsida taxa in Rajbari district with local name, habit, habitat, distribution and voucher numbers. Taxa Local name Habit Habitat Distribution Vouchers Alismataceae Sagittaria guayanensis subsp. lappula (D. Don) Bogin Muamia Her Aqu Rs,Ka,Ba,Go,Pa Miruna 1482 S. sagittifolia L. Muamia Her Aqu Rs,Ka,Ba,Go,Pa Miruna 242 Hydrocharitaceae Hydrilla verticillata (L.f.) Royle Kureli Her Aqu Rs,Ka,Ba,Go,Pa Miruna 2097 Nechamandra alternifolia (Roxb.) Thw. Sheola Her Aqu Rs,Ka,Ba,Go,Pa Miruna 1749 Ottelia alismoides (L.) Pers. Kuchkalai Her Aqu Rs,Ka,Ba,Go,Pa Miruna 1438 Vallisneria spiralis L. Pata seola Her Aqu Rs,Ka,Ba,Go,Pa Miruna 1779 Aponogetonaceae Aponogeton appendiculatus Bruggen Ghetu Her Aqu Rs,Ba,Ka,Go,Pa Miruna 185 Aponogeton crispus Thunb. Ghechu Her Aqu Rs,Ba,Ka,Go,Pa Miruna 231 Aponogeton natans (L.) Engl. & Krause Apanogeton Her Aqu Rs,Ba,Ka,Go,Pa Miruna 205 Arecaceae Areca catechu L. Supari Tre Hom Rs,Ka,Ba,Go,Pa Miruna 1595 Borassus flabellifer L. Tal Tre Roa Rs,Ka,Ba,Go,Pa Miruna 1673 Calamus viminalis Willd. Bet Cli Scr Rs,Ka,Ba,Go,Pa Miruna 61 Caryota mitis Lour. Bottle palm Tre Hom Rs,Ka,Ba,Go,Pa Miruna 1759 Caryota urens L. Sagu palm Tre Hom Rs,Ka,Ba,Go,Pa Miruna 1663 Chrysalidocarpus lutescens (Bory) H. Wen. Holud palm Tre Hom Rs,Ba,Ka,Go,Pa Miruna 1673 Cocos nucifera L. Narikel Tre Hom Rs,Ka,Ba,Go,Pa Miruna 320 Corypha taliera Roxb. Tali Tre Hom Rs Miruna 1449 Elaeis guineensis Jacq. Oil Palm Tre Hom Rs,Ka,Ba,Go,Pa Miruna 1565 Licuala spinosa Wurmb Unknown Shr Hom Rs,Ka,Ba,Go,Pa Miruna 1774 Phoenix sylvestris (L.) Roxb. Khejur Tre Roa Rs,Ka,Ba,Go,Pa Miruna 1593 Araceae Adelonema wallisii (Regel) S.Y.Wong & Croat Jongli kachu Her Scr Rs,Go,Bal,Pa,Ka Miruna 1015 Aglaonema costatum N.E. Brown Nemacos Her Hom Rs,Ka,Ba,Go,Pa Miruna 1658 Aglaonema robeleynii (Van Geert) Pitcher & Manda Nemacris Her Hom Rs,Ka,Ba,Go,Pa Miruna 1659 Alocasia cucullata (Lour.) G. Don Bish kachu Her Scru Rs,Ka,Ba,Go,Pa Miruna 584 Alocasia fornicata (Roxb.) Schott Salu kachu Her Hom Rs,Ka,Ba,Go,Pa Miruna 976 Alocasia macrorrhizos (L.) G. Don Man kachu Her Scr Rs,Ka,Ba,Go,Pa Miruna 975 Alocasia portei Schott Puti kachu Her Scr Rs,Ka,Ba,Go,Pa Miruna 977 Amorphophallus bulbifer (Schott) Blume Jongle ol Her Scr Rs,Ka,Ba,Go,Pa Miruna 978 Amorphophallus paeoniifolius (Dennt.) Nicol. Olkachu Her Agr Rs,Ka Miruna 586 Caladium bicolor (Ait.) Vent. Diranga kachu Her Hom Rs,Ka,Ba,Go,Pa Miruna 979 Caladium humboldtii (Raf.) Schott Befula kachu Her Hom Rs,Ka,Ba,Go,Pa Miruna 980 Colocasia esculenta (L.) Schott Kachu Her Agr Rs,Ka,Ba,Go,Pa Miruna 981 Colocasia fallax Schott Ranga kachu Her Hom Rs,Ka,Ba,Go,Pa Miruna 982 Colocasia mannii Hook. f. Mani kachu Her Scr Rs,Ka,Ba,Go,Pa Miruna 1719 Epipremnum aureum (Linden & Andr.) G.S. Bunting Pargacha Cli Roa Rs,Go,Bal,Pa,Ka Miruna 1723 Lasia spinosa (L.) Thw. Kanta kachu Her Scru Rs,Go,Bal,Pa,Ka Miruna 329 INTEGRATING TAXONOMY AND DRUG DISCOVERY 245 Table 1 contd. Taxa Local name Habit Habitat Distribution Vouchers Monstera obliqua Miq. Thaka Epi Hom Rs,Ka,Ba,Go,Pa Miruna 1359 Pistia stratiotes L. Topa pana Her Aqu Ba,Go,Ka, Rs Miruna 243 Raphidophora aurea (Linden & Andr.) Birdsey Charulata Cli Roa Ba,Go,Ka, Rs Miruna 983 Scindapsus officinalis (Roxb.) Schott Gaj pipal Cli Scr Ka, Rs Miruna 378 Scindapsus scortechinii Hook. f. Kain kanthal Cli Scr Ba,Rs Miruna 994 Syngonium podophyllum Schott Podolota kachu Cli Scr Ba,Go,Ka,Rs,Pa Miruna 984 Typhonium flagelliforme (Lodd.) Blume Ghechu Her Scr Ba,Go,Ka,Rs,Pa Miruna 767 Typhonium roxburghii Schott Roxy kachu Her Scr Ba,Ka,Go,Rs,Pa Miruna 774 Typhonium trilobatum (L.) Schott Ghet kachu Her Scr Rs,Ka,Ba,Go,Pa Miruna 1594 Xanthosoma sagittifolium (L.) Schott Dudh kachu Her Scr Ra,Ka,Ba,Go,Pa Miruna 1553 Lemnaceae Lemna minor L. Kuti pana Her Aqu Rs,Ka,Ba,Go,Pa Miruna 670 Spirodela polyrhiza (L.) Schleid. Tetule pana Her Aqu Rs,Ka,Ba,Go,Pa Miruna 244 Wolffia arrhiza (L.) Horkel ex Wimm. Sujipana Her Aqu Rs Miruna 16 Commelinaceae Commelina appendiculata C.B. Clarke Kulalatakansira Her Roa Ba,Go,Ka, Rs Miruna 1546 Commelina benghalensis L. Kanshira Her Roa Go,Ba,Ka,Rs Miruna 382 Commelina erecta L. Jata kansira Her Roa Go,Ba,Ka,Rs Miruna 1508 Commelina longifolia Lam. Pani kansira Her Aqu Ka,Go,Ba,Rs Miruna 120 Commelina paludosa Blume Kanchuria Her Roa Ba,Go,Ka, Rs Miruna 129 Cyanotis axillaris (L.) D. Don ex Sweet Baghanula Her Roa Ba,Go,Ka, Rs Miruna 220 Cyanotis cristata (L.) D. Don Unknown Her Roa Go,Ka,Ba,Rs Miruna 216 Floscopa scandens Lour. Hangsapdi gac Her Roa Rs,Go Miruna 1504 Murdannia nudiflora (L.) Brenan Kenduli Her Roa Rs,Ka Miruna 157 Tradescantia spathacea Sw. Deopindo Her Hom Rs,Ka,Ba,Go,Pa Miruna 1521 Cyperaceae Actinoscirpus grossus (L.f.) Goetgh. & D.A. Simpson Karui ghas Her Ope Ra,Ka,Ba,Go,Pa Miruna 1526 Bulbostylis barbata (Rottb.) C.B. Clarke Bulbobata Her Ope Rs,Ka,Ba,Go,Pa Miruna 1763 Cyperus articulatus L. Shoda Her Ope Rs,Ka,Ba,Go,Pa Miruna 1552 Cyperus fuscus L. Kanch Her Ope Rs,Ka,Ba,Go,Pa Miruna 1295 Cyperus cuspidatus Kunth Chapa ghas Her Ope Rs,Ka,Ba,Go,Pa Miruna 1577 Cyperus cyperoides (L.) Kuntze Boro gothubi Her Agr Rs,Ka,Ba,Go,Pa Miruna 1509 Cyperus difformis L. Behua Her Agr Rs,Ka,Ba,Go,Pa Miruna 83 Cyperus digitatus Roxb. Hath ghas Her Ope Rs,Ba Miruna 1296 Cyperus exaltatus Retz. Tata ghas Her Aqu Rs,Pa,Go Miruna 1510 Cyperus imbricatus Retz. Buethi Her Ope Rs,Pa,Go, Miruna 1520 Cyperus iria L. Barachucha Her Ope Ra,Ka,Ba,Go,Pa Miruna 1483 Cyperus michelianus (L.) Delile Choto gutubi Her Ope Rs,Ka,Ba,Go,Pa Miruna 1572 Cyperus mindorensis (Steud.) Huygh Gothubi Her Agr Rs,Ka,Ba,Go,Pa Miruna 1862 Cyperus procerus Rottb. Lamba mutha Her Ope Rs,Ka,Ba,Go,Pa Miruna 199 Cyperus pulcherrimus Willd. ex Kunth Shumo mutha Her Ope Rs,Ka,Ba,Go,Pa Miruna 1294 Cyperus pumilus L. Paikpami ghas Her Agr Rs,Ka,Ba,Go,Pa Miruna 1740 Cyperus rotundus L. Mutha He Ope Ra,Ka,Ba,Go,Pa Miruna 214 246 BANU et al. Table 1 contd. Taxa Local name Habit Habitat Distribution Vouchers Cyperus tenuiculmis Boeck. Khude potari Her Ope Rs,Ka,Ba,Go,Pa Miruna 1484 Cyperus thunbergii Vahl Mura ghas Her Ope Ra,Ka,Ba,Go,Pa Miruna 1548 Cyperus tuberosus Rottb. Dima mutha Her Ope Rs,Ka,Ba,Go,Pa Miruna 1291 Eleocharis acutangula (Roxb.) Schult. Chesra Her Ope Rs,Go Miruna 1837 Fimbristylis aestivalis (Retz.) Vahl Valis fibri Her Ope Rs,Ka,Ba,Go,Pa Miruna 148 Fimbristylis alboviridis C.B. Clarke Sadate fimbri Her Ope Rs,Ka,Ba,Go,Pa Miruna 373 Fimbristylis dichotoma (L.) Vahl subsp. dichotoma Bara nirbishi Her Agr Rs,Ka,Ba,Go,Pa Miruna 123 Fimbristylis miliacea (L.) Vahl Bura javani Her Ag Rs,Ka,Ba,Go,Pa Miruna 142 Fuirena ciliaris (L.) Roxb. Chhata ghas Her Agr Rs,Ka,Ba,Go,Pa Miruna 1800 Rhynchospora berteroi (Spreng.) C.B. Clarke Bindimuthi Her Ope Rs,Ka,Ba,Go,Pa Miruna 1826 Schoenoplectiella supina (L.) Lye Putputicechra Her Agr Ra,Ka,Ba,Go,Pa Miruna 190 Poaceae Alloteropsis cimicina (L.) Stapf Alotara cina Her Ope Rs,Go Miruna 1564 Avena fatua L. Jangli jai Her Aqu Rs,Pa Miruna 1396 Axonopus compressus (Sw.) P. Beauv. Mathghas Her Ope Go,Ka,Ba,Rs Miruna 1527 Bambusa balcooa Roxb. Baro aansh Tre Scr Ba,Ka,Go,Rs Miruna 1551 Bambusa bambos (L.) Voss Bon bans Tre Scr Ka,Ba,Go, Rs Miruna 1561 Bambusa salarkhanii M. K. Alam Katajali bans Tre Scr Rs Miruna 687 Bambusa vulgaris Scharad. ex Wendl. Jai bansh Tre Hom Ra,Ka,Ba,Go Miruna 1834 Bothriochloa bladhii (Retz.) S. T. Blake Gandagourana Her Hom Rs,Ka,Ba,Go,Pa Miruna 2011 Bothriochloa pertusa (L.) A. Camus Barmuda ghas Her Ope Ra,Ka,Ba,Go,Pa Miruna 2093 Cenchrus purpureus (Schumach.) Morrone Nepier ghas Her Ope Rs,Ka,Ba,Go Miruna 717 Chrysopogon aciculatus (Retz.) Trin. Chorkanta Her Ope Rs,Ka,Ba,Go Miruna 1761 Coix aquatica Roxb. Tosbi dana Her Agr Rs, Go Miruna 487 Cynodon dactylon (L.) Pers. Durba ghas Her Ope Go,Ba,Ka,Rs Miruna 605 Cyrtococcum accrescens (Trin.) Stapf Konaghas Her Ope Go,Ba,Ka,Rs Miruna 520 Cyrtococcum oxyphyllum (Hochst. ex Steud.) Stapf Pokra ghas Her Ope Go,Ba,Ka,Rs Miruna 521 Dactyloctenium aegyptium (L.) Willd. Mukra Her Ope Rs,Go,Ba,Pa,Ka Miruna 118 Desmostachya bipinnata (L.) Stapf Kusha Her Ope Rs,Ka,Ba,Go,Pa Miruna 1821 Digitaria ciliaris (Retz.) Koeler Kokjachira Her Ope Rs,Ka,Ba,Go,Pa Miruna 172 Digitaria ischaemum (Schreb.) Muhl. Kudeanguligas Her Ope Rs,Ka,Ba,Go,Pa Miruna 519 Digitaria sanguinalis (L.) Scop. Makunjali Her Roa Rs,Ka,Ba,Go,Pa Miruna 518 Digitaria setigera Roth Sheti ghas Her Roa Rs,Ka,Ba,Go,Pa Miruna 517 Digitaria ternata (A. Rich.) Stapf Nata ghas Her Agr Rs,Ka,Ba,Go,Pa Miruna 516 Dinebra chinensis (L.) Peterson & N.Snow Phulka ghas Her Agri Ba,Go,Ka,Rs,Pa Miruna 124 Echinochloa colonum (L.) Link Shama ghas Her Agr Ba,Go,Ka,Rs Miruna 177 Echinochloa crus-galli (L.) P. Beauv. Borosama ghas Her Agr Ba,Go,Ka,Rs Miruna 133 Eleusine indica (L.) Gaertn. Ghira durba Her Agr Ba,Go,Ka,Rs Miruna 122 Eragrostis japonica (Thunb.) Trin. Chira ghas Her Agri Ba,Go,Ka,Rs Miruna 1529 Eragrostis lehmanniana Nees Kona ghas Her Ope Ba,Go,Ka,Rs Miruna 1559 Eragrostis tenella (L.) P. Beauv. ex Roem. & Schult. Koni ghas Her Ope Ba,Go,Ka,Rs Miruna 208 Eragrostis unioloides (Retz.) Nees ex Sted. Chiraghas Her Ope Ba,Ka,Go,Rs Miruna 178 Hemarthria protensa Steud. Panseru Her Ope Rs,Go Miruna 200 INTEGRATING TAXONOMY AND DRUG DISCOVERY 247 Table 1 contd. Taxa Local name Habit Habitat Distribution Vouchers Hordeum vulgare L. Job Her Agr Go,Ba,Rs,Ka Miruna 1515 Imperata cylindrica var. latifolia (Hook. f.) C. E. Hubb. Chon Her Ope Go,Ba,Rs,Ka Miruna 1525 Imperata cylindrica var. major (Nees) C. E. Hubb. ex Hubb. & Vaughan Kash Her Ope Ra,Ka,Ba,Go Miruna 202 Leersia hexandra Sw. Arali Ghas Her Ope Ba,Go,Ka,Rs,Pa Miruna 1524 Louisiella paludosa (Roxb.) Landge Barti Herb Aqu Rs,Ka,Ba,Go,Pa Miruna 512 Melocanna baccifera (Roxb.) Kurz Muli Bansh Shr Hom Rs,Ba, Ka,Go,Pa Miruna 2026 Oplismenus burmanni (Retz.) P. Beauv. Jabri durba Her Roa Rs,Ka,Ba,Go,Pa Miruna 207 Oryza sativa L. Dhan Her Agr Rs,Ka,Go,BaPa Miruna 1068 Panicum brevifolium L. Panibrevi ghas Her Ope Rs,Ba,Go,Pa, Ka Miruna 513 Panicum miliaceum L. Cheena chaul Her Agr Rs,Pa Miruna 1581 Paspalum conjugatum Bergius Dadkuru Herb Ope Rs,Ka,Ba,Go,Pa Miruna 511 Paspalum distichum L. Nat ghas Herb Aqu Rs,Go,Pa Miruna 150 Paspalum scrobiculatum L. Goicha Herb Ope Rs, Bal,Ka Miruna 1774 Paspalum sumatrense Roth Lambafuli ghas Herb Ope Pa,Go, Miruna 1757 Pennisetum purpureum Schum. Hati ghas Herb Ope Ba,Rs,Go,Pa Miruna 1841 Saccharum officinarum L. Akh Shrub Agr Ba,Go,Rs,Ka Miruna 423 Saccharum spontaneum L. Kash Herb Ope Ba,Ka,Go, Rs Miruna 524 Setaria flavida (Retz.) Veldkamp Datkuri ghas Herb Roa Rs,Ka,Ba,Go,Pa Miruna 1775 Setaria pumila (Poir.) Roem. & Schult. Holde kaon Herb Ope Rs,Pa,Ka,Ba,Go Miruna 524 Sporobolus indicus R. Br. Ghas Herb Ope Rs,Pa,Ka,Ba,Go Miruna 337 Thyrsostachys oliveri Gamble Burma bans Tree Ope Rs,Pa,Ka,Ba,Go Miruna 1453 Triticum aestivum L. Gom Herb Agr Ba,Go,Ka,Pa,Rs Miruna 1693 Urochloa panicoides P. Beauv. Ghas Herb Ope Rs,Ka,Ba,Go,Pa Miruna 1713 Urochloa ramosa (L.) T.Q.Nguyen Jhopa ghas Her Agri Rs,Ka,Ba,Go,Pa Miruna 1903 Urochloa reptans (L.) Stapf Para ghas Her Ope Rs,Ka,Ba,Go,Pa Miruna 205 Urochloa setigera (Retz.) Stapf Baro goghonti Her Ope Rs,Go Miruna 1769 Zea mays L. Bhutta Herb Agr Rs,Ka,Ba,Go,Pa Miruna 1664 Typhaceae Typha elephantina Roxb. Hogla Herb Aqu Ka, Rs Miruna 1048 Strelitziaceae Ravenala madagascariensis Sonn. Panthopadop Tre Hom Go,Rs,Ka,Pa,Ba Miruna 1691 Heliconiaceae Heliconia humilis Jacq. Tiapakhi phul Her Hom Go,Pa,Rs,Ka,Ba Miruna 1446 Heliconia psittacorum L. f. Tia thuti Her Hom Go,Pa,Rs,Ka,Ba Miruna 1467 Heliconia rostrata Ruiz & Pavon Chingri nomi Her Hom Rs,Go Ka,Ba,Pa Miruna 390 Musaceae Musa paradisiaca L. Kanch kola Her Hom Rs,Go Ka,Ba,Pa Miruna 389 Zingiberaceae Alpinia nigra (Gaertn.) Burtt Tara Her Scr Rs,Go,Ka Miruna 941 Curcuma amada Roxb. Amada Her Scr Rs Miruna 458 Curcuma longa L. Halud Her Agr Ba,Ka,Go,Pa,Rs Miruna 1597 Curcuma zedoaria (Christm.) Rosc. Shati Her Agr Rs,Ba,Ka Miruna 1687 Elettaria cardamomum (L.) Maton Elach Her Agr Rs Miruna 606 Hedychium coronarium Koen. Dolonchapa Her Hom Rs,Go,Ba,Pa,Ka Miruna 1690 248 BANU et al. Table 1 contd. Taxa Local name Habit Habitat Distribution Vouchers Kaempferia galanga L. Ekangi Her Hom Ba,Rs,Ka Miruna 456 Zingiber montanum (Koen.) Dietr. Bon ada Her Scr Ka,Rs,Ba Miruna 1691 Zingiber officinale Rosc. Ada Her Agr Go,Ba,Pa,Rs,Ka Miruna 457 Zingiber zerumbet (L.) Roscoe ex Sm. Shoti Her Scr Ka, Rs Miruna 638 Costaceae Hellenia speciosa (J. Koenig) S.R. Dutta Kura Her Hom Ba,Ka,Go,Pa,Rs Miruna 369 Cannaceae Canna indica L. Kolaboti Her Hom Ba,Pa,Rs,Ka,Go Miruna 439 Marantaceae Schumannianthus benthamianus (Kuntze) Veldkamp & Turner Shitolpati Her Aqu Rs,Go Miruna 388 Pontederiaceae Pontederia crassipes Mart. Kachuripana Her Aqu Go,Rs,Ba,Pa,Ka Miruna 685 Pontederia hastata L. Baranukha Her Aqu Pa,Rs,Ka,Go,Ba Miruna 822 Pontederia vaginalis Burm. f. Nukha Her Aqu Go,Rs,Ba,Pa,Ka Miruna 1124 Liliaceae Allium cepa L. Piaj Her Agr Rs,Ba,Go,Ka Miruna 425 Allium sativum L. Rasun Her Agr Rs,Go,Ka,Ba Miruna 583 Asparagus racemosus Willd. Shatomuli Cli Hom Rs,Go,Ba Miruna 589 Crinum asiaticum L. Shukhdorson Her Hom Pa,Rs,Ka,Go,Ba Miruna 1528 Pancratium verecundum Ait. Goroshun Her Scr Go,Rs,Ka Miruna 1248 Scadoxus multiflorus (Martyn) Raf. Ball phul Her Hom Pa,Rs,Ka,Go,Ba Miruna 1568 Zephyranthes minuta (Kunth) D. Dietr. Golapi ghasful Her Hom Go,Pa,Ka,Rs,Ba Miruna 1579 Zephyranthes tubispatha (L'Hér.) Herb. Holud ghasful Her Hom Go,Pa,Ka,Rs,Ba Miruna 1542 Aloeaceae Aloe vera (L.) Burm. f. Ghritakumari Her Hom Rs,Ka,Go,Ba Miruna 940 Agavaceae Agave americana L. Cantala Her Hom Rs,Ka,Go,Ba Miruna 1522 Agave amica (Medik.) Thiede & Govaerts Rajanigandha Her Hom Rs,Ka,Go,Ba Miruna 1447 Agave sisalana Perrine Sisal hemp Her Hom Rs,Ka,Go,Ba Miruna 1570 Agave vivipara L. Bombai agar Her Hom Rs,Ka,Ba,Go Miruna 1590 Cordyline fruticosa (L.) A. Chev. Agnishar Shr Hom Ba,Rs,Ka,Go Miruna 1617 Dracaena angustifolia (Medik.) Roxb. Chikna drakan Shr Hom Rs,Ka,Go,Ba Miruna 1501 Dracaena trifasciata (Prain) Mabb. Gora chaka Her Hom Rs,Ka,Go,Ba Miruna 1657 Furcraea foetida (L.) Haw. Gandho hemp Shr Hom Rs,Ka,Go,Ba Miruna 1672 Sansevieria roxburghiana Schult. & Schult.f. Gora chaka Her Hom Rs,Ka,Go,Ba Miruna 1448 Smilacaceae Smilax perfoliata Lour. Kumarilata Cli Scr Go,Ba,Rs,Ka Miruna 326 Dioscoreaceae Dioscorea aculeata L. Jointia alu Her Scr Rs,Ka,Go,Ba Miruna 558 Dioscorea alata L. Chupri alu Cli Scr Rs,Ka,Go,Ba Miruna 126 Dioscorea belophylla (Prain) Voigt ex Hai. Shora alu Cli Scr Rs,Ka,Go,Ba Miruna 1540 Dioscorea bulbifera var. bulbifera L. Gonj alu Cli Scr Rs,Ka,Go,Ba Miruna 449 Dioscorea bulbifera var. sativa Prain Jen alu Cli Roa Rs,Ka,Go,Ba Miruna 559 Dioscorea esculenta (Lour.) Burkill Maitta alu Cli Roa Rs,Ka,Go,Ba Miruna 451 INTEGRATING TAXONOMY AND DRUG DISCOVERY 249 Table 1 contd. Taxa Local name Habit Habitat Distribution Vouchers Dioscorea kamoonensis Kunth Erabera lata Cli Roa Rs,Ka,Go,Ba Miruna 125 Dioscorea oppositifolia L. Ludi korphul Cli Scr Rs,Ka,Go,Ba Miruna 560 Dioscorea pentaphylla L. Jhum alu Cli Scr Rs,Ka,Go,Ba Miruna 155 Orchidaceae Acampe praemorsa var. longepedunculata (Trimen) Govaerts Pargacha Epi Scr Go,Rs,Ba,Pa,Ka Miruna 1823 Rhynchostylis retusa (L.) Blume Rasna Epi Scr Go,Rs,Ba,Pa,Ka Miruna 1840 Vanda tessellata (Roxb.) Hook. ex G. Don Pargacha Epi Scr Go,Rs,Ba,Pa,Ka Miruna 1828 Habit: Her: Herb, Shr: Shrub, Tre: Tree, Cli: Climber, Epi: Epiphyte; Habitat: Aqu: Aquatic, Scr: Scrub jungles, Roa: Roadside, Hom: Homestead, Agr: Agricultural field, Ope: Open field; Distribution; Rs: Rajbari sadar, Ba: Baliakandi, Go: Goalondo, Ka: Kalukhali, Pa: Pangsha. Fig. 2. Ten dominant families of Liliopsida illustrating the number of genera and species in Rajbari. The study area supports a variety of aquatic habitats including ponds, beels, lowlands, and rivers, where many monocot species are found, and some of the common aquatic species are Aponogeton appendiculatus, Aponogeton natans, Eichhornia crassipes, Hydrilla verticillata, Ottelia alismoides, Pistia stratiotes, Sagittaria sagittifolia, Typha elephantina, Vallisneria spiralis, Wolffia arrhiza etc. A total of 25 medicinal plants used by traditional healers in the study area for treatment of different diseases, and notable species are Aloe vera, Amorphophallus paeoniifolius, Colocasia esculenta, Hellenia speciosa, Curcuma amada, Cyperus rotundus, Dioscorea alata, Kaempferia galanga, Lasia spinosa, Pontederia hastata, Vanda tessellata and Zingiber zerumbet. Some medicinally important and rare species are shown in Figure 3. Field observations have identified several rare species, such as Coix aquatica, Schumannianthus benthamianus, Bulbostylis barbata and Bambusa salarkhanii, which warrants further attention for conservation efforts. 250 BANU et al. While numerous studies have focused on the angiosperm flora of several upazilas in Bangladesh (Islam et al., 2009; Rahman et al., 2019a,b; Sarker et al., 2013; Sajib et al., 2014; Mahmudah et al., 2017), little effort has been made to produce comprehensive district-level flora. Khanam and Khan (2020) documented 168 species of Liliopsida (monocotyledons) from Narsinghdi district, whereas Hossain et al. (2021) identified 144 taxa from Liliopsida in the coastal district Satkhira, and Islam et al. (2022) reported a mere 133 taxa from Borguna district. In contrast, higher numbers of monocotyledonous taxa were recorded in Chapai Nawabganj and Rangpur districts, with 224 and 211 taxa, respectively (Islam and Khan, 2024; Khan et al., 2021). Fig. 3. Some medicinal and rare plants of Rajbari district. A. Amorphophallus paeoniifolius, B. Bambusa salarkhanii, C. Corypha taliera, D. Curcuma amada, E. Cyanotis cristata, F. Cyperus michelianus, G. Cyrtococcum accrescens, H. Dactyloctenium aegyptium, I. Heliconia rostrata, J. Hellenia speciosa, K. Kaempferia galanga, L. Nechamandra alternifolia, M. Pontederia hastata, N. Schumannianthus benthamianus, O. Syngonium podophyllum, P. Zingiber zerumbet. INTEGRATING TAXONOMY AND DRUG DISCOVERY 251 Compared to the earlier reports, our study, with 201 monocotyledonous taxa from Rajbari, surpasses the figures reported for Narsinghdi, Borguna, Satkhira, and Patuakhali (Sultana, 2012; Khanam and Khan, 2020; Hossain et al., 2021; Islam et al., 2022), yet falls slightly short compared to the monocot floras of Rangpur and Chapai Nawabganj flora (Khan et al., 2021; Islam and Khan, 2024). Molecular docking analysis A total of 27 unique active site residues were identified in the MMP-9 receptor (Fig. 4). The surface area (SA) was calculated as 205.130 Ų, with a volume of 102.572 ų, making the active site as a significant binding region for molecular docking analysis. Performing site-specific docking with active site residues is crucial in accurately predicting the binding interactions between ligands and their target proteins. Unlike blind docking, which assesses potential binding across the entire protein surface, site-specific docking focuses on predefined active sites, enhancing the precision of ligand placement. This targeted approach allows for a more refined understanding of ligand-receptor interactions, increasing the likelihood of identifying effective drug candidates (Ahmed and Rahman, 2024). Fig. 4. Determination of the best ranked binding site in MMP-9 receptor. Rank 1 cavity was determined as the final binding site for its highest surface area and volumetric features. A. Rank 1 cavity, B. Rank 2 cavity. 252 BANU et al. Molecular docking of 22 phytocompounds of A. paeoniifolius revealed binding affinity ranged from -4.1 to -8.1 kcal/mol (Table 2). Alpha-carotene showed the highest affinity (-8.1 kcal/mol), while Oxalic acid demonstrated the lowest affinity (-4.1 kcal/mol). Doxycycline, as a control, scored -6.0 kcal/mol and comparing with it, a total of nine phytocompounds scored better than the control. These nine compounds were put forward for second-step screening via ADMET assay that revealed two lead compounds such as Riboflavin and Lupeol. The docked complexes of the leads and control drug are visualized in the Figure 5. Table 2. Binding affinities of A. paeoniifolius phytocompounds against the receptor MMP-9. No. Ligands IMPAAT ID/ PubChem CID Chemical formula Molecular weight (g/mol) Binding affinity (kcal/mol) 1 Alpha-carotene IMPHY011609 C40H56 536.9 -8.1 2 Riboflavin IMPHY000846 C17H20N4O6 376.4 -7.9 3 Stigmasterol IMPHY014842 C29H48O 412.7 -7.6 4 Quercetin IMPHY004619 C15H10O7 302.2 -7.2 5 Beta-sitosterol IMPHY014836 C29H50O 414.7 -6.9 6 Retinol IMPHY001308 C20H30O 286.5 -6.3 7 Amylotetraose IMPHY008888 C24H42O21 666.6 -6.3 8 Betulinic acid IMPHY012003 C30H48O3 456.7 -6.1 9 Lupeol IMPHY012473 C30H50O 426.7 -6.1 10 1-ethoxy-4-[(Z)-2-nitroprop-1- enyl] benzene 5373673 C11H13NO3 207.2 -5.9 11 Palmitic acid IMPHY007327 C16H32O2 256.4 -5.9 12 D-xylose IMPHY015116 C5H10O5 150.1 -5.9 13 4,6-Di-tert-butylresorcinol 79337 C14H22O2 222.3 -5.7 14 D-galactose IMPHY012050 C6H12O6 180.1 -5.7 15 Nicotinic acid IMPHY007357 C6H5NO2 123.1 -5.6 16 L-rhamnose IMPHY015056 C6H12O5 164.1 -5.6 17 Thiamine IMPHY000005 C12H17N4OS+ 265.3 -5.5 18 Phytic acid IMPHY007365 C6H18O24P6 660.0 -5.5 19 Beta-sitosterol palmitate IMPHY003933 C45H80O2 653.1 -5.4 20 Triacontane IMPHY009413 C30H62 422.8 -5.0 21 Calcium oxalate IMPHY003530 C2CaO4 128.1 -4.2 22 Oxalic acid IMPHY007450 C2H2O4 90.0 -4.1 23 Doxycycline (control) 54671203 C22H24N2O8 444.4 -6.0 Molecular interaction analysis The molecular interaction study revealed similar interaction patterns between the lead compounds and Doxycycline. Among the two leads and control, conventional hydrogen bonds (CHBs) were observed only in Riboflavin, supporting its superiority as potential anticancer drug candidate (Table 3). Riboflavin interacted with residues Gly186, Leu187, Leu188, His401, Glu402, His405, His411 and Met422 (Fig. 6A), forming CHBs with Gly186 and Met422 residues, while other residues were involved in hydrophobic interactions. Lupeol showed interactions with Leu187, Leu188, His401, His411, and Pro421 residues (Fig. 6B) where all residues formed hydrophobic interactions. Doxycycline interacted with Phe110, Leu187, His190, and His411 residues with hydrophobic bonding only (Fig. 6C). Hydrogen bonding and hydrophobic interactions are very important for drug binding and efficacy. Hydrogen bonds stabilize ligand- receptor complexes, enhancing specificity and orientation, which improves binding affinity. INTEGRATING TAXONOMY AND DRUG DISCOVERY 253 Fig. 5. Two lead compounds and control drug showing docked complexes after molecular docking analysis. A. Riboflavin, B. Lupeol, C. Doxycycline (control). 254 BANU et al. These interactions often dictate the orientation of the ligand within the binding cavity, facilitating effective biological activity. On the contrary, hydrophobic interactions promote the exclusion of water molecules from the binding site, further increasing the stability of the ligand- receptor complex. These interactions occur between nonpolar residues and contribute significantly to the overall binding energy (Ahmed et al., 2023b). Fig. 6. Two-dimensional molecular interaction analysis of the two leads and control drug targeting MMP-9 protein. A. Riboflavin, B. Lupeol, C. Doxycycline. Table 3. Evaluation of molecular interaction between the leads and the control drug targeting MMP-9 protein. Ligands Binding sites Hydrogen- bonding residues (Distance in Å) Hydrogen bonds number Hydrophobic- interaction Binding affinity (kcal/mol) Riboflavin Gly186, Leu187, Leu188, His401, Glu402, His405, His411, Met422 Gly186(2.54), Met422(2.59) 2 Leu187, Leu188, His401, Glu402, His405, His411 -7.9 Lupeol Leu187, Leu188, His401, His411, Pro421 No residues 0 Leu187, Leu188, His401, His411, Pro421 -6.1 Doxycycline (control) Phe110, Leu187, His190, His411 No residues 0 Phe110, Leu187, His190, His411 -6.0 INTEGRATING TAXONOMY AND DRUG DISCOVERY 255 ADMET evaluation ADMET study revealed drug-likeness of Riboflavin and Lupeol in comparison with Doxycycline (Table 4, Fig. 7). Among the lead compounds, Lupeol exhibited the highest molecular weight (426.7 g/mol). The H-bond accepting and donating profiles of Riboflavin was closely comparable to those of Doxycycline, while Lupeol demonstrated only one H-bond donor and acceptor. Lupeol had the highest molar refractivity score, while Riboflavin had the lowest. TPSA was lowest for Lupeol, while it was highest for Doxycycline. The gastrointestinal absorption capacity of the two lead compounds were very similar to that of the control drug. The CYP isoform inhibition profiles of both leads and Doxycycline were alike, with none showing inhibition against various CYP isoforms (Table 4). In terms of solubility, Riboflavin was highly soluble, Doxycycline was soluble and Lupeol exhibited poor solubility. Riboflavin adhered to Lipinski’s rule of five with zero violation, while Lupeol and Doxycycline demonstrated one violation each which is acceptable. In toxicity analysis, Riboflavin and Lupeol revealed satisfactory results with no major undesirable complications, similar to the control drug Doxycycline. The ADMET results of the present investigation were consistent with previous SBDD studies (Rahman et al., 2024; Ahmed et al., 2023a,b; Ahmed et al., 2024). Fig. 7. Drug-likeness and oral bioavailability evaluation of the leads and Doxycycline. LIPO indicates lipophilicity, INSOLU depicts insolubility, INSATU suggests insaturation index, FLEX points flexibility, SIZE implies molecular weight, and POLAR denotes polarity. Pink region reflects the best zone while red line denotes best fit. A. Riboflavin, B. Lupeol, C. Doxycycline. 256 BANU et al. Molecular dynamics simulation The MD simulation analysis unveiled structural stability and compactness of Riboflavin and Lupeol (Table 5). Both the leads showed similar mean values in RMSD (root mean square deviation), RMSF (root mean square fluctuation), Rg (radius of gyration), and SASA (solvent accessible surface area). The RMSD analysis showcased the stability of Riboflavin and Lupeol after 30 ns and continued to stable until 100 ns (Fig. 8A). Riboflavin and Lupeol closely followed each other than Doxycycline. The control drug exhibited a minor fluctuation between 12 to 18 ns, stabilized until 85 ns, and then showed a slight upward movement, becoming stable again with a downward movement near 100 ns. The RMSF analysis showed fluctuations in a narrow range (Fig. 8B). The mean RMSF varied from 1.05 ± 0.78 to 1.40 ± 1.07 Å, where Doxycycline scored the lowest and Riboflavin scored the highest. Although the RMSF graph begins with residue index 1, this corresponds to the actual sequence of the protein. Specifically, the first residue in the graph (index 1) corresponds to Phe110 in the protein sequence, the second residue (index 2) corresponds to Val111, and so on. This consistent pattern ensures that the fluctuations observed in the RMSF graph can be directly mapped to the biologically relevant residue positions, despite the indexing convention used by the simulation software. Table 4. ADMET properties evaluation of the lead candidates and Doxycycline. Parameters Molecule Riboflavin Lupeol Doxycycline Physicochemical properties Formula C17H20N4O6 C30H50O C22H24N2O8 Molecular weight (g/mol) 376.4 426.7 444.4 H-bond acceptors 8 1 9 H-bond donors 5 1 6 Molar refractivity 96.99 135.14 110.91 TPSA 161.56 Å2 20.23 Å2 181.62 Å2 Lipophilicity iLOGP 1.63 4.72 1.82 XLOGP3 -1.46 9.87 0.54 WLOGP -1.68 8.02 -0.50 MLOGP -0.54 6.92 -2.08 Silicos-IT Log P 1.09 6.82 -0.98 Consensus Log P -0.19 7.27 -0.24 Pharmacokinetics GI absorption Low Low Low CYP1A2 No No No CYP2C19 No No No CYP2C9 No No No Log Kp -9.63 cm/s -1.90 cm/s -8.63 cm/s Water solubility (ESOL) Log S -1.31 -8.64 -2.94 Solubility (mg/ml) 1.85E+01 9.83E-07 5.07E-01 Solubility (mol/l) 4.93E-02 2.30E-08 1.14E-03 Class Very soluble Poorly soluble Soluble Drug likeness Lipinski (violations) 0 1 1 Bioavailability score 0.55 0.55 0.11 Medicinal chemistry PAINS (alerts) 0 0 0 Synthetic accessibility 3.84 5.49 5.25 Toxicity Acute inhalation toxicity No No No Acute oral toxicity No Yes No Acute dermal toxicity No No No Eye irritation and corrosion Yes No Yes Skin sensitization No No No Skin irritation and corrosion No Yes No INTEGRATING TAXONOMY AND DRUG DISCOVERY 257 Table 5. Molecular dynamics simulation trajectory analysis of the leads and Doxycycline. Tested systems PL RMSD (Å) RMSF (Å) Rg (Å) SASA (Å2) Riboflavin 2.92 ± 0.41 1.40 ± 1.07 3.96 ± 0.09 183.01 ± 39.93 Lupeol 3.04 ± 0.55 1.23 ± 1.03 4.26 ± 0.03 235.24 ± 38.18 Doxycycline (control) 2.11 ± 0.43 1.05 ± 0.78 3.85 ± 0.04 260.95 ± 30.59 The radius of gyration (Rg) study further corroborated the drug candidacy of the two lead compounds, as both exhibited stability without any drastic fluctuations (Fig. 8C). Lupeol maintained a very steady trajectory, with fluctuations less than (0.2 Å). Riboflavin also maintained steady trajectory but at around 35 to 52 ns, it showed a minor downward movement, during which it intersected with Doxycycline. From 52 ns onwards, Riboflavin stabilized, maintaining a steady distance from both Doxycycline and Lupeol. Doxycycline demonstrated a few initial movements from 0 to 5 ns, but after 5 ns, it remained stable throughout the 100 ns. The SASA analysis bolstered the drug candidacy of the two leads as mean SASA score was lower for the two leads compared to Doxycycline (Table 5). The lowest mean SASA score was found in Riboflavin (183.01 ± 39.93) Å2, followed by Lupeol (235.24 ± 38.18) Å2, and Doxycycline (260.95 ± 30.59) Å2. The trajectory graph elucidated the compactness of the two leads with the progression of time (Fig. 8D). Riboflavin and Lupeol showed minor primary movements from 0 to 55 ns, after which they maintained a consistent distance with each other and demonstrated a steady downward trend until 100 ns. Doxycycline also displayed a downward stabilization trend from around 50 ns until 88 ns, after which it showed a slight upward movement from 88 to 96 ns, and became stabilized again near 100 ns. Fig. 8. Molecular dynamics simulation study showing dynamic stability of the tested systems. A. Trajectory based on protein-ligand RMSD, B. Trajectory based on RMSF, C. Trajectory based on Rg, D. Trajectory based on SASA. 258 BANU et al. The protein-ligand contact analysis revealed that Riboflavin formed the most extensive protein-ligand interactions, surpassing both Doxycycline and Lupeol (Fig. 9). Riboflavin exhibited the highest interaction fraction with Phe110, followed by His175, His190, and other residues (Fig. 9A), signifying its robust binding potential. Lupeol, which showed predominant hydrophobic interactions, formed its strongest contacts with Tyr393, followed by Asp185 and Leu188 (Fig. 9B), underscoring the role of nonpolar interactions in its binding affinity. Doxycycline demonstrated the highest interaction with Tyr420, followed by Asp185 and Leu187 (Fig. 9C), reflecting its distinct interaction pattern. These variations in binding profiles suggest differential stability and affinity of the compounds within the active site, emphasizing the importance of diverse interactions, especially hydrophobic and hydrogen bonding, in determining the efficacy of ligand binding. Fig. 9. Evaluation of protein-ligand contacts during molecular dynamics simulation. A. Riboflavin, B. Lupeol, C. Doxycycline. INTEGRATING TAXONOMY AND DRUG DISCOVERY 259 PCA and Gibbs FEL The PCA and Gibbs FEL analyses provided crucial insights into the essential dynamics and conformational stability of Riboflavin and Lupeol compared to Doxycycline (Fig. 10). Fig. 10. Evaluation of essential molecular dynamics based on principal components analysis and Gibbs free energy landscapes. A. Riboflavin, B. Lupeol, C. Doxycycline, D. Superimposition of the two leads and Doxycycline. 260 BANU et al. The PCA phase-space distribution indicated that Riboflavin exhibited the highest degree of compactness, followed by Lupeol and Doxycycline, suggesting that Riboflavin maintains the most stable conformation during simulation. This was further corroborated by the Gibbs FEL analysis, which underscored the stability of Riboflavin by displaying a more centralized and extensive low- energy region (denoted by blue space), reflecting its preference for energetically favorable conformations (Fig. 10). Lupeol showed moderate stability, with a relatively smaller low-energy region, while Doxycycline displayed the least stable dynamics, with more dispersed energy states. These findings suggest that both Riboflavin and Lupeol demonstrate superior conformational stability compared to Doxycycline, potentially enhancing their suitability as drug candidates. The PCA and Gibbs FEL analyses align with previously published structure-based drug design study on Chamaecostus cuspidatus targeting DPP4 (Ahmed and Rahman, 2024). Molecular reactivity evaluation Molecular reactivity analysis revealed the energy levels of the electrons in the HOMO and LUMO states (Fig. 11). The energy of the HOMO state was the highest for Riboflavin (-6.496 eV), followed by Lupeol (-6.344 eV), and Doxycycline (-5.748 eV). For the LUMO state, the Fig. 11. DFT-based molecular reactivity analysis of the lead compounds and control drug. A. Riboflavin, B. Lupeol, C. Doxycycline (control). INTEGRATING TAXONOMY AND DRUG DISCOVERY 261 highest energy was recorded for Riboflavin (-3.009 eV), followed by Doxycycline (-2.370 eV), and Lupeol (0.571 eV). The band energy gap (ΔE) was 3.487, 6.915, and 3.378 eV for Riboflavin, Lupeol, and Doxycycline, respectively (Fig. 11). The HOMO represents the orbital with the highest energy-containing electrons in a molecule. The electrons in the HOMO are generally the most reactive due to their high energy state and are thus the easiest to excite or donate to another molecule. The LUMO is the lowest energy orbital that does not contain electrons but can accept them. The LUMO is critical for understanding molecular interactions, as it is the orbital most likely to accept electrons (Paul et al., 2023). The energy difference between HOMO and LUMO plays a critical role in understanding the molecular reactivity and kinetic stability of the lead compounds (Ahmed et al., 2023a). Doxycycline revealed the highest molecular reactivity with its lowest ΔE score of 3.378 eV. Riboflavin demonstrated closely similar results to Doxycyline with band energy gap of 3.487 eV. Lupeol showed the highest band energy gap of 6.915 eV and became the least reactive and most kinetically stable compound. The molecular reactivity results of the present investigation were congruent to the DFT analysis of Amberboa ramosa phytocompounds (Paul et al., 2023). With advanced computational biology techniques, our current investigation integrates classical plant taxonomy with drug design endeavor. This study would enrich the floristics knowledge of Liliopsida in Rajbari district and promote the discovery of anticancer agents targeting colorectal cancer. Furthermore, the study will encourage future floristics research to integrate taxonomic insights with bioinformatics, facilitating successful drug discovery from natural compounds and paving the way for exploring alternative medicines. Acknowledgements The first author extends her gratitude to the University Grant Commission of Bangladesh for granting the PhD Fellowship that facilitated this research. The authors thank Bangladesh National Herbarium for granting access to their herbarium materials. 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