Computational Identification of Potential Inhibitors Targeting uPA and uPAR

Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.65 https://doi.org/10.5281/zenodo.17393771 Computational Identification of Potential Inhibitors Targeting uPA and uPAR Sneha Roy1#, Saurav Kumar Mishra1#, Zainab A. Laxmidhar2, John J. Georrge1,2* 1 Department of Bioinformatics, University of North Bengal, District-Darjeeling, West Bengal-734013, India 2 Department of Bioinformatics, Christ College, Rajkot, Gujarat, India #Consider as joint first author *Corresponding author: johnjgeorr[email protected] Abstract: The plasminogen activator (PA) system has been implicated in a variety of physiological and pathological circumstances, including tissue regeneration, angiogenesis, cancer growth, and metastasis. Moreover, in the PA system, uPA and uPAR play a significant role, which makes it an ideal target for investigation. This study employed advanced computational approaches, followed by protein and ligand preparation and the docking of potential inhibitors towards these targets, which can lead to therapeutic developments. The docking analysis revealed that the set of ligand library-selected inhibitors is comparable to the approved drug amiloride and has higher docking scores. Finally, a chimeric molecule was generated, inhibiting the binding of uPA and uPAR and the catalytic activity of uPA. This identified inhibitor can be used for therapeutics to restrict the target mechanism. Overall, this study identified promising insights toward both targets. However, biological confirmation is essential to confirm the effectiveness of selected inhibitors. Keywords: Anti-cancer drug targets, Chimeric molecule design, Computational drug discovery, Molecular docking, uPA-uPAR system 1. Introduction In the field of pharmacology, understanding drug targets is essential for the development and application of therapeutic agents. A drug target is defined as a protein, lipid, or carbohydrate that interacts with a chemical compound or drug. It is essential to recognize that multiple targets can be associated with a single drug. The classification of drugs encompasses various categories, including approved drugs, withdrawn drugs, and illicit drugs, each serving distinct roles within the medical landscape. Withdrawn drugs are particularly noteworthy as they refer to those removed from the market by their manufacturers in at least one country, often due to safety concerns or a lack of efficacy. Recent studies from DrugBank reveal that there are more than 225 withdrawn drug targets linked to these discontinued drugs, which can belong to any biological species and may include enzymes, receptors, transporters, channels, and carrier molecules (Galan-Vasquez & Perez-Rueda, 2021; Sarkar et al., 2024). Among these withdrawn drug targets, the urokinase-type plasminogen activator (uPA) and its receptor (uPAR) play crucial roles in tumour invasion and progression, making them vital targets in cancer research. Their involvement in facilitating tumour cell migration and the degradation of the extracellular matrix underscores their significance in cancer biology. Understanding these targets is crucial for generating novel therapeutic approaches to improve patient outcomes (Leth & Ploug, 2021; Zhai et al., 2022). uPA is a serine protease that regulates tissue remodelling and cell migration. The active form of uPA attaches to its high-affinity receptor on the cell surface, and particular inhibitors can alter its enzymatic activity. Beyond its use as a thrombolytic agent, uPA serves as a prognostic marker for tumours and has become a model for designing anti-cancer therapies due to the structure of its receptor-binding domain (Bharadwaj et al., 2021; Ismail et al., 2021; Kanno, 2023). One of the primary parts of the uPA system is uPA, a serine protease that weighs about 50 kDa and is made up of two polypeptide chains connected by disulfide bonds; the A chain has two domains called the Growth Factor Domain (GFD) and the Kringle Domain (KD), while the B chain contains the catalytic domain essential for activating plasminogen. A single polypeptide chain that lacks transmembrane domains and folds into three extracellular domains (DI, DII, and DIII) makes up uPAR. It binds to cell membranes via glycosyl- Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.66 https://doi.org/10.5281/zenodo.17393771 phosphatidylinositol (GPI) (Masucci et al., 2022; Torres-Paris et al., 2023; Yuan et al., 2021). The binding of uPA occurs mainly at domain I but also involves contributions from other domains. Additionally, Pro-uPA and uPAR interact to increase plasminogen’s activation into plasmin. Furthermore, inhibitors of serine proteases, including PAI-1 and PAI-2, belong to the serpin superfamily, and PAI-1 is particularly significant as it reacts rapidly with the uPA system, while PAI-2 reacts more slowly (Gonias, 2021; Kumar et al., 2022; Pechlivani et al., 2021). Research continues to explore chemical molecules designed to inhibit the activity of uPA. Current strategies involve blocking the binding between uPA and uPAR or inhibiting the catalytic activity of uPA itself. Presently, only one drug, amiloride, a potassium-sparing diuretic, is available on the market that effectively inhibits uPA activity while sparing tPA. It has been shown that amiloride inhibits tumour growth and cell proliferation in various cancers. Moreover, an orally active prodrug known as MESUPRON is undergoing clinical trials for treating breast and pancreatic tumours. Previously used therapeutically, urokinase was withdrawn from the market due to inadequate performance. However, understanding withdrawn drug targets like uPA offers valuable insights into cancer biology and therapeutic development (Dreymann et al., 2022; El Salamouni et al., 2022). Moreover, the computation approaches were widely used to find potential inhibitors (Kachhadiya et al., 2024; Mishra et al., 2024; Mishra et al., 2025; Vaghasia et al., 2024; Vakhariya et al., 2023; Vakhariya Sakina et al., 2023; Vinjoda et al., 2024). Furthermore, this study focuses on identifying a potential inhibitor that can bind to uPA and uPAR, thereby stopping their mechanism of action through a computational approach. 2. Materials and Methods 2.1 Data sources and classification of withdrawn drug targets Withdrawn drug targets were sourced from the DrugBank database (http://www.drugbank.ca/) (Wishart et al., 2006), comprising 225 targets across various species, including humans, bacteria, and protozoa. These targets were categorized into enzyme and non-enzyme types using annotations from UniProt (http://www.uniprot.org/) (UniProt, 2019). Non-enzymes were further subcategorized into carriers, transporters, channels, and receptors. Pathway involvement and protein localization were mapped using data from KEGG (https://www.genome.jp/kegg/) and UniProt (UniProt, 2019). Human targets were primarily associated with membrane-localized and secreted proteins, highlighting their accessibility for drug interactions. 2.2 Retrieval of Structural Data uPA and uPAR The 3D structures of uPA and uPAR were collected via Protein Data Bank (PDB) (http://www.rcsb.org) (Burley et al., 2022). The A chain of uPA (PDB ID: 2I9B) and the B chain structure (PDB ID: 1F5L) were used. uPAR’s structure was retrieved from the same source. These proteins were prepared using the Schrödinger Protein Preparation Wizard to ensure structural integrity by addressing missing atoms, optimizing hydrogen bonding, and removing crystallographic water molecules (Montanucci et al., 2024). 2.3 Ligand Library Generation Target-Based Approach uPA and uPAR were queried against ChEMBL (https://www.ebi.ac.uk/chembl/) (Gaulton et al., 2012) and ZINC (http://zinc.docking.org/) (Irwin & Shoichet, 2005) databases to identify known and potential inhibitors. Ligands were retrieved in SDF format, filtered by binding affinity data, and prepared via LigPrep. This process involved tautomer generation, stereoisomer selection, and energy minimization (Montanucci et al., 2024). Ligand-Based Approach Amiloride, a known uPA inhibitor, was used as the query molecule. Searches in PubChem (https://pubchem.ncbi.nlm.nih.gov/) (Kim et al., 2021) and ChEMBL databases identified structurally similar and substructural analogues. Natural products were retrieved from the KNApSAcK (http://kanaya.naist.jp/KNApSAcK/) (Afendi et al., 2012) database using a SIMCOMP similarity search. Additional compounds were curated based on patent data in PubChem (Hattori et al., 2010; Kim et al., 2023). Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.67 https://doi.org/10.5281/zenodo.17393771 2.4 Molecular Docking Analysis The selected target and retrieved ligands were prepared and used for the docking analysis via the Glide module of Schrödinger, considering the Glide’s XP (Rehman & Najmi, 2024). Moreover, the ligand library was divided into Set 1 (chemical ligands) and Set 2 (natural product derivatives). The Receptor grids were also generated for uPA and uPAR, focusing on active site residues. 2.5 Evaluation of Drug-Likeness QikProp ( http://www.schrodinger.com/QikProp) was employed to predict physicochemical properties, including LogP, solubility, and molecular weight, ensuring adherence to Lipinski’s Rule of Five. Molecules failing these criteria were excluded from further analysis. QikProp was used to screen compounds for properties like LogP, HERG inhibition, and MDCK permeability. Molecules passing these filters were subjected to final docking analyses (Almasoudi et al., 2024). 2.6 Analysis of Chimeric Molecules Top-performing ligands from chemical and natural product libraries were analyzed to design chimeric molecules. Key structural features contributing to binding affinity were identified, and hybrid molecules were synthesized computationally. These molecules underwent the same docking and drug-likeness evaluation as parent compounds. 3. Results and Discussion 3.1 Classification of Withdrawn Drug Targets The classification of drug targets from withdrawn drugs has been systematically analyzed, focusing on their distribution among various organisms, specifically Humans, Bacteria, and Protozoa. A total of 225 withdrawn drug targets were identified, with no representation from Fungi, Viruses, or other organisms. The data in Figure 1 indicate that human targets predominantly consist of non-enzymes, which outnumber enzymatic targets. Conversely, Protozoa exhibit a lower count of enzymes compared to humans. This distinction highlights the differences in biological functions and therapeutic implications among these organisms. Further analysis categorized the targets based on enzyme classes in Figure 2. Among these enzyme classes, hydrolases are particularly abundant in human drug targets, reflecting their critical roles in various physiological processes and disease mechanisms. Conversely, ligases are less frequently encountered as drug targets due to their complex catalytic mechanisms and the challenges associated with designing effective inhibitors. However, in bacteria, there is a notable scarcity of isomerases and oxidoreductases among withdrawn drug targets, which may indicate a limited therapeutic focus on these enzyme classes within this organism group. Furthermore, the classification of non-enzymatic targets in Figure 3 shows the number of drug targets for humans and bacteria across various non-enzyme classes such as carriers, transporters, channels, receptors, and others. The results reveal that humans have significantly more drug targets in all categories compared to bacteria. The Receptors class is the largest category for humans, with 102 drug targets, whereas bacteria have only 4. However, in the carrier class, humans have 6 drug targets. In contrast, bacteria have only one drug target, while drug targets for transporters and channels are mainly found in humans, with six and twenty targets, respectively. In contrast, bacteria have none, which emphasizes the role of receptor-mediated signaling in human pharmacology. The withdrawn drug targets were also classified based on their protein localization, as shown in Table 1. Most targets were localized to the cell membrane in humans and bacteria. For humans, 55 targets were found in the cell membrane, 29 in the cell junction, and 25 in the general membrane. Additionally, many human targets were secreted proteins, with 24 and 17 targets localized to the cytoplasm, respectively. However, in bacteria, drug targets were predominantly found in the cell membrane (4 targets), the cell junction (4 targets), and secreted proteins (10 targets). Other bacterial localizations, such as the cytoplasm, had minimal representation with only 1 target. Human-specific localizations included mitochondrionrelated regions, endoplasmic reticulum membranes, nucleus membranes, and lysosomes, which were absent in bacterial targets. However, Table 2 shows the classification of drug targets based on their involvement in specific biological pathways for both humans and bacteria. In humans, a total of 51 targets are associated with the neuroactive ligand-receptor interaction pathway, followed by 29 targets Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.68 https://doi.org/10.5281/zenodo.17393771 for the calcium signaling pathway and 19 targets for adrenergic signaling in cardiomyocytes, indicating a significant focus on this pathway in drug development. In contrast, bacteria exhibit a smaller number of targets, with only 6 targets involved in Peptidoglycan biosynthesis, reflecting its importance in bacterial cell wall synthesis and a potential target for antimicrobial development and also shows the differences in therapeutic targets and biological processes between the two organisms. Figure 1. The bar diagram of drug target classification of enzymes and non-enzymes in humans, bacteria, and protozoa. Figure 2. The bar diagram of comparative analysis of enzyme-class drug targets in humans, bacteria, and protozoa. Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.69 https://doi.org/10.5281/zenodo.17393771 Figure 3. The bar diagram of the distribution of drug targets in humans and bacteria across non-enzyme classes. Table 1. Classification of drug targets by protein localization in humans and bacteria Sl. No. Protein Localization No. of drug targets Human Bacteria 1. Basolateral cell membrane 2 0 2. Cell Membrane 55 4 3. Cell junction 29 4 4. Cytoplasm 17 1 5. Cytoplasmic vesicle membrane 2 0 6. Endoplasmic Reticulum Membrane 4 0 7. Membrane 25 0 8. Lysosome 2 0 9. Microsome membrane 3 0 10. Mitochondrion 1 0 11. Mitochondrion matrix 1 0 12. Mitochondrion membrane 1 0 13. Mitochondrion outer membrane 1 0 14. Mitochondrion inner membrane 1 0 15. Nucleus membrane 2 0 16. Nucleus inner membrane 1 0 17. Nucleus 13 0 18. Secreted 24 10 19. Cell inner membrane 1 0 Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.70 https://doi.org/10.5281/zenodo.17393771 Table 2. Number of pathway-based drug targets in humans and bacteria Sl. No. Pathways No. of drug targets Human Bacteria 1. Neuroactive ligand-receptor interaction 51 0 2. Calcium signaling pathway 29 0 3. Adrenergic signaling in cardiomyocytes 19 0 4. cAMP signaling pathway 18 0 5. cGMP-PKG signaling pathway 18 0 6. Complement and coagulation cascades 18 0 7. Serotonergic synapse 17 0 8. MAPK signaling pathway 16 0 9. Metabolic pathways 16 2 10. Cholinergic synapse 15 0 11. Retrograde endocannabinoid signaling 15 0 12. Dopaminergic synapse 14 0 13. GABAergicsynapse 13 0 14. Oxytocin signaling pathway 13 0 15. Dilated cardiomyopathy 12 0 16. Morphine addiction 12 0 17. Nicotine addiction 12 0 18. Cardiac muscle contraction 11 0 19. Staphylococcus aureus infection 11 0 20. Salivary secretion 10 0 21. Transcriptional misregulation in cancer 10 0 22. Vascular smooth muscle contraction 10 0 23. Pathways in cancer 9 0 24. Systemic lupus erythematosus 9 0 25. Tuberculosis 9 0 26. Amphetamine addiction 8 0 27. Leishmaniasis 8 0 28. Osteoclast differentiation 8 0 29. Pertussis 8 0 30. Phagosome 8 0 31. Regulation of actin cytoskeleton 8 0 32. Adipo cytokine signaling pathway 7 0 33. Alzheimer’s disease 7 0 34. Chagasdisease(Americantrypanosomiasis) 7 0 35. Circadian entrainment 7 0 36. Cocaine addiction 7 0 37. Gastric acid secretion 7 0 38. Pancreatic secretion 7 0 39. PI3K-Akt signaling pathway 7 0 Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.71 https://doi.org/10.5281/zenodo.17393771 40. Alcoholism 6 0 41. GnRH signaling pathway 6 0 42. Insulin secretion 6 0 43. Small cell lung cancer 6 0 44. Bile secretion 5 0 45. Biosynthesis of secondary metabolites 5 0 46. Glutamatergic synapse 5 0 47. Micro RNAs incancer 5 0 48. Non-alcoholic fatty liver disease (NAFLD) 5 0 49. Parkinson’s disease 5 0 50. PPARsignalingpathway 5 0 51. Proteoglycans in cancer 5 0 52. Thyroid hormone signalingpathway 5 0 53. TypeII diabetes mellitus 5 0 54. Amoebiasis 4 0 55. AMPKsignalingpathway 4 0 56. Arachidonicacidmetabolism 4 0 57. Endocytosis 4 0 58. Estrogensignalingpathway 4 0 59. Gap junction 4 0 60. Hemopoeticcelllineage 4 0 61. Influenza A 4 0 62. Microbial metabolism in diverse environments 4 1 63. NF-kappaB signaling pathway 4 0 64. Nitrogen metabolism 4 0 65. Non-small cell lung cancer 4 0 66. Proximal tubule bicarbonate reclamation 4 0 67. Ras signaling pathway 4 0 68. Thyroid cancer 4 0 69. Toxoplasmosis 4 0 70. Chemical carcinogenesis 3 0 71. Endocrine and other factor-regulated calcium reabsorption 3 0 72. FcgammaR-mediated phagocytosis 3 0 73. HepatitisB 3 0 74. HepatitisC 3 0 75. Herpes simplex infection 3 0 76. HTLV-Infection 3 0 77. Inflammatory bowel disease(IBD) 3 0 78. Inflammatory mediator regulation of TRPchannels 3 0 79. Legionellosis 3 0 80. Long-term potentiation 3 0 81. Measles 3 0 82. Ovarian steroidogenesis 3 0 Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.72 https://doi.org/10.5281/zenodo.17393771 83. Pentose and glucoronate interconversions 3 0 84. Platelet activation 3 0 85. Prion diseases 3 0 86. Prolactin signaling pathway 3 0 87. Rap1signalingpathway 3 0 88. Steroid hormone biosynthesis 3 0 89. TNF signaling pathway 3 0 90. Toll-likereceptorsignalingpathway 3 0 91. Acute myeloid leukemia 2 0 92. Alanine aspartateand glutamate metabolism 2 0 93. Aldosterone-regulated sodiumreabsorption 2 0 94. Amyotrophic lateral sclerosis(ALS) 2 0 95. Apoptosis 2 0 96. Arginineandprolinemetabolism 2 0 97. Carbohydratedigestionandabsorption 2 0 98. Chemokine signaling pathway 2 0 99. Collectingduct acid secretion 2 0 100. Cytokine-cytokine receptor interaction 2 0 101. Drugmetabolism-cytochromeP450 2 0 102. Glycerolipidmetabolism 2 0 103. Glycerophospholipid metabolism 2 0 104. HIF-1signalingpathway 2 0 105. Huntington disease 2 0 106. Hypertrophic cardiomyopathy(HCM) 2 0 107. Insulin signaling pathway 2 0 108. Long-termdepression 2 0 109. Malaria 2 0 110. mTOR signaling pathway 2 0 111. Naturalkiller cell mediated cytotoxicity 2 0 112. Neurotrophin signaling pathway 2 0 113. NOD-likereceptorsignalingpathway 2 0 114. Oocyte meiosis 2 0 115. Porphyrin and chlorophyll metabolism 2 0 116. Prostate cancer 2 0 117. Rheumatoid arthritis 2 0 118. RIG-I-likereceptorsignalingpathway 2 0 119. Synapticvesiclecycle 2 0 120. Tcellreceptorsignalingpathway 2 0 121. Thyroid hormone synthesis 2 0 122. Tyrosine metabolism 2 0 123. Vibriocholerae infection 2 0 124. Viral carcinogenesis 2 0 125. ABC transporters 1 0 Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.73 https://doi.org/10.5281/zenodo.17393771 126. African trypanosomiasis 1 0 127. Allograft rejection 1 0 128. alpha-linolenicacid metabolism 1 0 129. Aminobenzoate degradation 1 0 130. Antigen processing and presentation 1 0 131. Ascorbate and aldarate metabolism 1 0 132. Asthma 1 0 133. B cell receptor signaling pathway 1 0 134. Betalain biosynthesis 1 0 135. Butanoate metabolism 1 0 136. Caprolactum degradation 1 0 137. Carbon metabolism 1 0 138. Cell adhesion molecules (CAMs) 1 0 139. Central carbon metabolism in cancer 1 0 140. Chronic myeloid leukemia 1 0 141. Circadian rhythm 1 0 142. Citrate cycle (TCA cycle) 1 0 143. CytosolicDNA-sensing pathway 1 0 144. Degradation of aromatic compounds 1 0 145. D-GlutamineandD-glutamatemetabolism 1 0 146. Drug metabolism-other enzymes 1 0 147. Epithelial cell signaling in Helicobacter pylori infection 1 0 148. Epstein-Barr virus infection 1 0 149. Ether lipid metabolism 1 0 150. Fat digestion and absorption 1 0 151. Fatty acid metabolism 1 0 152. FcepsilonRIsignalingpathway 1 0 153. Focal adhesion 1 0 154. Folate biosynthesis 1 0 155. FoxO signaling pathway 1 0 156. Fructose and mannose metabolism 1 0 157. Galactose metabolism 1 0 158. Glioma 1 0 159. Glycine serine and threonine metabolism 1 0 160. Glycolysis/Gluconeogenesis 1 0 161. Graft-versus-host disease 1 0 162. Hedgehog signaling pathway 1 0 163. Hippo signaling pathway 1 0 164. Histidine metabolism 1 0 165. Isoquinolinealkaloidbiosynthesis 1 0 166. Linoleicacid metabolism 1 0 167. Melanogenesis 1 0 168. MetabolismofxenobioticsbycytochromeP450 1 0 Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.80 https://doi.org/10.5281/zenodo.17393771 4. 19604248 5. 53696129 Table 10. The docking scores of the top five patent molecules of C00019660 (Xanthine)with the A and B chains of uPA and uPAR Sl. No. ID Docking score B chain of uPA Receptor A chain of uPA 1. 53676400 -5.3908 - -3.6148 2. 3018304 -5.2222 -3.5206 - 3. 67956 - - -5.0513 4. 19604248 -5.0142 -2.6816 -2.5363 5. 53696129 -4.9963 - - Figure 10. Molecular docking analysis of 53676400 with A chain and B chain of uPA and uPAR. Figure 11. Molecular docking analysis of 3018304 with the A chain and the B chain of uPA and uPAR. Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.81 https://doi.org/10.5281/zenodo.17393771 Three chimeric molecules were designed by combining the parent molecules C00007501 and C00019660, aiming to improve binding interactions through hydrogen bonding with uPA and uPAR (Table 11). The docking results (Table 12) indicate that the first chimeric molecule, 3,7-dihydroxy-2- (hydroxyamino)-6-[hydroxy(hydroxymethyl)amino]-2,3,7,8-tetrahydropteridin-4(1H)-one, shows the best binding affinity for both the A chain, B chain, and uPAR, with docking scores of -5.236656, - 6.525169, and -5.910039, respectively. This molecule demonstrates a better binding affinity compared to amiloride. The second and third chimeric molecules, which are derivatives of the parent compounds, also exhibit promising binding results, particularly for the B chain of uPA and uPAR. The second molecule, 5-hydroxy-6-(hydroxyamino)-2,7-dioxo-1,2,4,5,6,7-hexahydro-3H-imidazo[4,5-b] pyridine3-carboxylic acid, shows a good docking score with B chain and uPAR, while the third molecule, 5,6bis(hydroxyamino)-2,7-dioxo-1,2,4,5,6,7-hexahydro-3H-imidazo[4,5-b] pyridine-3-carboxylic acid, also demonstrates significant binding to the B chain and uPAR.H-bond interactions of chimeric molecule 3,7-dihydroxy-2-(hydroxyamino)-6-[hydroxy(hydroxymethyl)amino]-2,3,7,8tetrahydropteridin-4(1H)-one with A and B chain of uPA and uPAR are shown in Figure 12. The oxygen atom of 3,7-dihydroxy-2-(hydroxyamino)-6-[hydroxy(hydroxymethyl)amino]-2,3,7,8tetrahydropteridin-4(1H)-one is mainly involved in the formation of hydrogen bonds with A chain, B chain, and uPAR. Table 11. Two-dimensional structures of the chimeric molecules Sl. No. ID Structures 1. 3,7-dihydroxy-2-(hydroxyamino)- 6- [hydroxy(hydroxymethyl)amino]- 2,3,7,8-tetrahydropteridin-4(1H)- one 2. 5-hydroxy-6-(hydroxyamino)-2,7dioxo-1,2,4,5,6,7-hexahydro-3Himidazo[4,5-b] pyridine-3carboxylic acid 3. 5,6-bis(hydroxyamino)-2,7-dioxo1,2,4,5,6,7-hexahydro-3Himidazo[4,5-b] pyridine-3carboxylic acid Table 12. The docking scores of the chimeric molecules Sl. No. ID Docking score uPA uPAR A chain B chain 1. 3,7-dihydroxy-2-(hydroxyamino)-6- [hydroxy(hydroxymethyl)amino]- 2,3,7,8-tetrahydropteridin-4(1H)-one -5.236656 -6.525169 -5.910039 2. 5-hydroxy-6-(hydroxyamino)-2,7dioxo-1,2,4,5,6,7-hexahydro-3Himidazo[4,5-b] pyridine-3carboxylic acid -4.357151 -6.520677 -4.873281 3. 5,6-bis(hydroxyamino)-2,7-dioxo1,2,4,5,6,7-hexahydro-3Himidazo[4,5-b] pyridine-3carboxylic acid -3.883489 -6.162574 -4.848436 Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.82 https://doi.org/10.5281/zenodo.17393771 Figure 12. Molecular docking analysis of the chimeric molecule with uPA and uPAR. Natural Products and Drug-Likeness Optimization All top-performing molecules passed QikProp’s drug-likeness filters, confirming their potential as drug candidates. The chimeric molecule, in particular, exhibited optimal physicochemical properties, including a favorable LogP value and molecular weight within acceptable ranges. These properties suggest good bioavailability and stability. 4. Conclusion The uPA-uPAR system represents a promising anti-cancer drug target. This study demonstrated the efficacy of computational methods in identifying inhibitors with enhanced binding affinities compared to amiloride. The novel chimeric molecule holds potential for future drug development, emphasizing the role of hybrid strategies in addressing the limitations of existing therapeutics. Funding None. Data Availability Statement Not applicable. Competing interests The authors declare no competing interests. Ethical approval Not applicable. References Afendi, F. M., Okada, T., Yamazaki, M., Hirai-Morita, A., Nakamura, Y., Nakamura, K., Ikeda, S., Takahashi, H., Altaf-Ul-Amin, M., Darusman, L. K., Saito, K., & Kanaya, S. (2012). KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research. Plant Cell Physiol, 53(2), e1. https://doi.org/10.1093/pcp/pcr165 Almasoudi, H. H., Mashraqi, M. M., Alshamrani, S. A., Alharthi, A. A., Alsalmi, O., Nahari, M. H., AlMansour, F. S. H., & Alhazmi, A. Y. M. (2024). Structure-Based In Silico Approaches Reveal IRESSA as a Multitargeted Breast Cancer Regulatory, Signalling, and Receptor Protein Inhibitor. Pharmaceuticals, 17(2), 208. Bharadwaj, A. G., Holloway, R. W., Miller, V. A., & Waisman, D. M. (2021). Plasmin and Plasminogen System in the Tumor Microenvironment: Implications for Cancer Diagnosis, Prognosis, and Therapy. Cancers (Basel), 13(8). https://doi.org/10.3390/cancers13081838 Burley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Duarte, J. M., Dutta, S., Fayazi, M., & Feng, Z. (2022). RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Science, 31(1), 187-208. Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.83 https://doi.org/10.5281/zenodo.17393771 Dreymann, N., Wuensche, J., Sabrowski, W., Moeller, A., Czepluch, D., Vu Van, D., Fuessel, S., & Menger, M. M. (2022). Inhibition of Human Urokinase-Type Plasminogen Activator (uPA) Enzyme Activity and Receptor Binding by DNA Aptamers as Potential Therapeutics through Binding to the Different Forms of uPA. Int J Mol Sci, 23(9). https://doi.org/10.3390/ijms23094890 El Salamouni, N. S., Buckley, B. J., Ranson, M., Kelso, M. J., & Yu, H. (2022). Urokinase plasminogen activator as an anti-metastasis target: inhibitor design principles, recent amiloride derivatives, and issues with human/mouse species selectivity. Biophys Rev, 14(1), 277-301. https://doi.org/10.1007/s12551-02100921-7 Galan-Vasquez, E., & Perez-Rueda, E. (2021). A landscape for drug-target interactions based on network analysis. PLoS One, 16(3), e0247018. https://doi.org/10.1371/journal.pone.0247018 Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., & Overington, J. P. (2012). ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res, 40(Database issue), D1100-1107. https://doi.org/10.1093/nar/gkr777 Gonias, S. L. (2021). Plasminogen activator receptor assemblies in cell signaling, innate immunity, and inflammation. Am J Physiol Cell Physiol, 321(4), C721-C734. https://doi.org/10.1152/ajpcell.00269.2021 Hattori, M., Tanaka, N., Kanehisa, M., & Goto, S. (2010). SIMCOMP/SUBCOMP: chemical structure search servers for network analyses. Nucleic acids research, 38(suppl_2), W652-W656. Irwin, J. J., & Shoichet, B. K. (2005). ZINC--a free database of commercially available compounds for virtual screening. J Chem Inf Model, 45(1), 177-182. https://doi.org/10.1021/ci049714+ Ismail, A. A., Shaker, B. T., & Bajou, K. (2021). The Plasminogen-Activator Plasmin System in Physiological and Pathophysiological Angiogenesis. Int J Mol Sci, 23(1). https://doi.org/10.3390/ijms23010337 Kachhadiya, D. K., Pooja, K., Mishra, S. K., & George, J. J. (2024). In Silico Based Identification Of Novel Inhibitors For Selected MDR Protein From Shigella Species: A Validation Through Molecular Docking Analysis. Educational Administration: Theory And Practice, 30(6 (S)), 309-316. Kanno, Y. (2023). The uPA/uPAR System Orchestrates the Inflammatory Response, Vascular Homeostasis, and Immune System in Fibrosis Progression. Int J Mol Sci, 24(2). https://doi.org/10.3390/ijms24021796 Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., & Yu, B. (2023). PubChem 2023 update. Nucleic acids research, 51(D1), D1373-D1380. Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2021). PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res, 49(D1), D1388-D1395. https://doi.org/10.1093/nar/gkaa971 Kumar, A. A., Buckley, B. J., & Ranson, M. (2022). The Urokinase Plasminogen Activation System in Pancreatic Cancer: Prospective Diagnostic and Therapeutic Targets. Biomolecules, 12(2). https://doi.org/10.3390/biom12020152 Leth, J. M., & Ploug, M. (2021). Targeting the Urokinase-Type Plasminogen Activator Receptor (uPAR) in Human Diseases With a View to Non-invasive Imaging and Therapeutic Intervention. Front Cell Dev Biol, 9, 732015. https://doi.org/10.3389/fcell.2021.732015 Masucci, M. T., Minopoli, M., Di Carluccio, G., Motti, M. L., & Carriero, M. V. (2022). Therapeutic Strategies Targeting Urokinase and Its Receptor in Cancer. Cancers (Basel), 14(3). https://doi.org/10.3390/cancers14030498 Mishra, S. K., Jeba Praba, J., & Georrge, J. J. (2024). An emerging trends of bioinformatics and big data analytics in healthcare. Digital Transformation in Healthcare 5.0: Volume 2: Metaverse, Nanorobots and Machine Learning, 159. Mishra, S. K., Roy, S., Chhetri, T., & Georrge, J. J. (2025). Computer‐assisted Methods and Tools for Structure‐and Ligand‐based Drug Design. Computational Methods for Rational Drug Design, 69-95. Montanucci, L., Iori, S., Lahtela-Kakkonen, M., Pauletto, M., Giantin, M., & Dacasto, M. (2024). Impact of Missense Mutations on AFB1 Metabolism in Bovine Cytochrome P4503A Isoforms: A Computational Mutagenesis and Molecular Docking Analysis. International Journal of Molecular Sciences, 25(23), 12529. Pechlivani, N., Kearney, K. J., & Ajjan, R. A. (2021). Fibrinogen and Antifibrinolytic Proteins: Interactions and Future Therapeutics. Int J Mol Sci, 22(22). https://doi.org/10.3390/ijms222212537 Rehman, Z. u., & Najmi, A. (2024). Exploring EGFR inhibitors with the aid of virtual screening, docking, and dynamics simulation studies. Journal of Biomolecular Structure and Dynamics, 42(19), 1048910509. Recent Trends in Science and Technology-2024 Bioinformatics www.christcollegerajkot.edu.in, © Christ College, Rajkot, India ISBN: 9788197073274, Page No.84 https://doi.org/10.5281/zenodo.17393771 Sarkar, K., Debnath, S., Ghosh, R., Choudhury, D., Chakraborty, K., Saha, P., Singha, A., Nandi, A., Goswami, B., & Ghosh, A. (2024). Repurposing of DrugBank molecules as dual non-hydroxamate HDAC8 and HDAC2 inhibitors by pharmacophore modeling, molecular docking, and molecular dynamics studies. Journal of Biomolecular Structure and Dynamics, 1-23. Torres-Paris, C., Chen, Y., Xiao, L., Song, H. J., Chen, P., & Komives, E. A. (2023). The autoactivation of human single-chain urokinase-type plasminogen activator (uPA). J Biol Chem, 299(10), 105179. https://doi.org/10.1016/j.jbc.2023.105179 UniProt, C. (2019). UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res, 47(D1), D506D515. https://doi.org/10.1093/nar/gky1049 Vaghasia, V. V., Sharma, K., Mishra, S. K., & Georrge, J. J. (2024). In silico identification of natural product inhibitor for multidrug resistance proteins from selected gram-positive bacteria. In Nanotechnology and In Silico Tools (pp. 309-317). Elsevier. Vakhariya, S., Kumar Mishra, S., & Georrge, J. J. (2023). Identification of Novel Curcumin Analogues to Inhibit Mitogen-Activated Protein Kinase 1 Using in Silico Combinatorial Library Design and Molecular Docking Approach. Available at SSRN 4649193. Vakhariya Sakina, S., Mishra, S. K., Sharma, K., & Georrge, J. J. (2023). Designing of a novel curcumin analogue to inhibit mitogen-activated protein kinase: A cheminformatics approach. Journal of Phytonanotechnology and Pharmaceutical Sciences, 3(1), 37-47. Vinjoda, P., Mishra, S. K., Sharma, K., & Georrge, J. J. (2024). In silico identification of novel drug target and its natural product inhibitors for herpes simplex virus. In Nanotechnology and In Silico Tools (pp. 377-383). Elsevier. Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z., & Woolsey, J. (2006). DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res, 34(Database issue), D668-672. https://doi.org/10.1093/nar/gkj067 Yuan, C., Guo, Z., Yu, S., Jiang, L., & Huang, M. (2021). Development of inhibitors for uPAR: blocking the interaction of uPAR with its partners. Drug Discov Today, 26(4), 1076-1085. https://doi.org/10.1016/j.drudis.2021.01.016 Zhai, B. T., Tian, H., Sun, J., Zou, J. B., Zhang, X. F., Cheng, J. X., Shi, Y. J., Fan, Y., & Guo, D. Y. (2022). Urokinase-type plasminogen activator receptor (uPAR) as a therapeutic target in cancer. J Transl Med, 20(1), 135. https://doi.org/10.1186/s12967-022-03329-3 How to cite this Book Chapter? APA Style Sneha Roy, Saurav Kumar Mishra, Zainab A. Laxmidhar, John J. Georrge (2024). Computational Identification of Potential Inhibitors Targeting uPA and uPAR. Recent Trends in Science and Technology-2024 (pp.65-84). ISBN: 9788197073274. Rajkot, Gujarat, India: Christ Publications. https://doi.org/10.5281/zenodo.17393771 MLA Style Sneha Roy, Saurav Kumar Mishra, Zainab A. Laxmidhar, John J. Georrge. “Computational Identification of Potential Inhibitors Targeting uPA and uPAR”. Recent Trends in Science and Technology-2024 (ISBN: 9788197073274). Rajkot, Gujarat, India: Christ Publications, 2024. pp. 65-84. https://doi.org/10.5281/zenodo.17393771 Chicago Style Sneha Roy, Saurav Kumar Mishra, Zainab A. Laxmidhar, John J. Georrge. “Computational Identification of Potential Inhibitors Targeting uPA and uPAR”. Recent Trends in Science and Technology-2024 (ISBN: 9788197073274), pp. 65-84. Rajkot, Gujarat, India: Christ Publications, 2024. https://doi.org/10.5281/zenodo.17393771