Identification of potent anti-fibrinolytic compounds against plasminogen and tissue-type plasminogen activator employingin silicoapproaches

AbstractAbstractThe zymogen protease Plasminogen (Plg) and its active form plasmin (Plm) carry out important functions in the blood clot disintegration (breakdown of fibrin fibers) process. Inhibition of plasmin effectively reduces fibrinolysis to circumvent heavy bleeding. Currently, available Plm inhibitor tranexamic acid (TXA) used for treating severe hemorrhages is associated with an increased incidence of seizures which in turn were traced to gamma-aminobutyric acid antagonistic activity (GABAa) in addition to having multiple side effects. Fibrinolysis can be suppressed by targeting the three important protein domains: the kringle-2 domain of tissue plasminogen activator, the kringle-1 domain of plasminogen, and the serine protease domain of plasminogen. In the present study, one million molecules were screened from the ZINC database. These ligands were docked to their respective protein targets using Autodock Vina, Schrödinger Glide, and ParDOCK/BAPPL+. Thereafter, the drug-likeness properties of the ligands were evaluated using Discovery Studio 3.5. Subsequently, we subjected the protein-ligand complexes to molecular dynamics simulation of 200 ns in GROMACS. The identified ligands P76(ZINC09970930), C97(ZINC14888376), and U97(ZINC11839443) for each protein target are found to impart higher stability and greater compactness to the protein-ligand complexes. Principal component analysis (PCA) implicates, that the identified ligands occupy smaller phase space, form stable clusters, and provide greater rigidity to the protein-ligand complexes. Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis reveals that P76, C97, and U97 exhibit better binding free energy (ΔG) when compared to that of the standard ligands. Thus, our findings can be useful for the development of promising anti-fibrinolytic agents.Communicated by Ramaswamy H. SarmaKeywords: plasminstructure-based virtual screeningmolecular dockingmolecular dynamics simulationprincipal component analysis (PCA)Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) AcknowledgmentsThe authors owe sincere thanks to the Department of Science and Technology, Government of India, Indian Association for the Cultivation of Science, Kolkata, India, and the Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India for providing all the necessary facilities. Suparna Banerjee gratefully acknowledges the support of the Council of Scientific & Industrial Research, Govt. of India for fellowship.Disclosure statementNo potential conflict of interest was reported by the authors.Authorship contributionSuparna Banerjee: Investigation, Data curation, Formal analysis, Validation, Visualization, Writing - original draft, review& editing. Yeshwanth M.: Investigation, Data curation, Formal analysis, Validation, Visualization, Writing - original draft, review& editing. D. Prabhu: Data curation, Formal analysis, Visualization, review& editing. K. Sekar: Supervision, Prosenjit Sen: Conceptualization, Supervision, Writing - review& editing. Suparna Banerjee and Yeshwanth M. have contributed equally to the work.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.