Comparative Study of PLANTs and Autodock Vina for Ligand Docking in Cancer Drug Discovery Rifki Febriansah*1, Mustofa2, Triana Hertiani3, Jaka Widada4
1School of Pharmacy, Faculty of medicine and Health Sciences, Universitas Muhammadiyah Yogyakarta 2Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta 3Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 4Faculty of Agriculture, Universitas Gadjah Mada, Yogyakarta
Abstract
Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study of biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of molecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure- and ligand-based methods in cancer disease. Cancer is still a major health problem in the world because of its high morbidity and mortality. Among the cancers that attack humans, breast cancer is the most prevalent cancer among women in the United States with 182,460 new cases (26% of cancer-fighting women) in 2008. We have performed a comparative assessment of two programs for molecular docking: PLANTS and AutoDock Vina 3.0. This was accomplished using two different studies: RMSD score and docking experiments against 6 different proteins (cyclin D1, cyclin E, p53, HER-2, EGFR, VEGF) which are specific proteins target in cancer research. The docking accuracy of the methods was judged based on the corresponding docking score. The results from experiments showed that Autodock Vina 3.0 has more appropriate than PLANTS method. A speed comparison demonstrated that Autodock Vina was faster than PLANTS among the tested docking programs. The Autodock Vina could perform all the RMSD score < 2.0 A for 6 target proteins, and PLANTS only showed 2 of 6 that has RMSD score < 2.0 A. We can conclude that Autodock Vina more effective than PLANTS method for molecular docking in cancer drug discovery.
Keywords: molecular modeling; drug discovery; molecular interaction; PLANTS, Autodock Vina
Topic: International Symposium of Engineering, Technology, and Health Sciences
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