PyPLIF HIPPOS-aided construction and retrospective validation of structure-based virtual screening protocol targeting VEGFR2

supanji supanji; Ayudha Bahana Perdamaian; Titi Marsifah; Riris  Istighfari Jenie; Muthi Ikawati; Dewi  Kartikawati Paramita; Enade  Perdana Istyastono

doi:10.22146/ijp.9820

supanji supanji Faculty of medicine, Public Health, and Nurse
Ayudha Bahana Perdamaian Department of Ophthalmology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Dr. Sardjito General Hospital, Yogyakarta, Indonesia
Titi Marsifah Master Program of Health and Medicine Science, Faculty of Medicine, Public Health, and Nurse, Universitas Gadjah Mada, Yogyakarta, Indonesia
Riris Istighfari Jenie
Muthi Ikawati
Dewi Kartikawati Paramita Department of Histology and Molecular Biology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia.
Enade Perdana Istyastono Faculty of Pharmacy, Sanata Dharma University, Yogyakarta, Indonesia

Keywords: VEGFR2, PyPLIF HIPPOS, machine learning, short peptide

Abstract

Recently, the discovery of small molecules as inhibitors for vascular endothelial growth factor receptor 2 (VEGFR2) is of timely interest, especially in the area of ocular neovascular diseases. On the other hand, PyPLIF HIPPOS in combination with machine learning techniques has been reported to increase the prediction quality of structure-based virtual screening (SBVS) protocols. The original version of PyPLIF has served in the development of an SBVS protocol that successfully identified novel chalcone derivatives and short peptides as potent inhibitors for acetylcholinesterase. In this short communication, construction and retrospective validation of an SBVS protocol employing PyPLIF HIPPOS targeting VEGFR2 are presented to make it publicly available. The retrospective validation employed 409 active compounds and 24,950 decoys from the enhanced version of the directory of useful decoys. All compounds were docked independently 3 times using AutoDock Vina followed by the identification of the protein-ligand interaction fingerprints (PLIF) employing PyPLIF HIPPOS. The derived ensemble PLIF descriptors were then used in the decision tree construction using a machine-learning technique called recursive partitioning and regression trees. The best decision was then incorporated in the SBVS protocol. The F-measure and enrichment factor values of the SBVS protocol were 0.387 and 76.879, respectively. Hence, the SBVS protocol is readily available to screen small molecules or short peptides.

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PyPLIF HIPPOS-aided construction and retrospective validation of structure-based virtual screening protocol targeting VEGFR2

Abstract

References

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