Construction and Retrospective Validation of Structure-Based Virtual Screening Protocols to Identify Potent Ligands for Human Adrenergic Beta-2 Receptor

Abstract

Adrenergic Beta-2 Receptor (ADRB2) is a member of Gprotein coupled receptors family, which has served as targets for more than 30% of top-selling drugs in the market. Recently, an enhanced dataset of ligands and decoys for ADRB2 has publicly available. However, the original retrospective structure-based virtual screening campaign accompanying the dataset showed relatively poor quality with enrichment factor of true positives at 1% false positives (EF 1% ) value of 3.9. In this article, the construction and retrospective validation of a structure-based virtual screening protocol by employing PLANTS1.2 as the molecular docking software and PyPLIF as an alternative post docking scoring functions are presented. The results show that the developed protocols have better quality compared the original structure-based virtual screening with EF 1% values of 24.24 and 8.22 by using ChemPLP from PLANTS1.2 and by using Tc-PLIF from PyPLIF, respectively. Further investigation by performing systematic filtering resulted in the identification of D113, S203, and N293 as molecular determinants in ADRB2-ligand binding.

Increasing the virtual screening quality to identify adrenergic β2 receptor ligands using classification trees

“…Employing PLIF was proven to increase the quality of SBVS to identify ADRB2 ligands [12,18]. Interestingly, systematic filtering on the PLIF bitstrings could also pinpoint the important molecular determinants in the ADRB2-ligand binding [18,19].…”

“…Employing PLIF was proven to increase the quality of SBVS to identify ADRB2 ligands [12,18]. Interestingly, systematic filtering on the PLIF bitstrings could also pinpoint the important molecular determinants in the ADRB2-ligand binding [18,19]. By correlating the results from the systematic filtering on PLIF bitstrings [18] to the site-directed mutation data stored in GPCRDB [19], the important molecular determinants in the ADRB2-ligand binding were identified, i.e.…”

“…By correlating the results from the systematic filtering on PLIF bitstrings [18] to the site-directed mutation data stored in GPCRDB [19], the important molecular determinants in the ADRB2-ligand binding were identified, i.e. D113, S203 and N293 [18,19]. Notably, other machine learning methods, e.g.…”

“…Notably, other machine learning methods, e.g. binary quantitative-structure activity relationship (QSAR) [20], support vector machine [21] and recursive partitioning [8,22,23] can make use of these PLIF bitstrings to improve the SBVS quality, both in ligand identification [9,11,13,18,24,25] and ligand function prediction [12,15].…”

“…The research presented in this article aimed to employ Recursive Partitioning, and Regression Trees (RPART) method [23] to increase the quality of SBVS in the ADRB2 ligands identification by using docking poses resulted from previously SBVS campaigns [18]. Istyastono and Setyaningsih [18] has performed SBVS using PLANTS1.2 as the molecular docking software [26,27] and PyPLIF to identify the PLIF bitstrings of each docked pose [28,29].…”