PyPLIF-ASSISTED REDOCKING INDOMETHACIN-(R)-ALPHA-ETHYL-ETHANOLAMIDE INTO CYCLOOXYGENASE-1

Abstract

Identification of Protein-Ligand Interaction Fingerprints (PLIF) has been performed as the rescoring strategy to identify the best pose for the docked poses of indomethacin-(R)-α-ethyl-etanolamide (IMM) in the binding site of cyclooxygenase-1 (COX-1) from simulations using PLANTS molecular docking software version 1.2 (PLANTS1.2). Instead of using the scoring functions included in the docking software, the strategy presented in this article used external software called PyPLIF that could identify the interactions of the ligand to the amino acid residues in the binding pocket and presents them as binary bitstrings, which subsequently were compared to the interaction bitstrings of the co-crystal ligand pose. The results show that PyPLIF-assisted redocking strategy could select the correct pose much better compared to the pose selection without rescoring. Out of 1000 iterative attempts, PyPLIF-assisted redocking simulations could identify 971 correct poses (more than 95%), while the redocking simulations without PyPLIF could only identify 500 correct poses (50%).These works have also provided us with the initial step of the construction of a valid Structure-Based Virtual Screening (SBVS) protocol to identify COX-1 inhibitors.

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

“…The docking poses completed with their ChemPLP scores [26] and PLIF bitstrings [28,29] were obtained from previously published retrospective SBVS campaigns on ADRB2 ligands and decoys [5,18]. This previously published SBVS protocol [18] was used as the reference protocol in this research.…”

“…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]. The constructed SBVS protocol was retrospectively validated using ADRB2 ligands and decoys from DUD-e [5], which consisted of 231 ADRB2 ligands and 15,000 decoys [5,18].…”

Binary Quantitative Structure-Activity Relationship Analysis to Increase the Predictive Ability of Structure-Based Virtual Screening Campaigns Targeting Cyclooxygenase-2

“…The development of methods and computer applications to identify and compare Protein-Ligand Interaction Fingerprints (PLIF) [1][2][3][4][5] and its variances has been of considerable interest since employing such fingerprints was a promising strategy to leverage the wealth of generated data in rational drug design [6]. Together with Structural Interaction Fingerprint (SIFt) and molecular interaction fingerprinting [6], PLIF was categorized as binding site-focused interaction fingerprinting methods [2].…”

“…Mainly for docking purposes, molecular interaction fingerprinting featuring strong and weak hydrogen bonds, ionic and hydrophobic interactions, π-stacking as well as π-cation interactions and metal complexes was developed in 2007 [1][2]. Inspired by this molecular interaction fingerprinting [1], our research group developed a Python implementation of PLIF featuring hydrogen bonds, ionic and hydrophobic interactions and π-stacking under the name PyPLIF [4][5]. Molecular interaction fingerprinting [1] has been expanded recently by encoding the patterns of protein−ligand interactions in fingerprints and graphs that could be applied for post-processing docking poses and search for plausible bioisosteric scaffolds [2][3].…”

“…Rescoring the results of the molecular docking simulations by calculating Tanimoto-coefficient similarity with a PLIF reference (Tc-PLIF) has been shown to increase the predictive ability of several SBVS campaigns [1,6,[11][12][13] and to better re-dock small molecules in their native poses [1,4,6] compared to standard docking scores [2][3]. Notably, different PLIF references could lead to very different paths of the discoveries [12,14].…”