Abstract
Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction fingerprinting (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful decoys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug design and discovery.
Highlights
Information on the important amino acid residues that bind to ligand could significantly increase the quality of structure-based drug design and discovery, especially in computer-aided fragment-based approaches [1]
The decision trees were resulted from Recursive Partition and Regression Trees (RPART) analysis using ensemble proteinligand interaction fingerprinting (PLIF) as the descriptor
At the beginning of the studies, we found that the published version of PyPLIF HIPPOS [18] could not recognize the disulfide bridge in the protein, which was subsequently fixed in the 0.1.2 version
Summary
Information on the important amino acid residues that bind to ligand could significantly increase the quality of structure-based drug design and discovery, especially in computer-aided fragment-based approaches [1]. The application of the knowledge in structure-based virtual screening (SBVS) campaigns has led to successful discoveries of novel fragments targeting histamine H1 [2], H3 [3], and H4 [4] receptors. The studies employed the previously identified Asp107 [5,6,7,8,9], Asp114 [5,7,8,9], and Asp94 [5,7,8,9,10] as the molecular determinants of the ligand binding to the histamine H1, H3, and H4 receptors, respectively. Istyastono et al [12] combined three-dimension (3D) QSAR analysis and molecular docking simulations to pinpoint the molecular determinants in histamine H4 receptor-ligand binding. Istyastono et al [13] employed a combination of molecular docking simulations using PLANTS [14], proteinligand interaction fingerprinting (PLIF) using PyPLIF [15,16], and supervised machine learning using Recursive Partition and Regression Trees (RPART) [17] in a retrospective
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