Structure-based virtual screening for fragment-like ligands of the G protein-coupled histamine H₄receptor

Abstract

We have explored the possibilities and challenges of structure-based virtual screening (SBVS) against the human histamine H 4 receptor (H 4 R), a key player in inflammatory responses. Several SBVS strategies, employing different H 4 R ligand conformations, were validated and optimized with respect to their ability to discriminate small fragment-like H 4 R ligands from true inactive fragments, and compared to ligand-based virtual screening (LBVS) approaches. SBVS studies with a molecular interaction fingerprint (IFP) scoring method enabled the identification of H 4 R ligands that were not identified in LBVS runs, demonstrating the scaffold hopping potential of combining molecular docking and IFP scoring. Retrospective VS evaluations against H 4 R homology models based on the histamine H 1 receptor (H 1 R) crystal structure did not give higher enrichments of H 4 R ligands than H 4 R models based on the beta-2 adrenergic receptor (β 2 R). Complementary prospective SBVS studies against β 2 R-based and H 1 R-based H 4 R homology models led to the discovery of different new fragment-like H 4 R ligand chemotypes. Of the 37 tested compounds, 9 fragments (representing 5 different scaffolds) had affinities between 0.14 and 6.3 μM at the H 4 R.

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

“…Protein-Ligand Interaction Fingerprints (PLIF) as a post docking rescoring function has been introduced and reported could optimize fragment and scaffold docking [8]. Employing this rescoring function, several SBVS protocol were constructed and successfully validated retro-and prospectively [9][10][11][12][13][14]. Most targets of the SBVS using PLIF for rescoring belong to G-Protein Coupled Receptors (GPCRs) family [10].…”

“…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].…”

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

“…Structure-Based Virtual Screening (SBVS) campaigns to discover novel fragments and ligands have obtained advantages in employing PLIF identifications and comparisons for post-processing docking poses [1,[7][8][9][10][11][12][13]. 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].…”

“…Interestingly, using interaction fingerprints to filter desired docking poses and to construct decision trees could increase the accuracy of docking simulations [15]. Employing interaction fingerprints as post docking descriptors, for example in binary Quantitative Structure-Activity Relationship (QSAR) analysis [16][17] to increase the predictive ability of SBVS protocols is therefore attractive since this offers opportunities to overcome one limitation of the available methods: the dependence on the protein-ligand structural complexes as the references [2,12,14]. Very recently, systematic filtering on PLIF interaction bitstring in retrospective SBVS campaigns targeting adrenergic β 2 receptor [18] and using decision trees by employing Recursive Partitioning and Regression Tree (RPART) package in R computational statistics software [19] in retrospective SBVS campaigns targeting estrogen receptor alpha [20] were reported to significantly increase the predictive ability.…”

“…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]. Interestingly, using interaction fingerprints to filter desired docking poses and to construct decision trees could increase the accuracy of docking simulations [15].…”