Virtual Fragment Screening: Discovery of Histamine H₃ Receptor Ligands Using Ligand-Based and Protein-Based Molecular Fingerprints

Abstract

Virtual fragment screening (VFS) is a promising new method that uses computer models to identify small, fragment-like biologically active molecules as useful starting points for fragment-based drug discovery (FBDD). Training sets of true active and inactive fragment-like molecules to construct and validate target customized VFS methods are however lacking. We have for the first time explored the possibilities and challenges of VFS using molecular fingerprints derived from a unique set of fragment affinity data for the histamine H(3) receptor (H(3)R), a pharmaceutically relevant G protein-coupled receptor (GPCR). Optimized FLAP (Fingerprints of Ligands and Proteins) models containing essential molecular interaction fields that discriminate known H(3)R binders from inactive molecules were successfully used for the identification of new H(3)R ligands. Prospective virtual screening of 156,090 molecules yielded a high hit rate of 62% (18 of the 29 tested) experimentally confirmed novel fragment-like H(3)R ligands that offer new potential starting points for the design of H(3)R targeting drugs. The first construction and application of customized FLAP models for the discovery of fragment-like biologically active molecules demonstrates that VFS is an efficient way to explore protein-fragment interaction space in silico.

Structure-based discovery and binding site analysis of histamine receptor ligands

“…Sirci and coworkers reported the identification of new H3 antagonist fragments using ligandbased and protein-based molecular fingerprints [39]. In their study they used the FLAP approach (FLAP: Fingerprint for Ligands And Protein) with interacting molecular fields (MIFs) to identify pharmacophores on a H3R homology model built on the H1R structure as a template.…”

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].…”

Virtual fragment screening on GPCRs: A case study on dopamine D3 and histamine H4 receptors

“…Diverse conformations may also be sampled by MD simulation, Monte Carlo or low-mode conformational search starting from a single homology model [15,16]. De Graaf et al used a homology model of the histamine H3 receptor and subsequent MD sampling to provide the conformations used for retrospective and prospective virtual fragment screening [17]. In the present study we performed prospective virtual fragment screening on the available dopamine D3 receptor crystal structure and a homology model of the histamine H4 receptor based on the recently solved histamine H1 receptor crystal structure.…”