Albert J. Kooistra

Crystal structure-based virtual screening for novel fragment-like ligands of the human histamine H1 receptor

The recent crystal structure determinations of druggable class A G protein-coupled receptors (GPCRs) have opened up excellent opportunities in structure-based ligand discovery for this pharmaceutically important protein family. We have developed and validated a customized structure-based virtual fragment screening protocol against the recently determined human histamine H(1) receptor (H(1)R) crystal structure. The method combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. The optimized in silico screening approach was successfully applied to identify a chemically diverse set of novel fragment-like (≤22 heavy atoms) H(1)R ligands with an exceptionally high hit rate of 73%. Of the 26 tested fragments, 19 compounds had affinities ranging from 10 μM to 6 nM. The current study shows the potential of in silico screening against GPCR crystal structures to explore novel, fragment-like GPCR ligand space.

KLIFS: A Knowledge-Based Structural Database To Navigate Kinase–Ligand Interaction Space

Protein kinases regulate the majority of signal transduction pathways in cells and have become important targets for the development of designer drugs. We present a systematic analysis of kinase-ligand interactions in all regions of the catalytic cleft of all 1252 human kinase-ligand cocrystal structures present in the Protein Data Bank (PDB). The kinase-ligand interaction fingerprints and structure database (KLIFS) contains a consistent alignment of 85 kinase ligand binding site residues that enables the identification of family specific interaction features and classification of ligands according to their binding modes. We illustrate how systematic mining of kinase-ligand interaction space gives new insights into how conserved and selective kinase interaction hot spots can accommodate the large diversity of chemical scaffolds in kinase ligands. These analyses lead to an improved understanding of the structural requirements of kinase binding that will be useful in ligand discovery and design studies.

Virtual fragment screening: discovery of histamine H3 receptor ligands using ligand-based and protein-based molecular fingerprints.

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.

A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design

Chemogenomics focuses on the discovery of new connections between chemical and biological space leading to the discovery of new protein targets and biologically active molecules. G‐protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amount of ligand binding affinity data available. The increasing number of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high‐resolution structural analyses of GPCR‐ligand complexes.

KLIFS: a structural kinase-ligand interaction database

Protein kinases play a crucial role in cell signaling and are important drug targets in several therapeutic areas. The KLIFS database contains detailed structural kinase-ligand interaction information derived from all (>2900) structures of catalytic domains of human and mouse protein kinases deposited in the Protein Data Bank in order to provide insights into the structural determinants of kinase-ligand binding and selectivity. The kinase structures have been processed in a consistent manner by systematically analyzing the structural features and molecular interaction fingerprints (IFPs) of a predefined set of 85 binding site residues with bound ligands. KLIFS has been completely rebuilt and extended (>65% more structures) since its first release as a data set, including: novel automated annotation methods for (i) the assessment of ligand-targeted subpockets and the analysis of (ii) DFG and (iii) αC-helix conformations; improved and automated protocols for (iv) the generation of sequence/structure alignments, (v) the curation of ligand atom and bond typing for accurate IFP analysis and (vi) weekly database updates. KLIFS is now accessible via a website (http://klifs.vu-compmedchem.nl) that provides a comprehensive visual presentation of different types of chemical, biological and structural chemogenomics data, and allows the user to easily access, compare, search and download the data.

Discovery of novel Trypanosoma brucei phosphodiesterase B1 inhibitors by virtual screening against the unliganded TbrPDEB1 crystal structure

Trypanosoma brucei cyclic nucleotide phosphodiesterase B1 (TbrPDEB1) and TbrPDEB2 have recently been validated as new therapeutic targets for human African trypanosomiasis by both genetic and pharmacological means. In this study we report the crystal structure of the catalytic domain of the unliganded TbrPDEB1 and its use for the in silico screening for new TbrPDEB1 inhibitors with novel scaffolds. The TbrPDEB1 crystal structure shows the characteristic folds of human PDE enzymes but also contains the parasite-specific P-pocket found in the structures of Leishmania major PDEB1 and Trypanosoma cruzi PDEC. The unliganded TbrPDEB1 X-ray structure was subjected to a structure-based in silico screening approach that combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. This approach identified six novel TbrPDEB1 inhibitors with IC50 values of 10-80 μM, which may be further optimized as potential selective TbrPDEB inhibitors.

Small and colorful stones make beautiful mosaics: Fragment-Based Chemogenomics

Smaller stones with a wide variety of colors make a higher resolution mosaic. In much the same way, smaller chemical entities that are structurally diverse are better able to interrogate protein binding sites. This feature article describes the construction of a diverse fragment library and an analysis of the screening of six representative protein targets belonging to three diverse target classes (G protein-coupled receptors ADRB2, H1R, H3R, and H4R, the ligand-gated ion channel 5-HT3R, and the kinase PKA) using chemogenomics approaches. The integration of experimentally determined bioaffinity profiles across related and unrelated protein targets and chemogenomics analysis of fragment binding and protein structure allow the identification of: (i) unexpected similarities and differences in ligand binding properties, and (ii) subtle ligand affinity and selectivity cliffs. With a wealth of fragment screening data being generated in industry and academia, such approaches will contribute to a more detailed structural understanding of ligand-protein interactions.

Function-specific virtual screening for GPCR ligands using a combined scoring method.

The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H1 receptor (H1R) antagonists/inverse agonists, 2) the selective structure-based identification of ß2-adrenoceptor (ß2R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H1R and ß2R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß2R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß2R and 73% of the H1R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß2R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.

From Three-Dimensional GPCR Structure to Rational Ligand Discovery

This chapter will focus on G protein-coupled receptor structure-based virtual screening and ligand design. A generic virtual screening workflow and its individual elements will be introduced, covering amongst others the use of experimental data to steer the virtual screening process, ligand binding mode prediction, virtual screening for novel ligands, and rational structure-based virtual screening hit optimization. An overview of recent successful structure-based ligand discovery and design studies shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands for GPCRs. Moreover, the recently solved GPCR crystal structures have further increased the opportunities in structure-based ligand discovery for this pharmaceutically important protein family. The current chapter will discuss several challenges in rational ligand discovery based on GPCR structures including: (i) structure-based identification of ligands with specific effects on GPCR mediated signaling pathways, and (ii) virtual screening and structure-based optimization of fragment-like molecules.

Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A β-Adrenoceptor Case Study.

The spectacular advances in G-protein-coupled receptor (GPCR) structure determination have opened up new possibilities for structure-based GPCR ligand discovery. The structure-based prediction of whether a ligand stimulates (full/partial agonist), blocks (antagonist), or reduces (inverse agonist) GPCR signaling activity is, however, still challenging. A total of 31 β1 (β1R) and β2 (β2R) adrenoceptor crystal structures, including antagonist, inverse agonist, and partial/full agonist-bound structures, allowed us to explore the possibilities and limitations of structure-based prediction of GPCR ligand function. We used all unique protein-ligand interaction fingerprints (IFPs) derived from all ligand-bound β-adrenergic crystal structure monomers to post-process the docking poses of known β1R/β2R partial/full agonists, antagonists/inverse agonists, and physicochemically similar decoys in each of the β1R/β2R structures. The systematic analysis of these 1920 unique IFP-structure combinations offered new insights into the relative impact of protein conformation and IFP scoring on selective virtual screening (VS) for ligands with a specific functional effect. Our studies show that ligands with the same function can be efficiently classified on the basis of their protein-ligand interaction profile. Small differences between the receptor conformation (used for docking) and reference IFP (used for scoring of the docking poses) determine, however, the enrichment of specific ligand types in VS hit lists. Interestingly, the selective enrichment of partial/full agonists can be achieved by using agonist IFPs to post-process docking poses in agonist-bound as well as antagonist-bound structures. We have identified optimal structure-IFP combinations for the identification and discrimination of antagonists/inverse agonist and partial/full agonists, and defined a predicted IFP for the small full agonist norepinephrine that gave the highest retrieval rate of agonists over antagonists for all structures (with an enrichment factor of 46 for agonists and 8 for antagonists on average at a 1% false-positive rate). This β-adrenoceptor case study provides new insights into the opportunities for selective structure-based discovery of GPCR ligands with a desired function and emphasizes the importance of IFPs in scoring docking poses.