Clavis Aurea? Structure-enabled approaches of identifying and optimizing GPCR ligands

The center piece of this thesis is the key-lock metaphor, specifically in the context of computer-aided drug design. This concept was introduced in chapter 1 where the process of drug discovery and lead optimization is discussed. Due to the fact that there are many locks (proteins) and even more keys (ligands), computational research can provide an interesting opportunity to aid and speedup drug discovery. Different methods are introduced in chapter 1, which were used throughout this thesis. In chapter 2 an overview is provided of the different methods of docking and Virtual Screening in the context of GPCRs. Chapter 3 demonstrates the importance of including water molecules in Virtual Screening. In Chapter 4 a Virtual Screen is applied with explicit water molecules on the Adenosine A2A receptor. Chapter 5 presents and discusses an alternative method of analyzing Virtual Screens, Interaction Fingerprints. In Chapter 6 it is demonstrated that more recent statistical methods like deep neural networks are able to outperform other methods, like naïve bayes and random forests. Chapter 7 introduces a method of calculating relative binding energy differences, Free Energy Perturbation (FEP). In Chapter 8 we conclude the thesis with general conclusions and an outlook of the field.The research was financially supported by the Netherlands Organization for Scientific Research - Chemical Sciences (NWO-TOP #714.011.001).Medicinal Chemistr

A covalent antagonist for the human adenosine A(2A) receptor

The structure of the human A(2A) adenosine receptor has been elucidated by X-ray crystallography with a high affinity non-xanthine antagonist, ZM241385, bound to it. This template molecule served as a starting point for the incorporation of reactive moieties that cause the ligand to covalently bind to the receptor. In particular, we incorporated a fluorosulfonyl moiety onto ZM241385, which yielded LUF7445 (4-((3-((7-amino-2-(furan-2-yl)-[1, 2, 4]triazolo[1,5-a][1, 3, 5]triazin-5-yl)amino)propyl)carbamoyl)benzene sulfonyl fluoride). In a radioligand binding assay, LUF7445 acted as a potent antagonist, with an apparent affinity for the hA(2A) receptor in the nanomolar range. Its apparent affinity increased with longer incubation time, suggesting an increasing level of covalent binding over time. An in silico A(2A)-structure-based docking model was used to study the binding mode of LUF7445. This led us to perform site-directed mutagenesis of the A(2A) receptor to probe and validate the target lysine amino acid K153 for covalent binding. Meanwhile, a functional assay combined with wash-out experiments was set up to investigate the efficacy of covalent binding of LUF7445. All these experiments led us to conclude LUF7445 is a valuable molecular tool for further investigating covalent interactions at this receptor. It may also serve as a prototype for a therapeutic approach in which a covalent antagonist may be needed to counteract prolonged and persistent presence of the endogenous ligand adenosine

A covalent antagonist for the human adenosine A2A receptor

The structure of the human A2A adenosine receptor has been elucidated by X-ray crystallography with a high affinity non-xanthine antagonist, ZM241385, bound to it. This template molecule served as a starting point for the incorporation of reactive moieties that cause the ligand to covalently bind to the receptor. In particular, we incorporated a fluorosulfonyl moiety onto ZM241385, which yielded LUF7445 (4-((3-((7-amino-2-(furan-2-yl)-[1, 2, 4]triazolo[1,5-a][1, 3, 5]triazin-5-yl)amino)propyl)carbamoyl)benzene sulfonyl fluoride). In a radioligand binding assay, LUF7445 acted as a potent antagonist, with an apparent affinity for the hA2A receptor in the nanomolar range. Its apparent affinity increased with longer incubation time, suggesting an increasing level of covalent binding over time. An in silico A2A-structure-based docking model was used to study the binding mode of LUF7445. This led us to perform site-directed mutagenesis of the A2A receptor to probe and validate the target lysine amino acid K153 for covalent binding. Meanwhile, a functional assay combined with wash-out experiments was set up to investigate the efficacy of covalent binding of LUF7445. All these experiments led us to conclude LUF7445 is a valuable molecular tool for further investigating covalent interactions at this receptor. It may also serve as a prototype for a therapeutic approach in which a covalent antagonist may be needed to counteract prolonged and persistent presence of the endogenous ligand adenosine.KEYWORDS: A2A adenosine receptor; Adenosine; Covalent antagonist; G protein-coupled receptors; Radioligand bindin

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption

Structure-Affinity Relationships and Structure-Kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A(3) Receptor Antagonists

We expanded on a series of pyrido[2,1-f]purine-2,4-clione derivatives as human adenosine A(3) receptor (hA(3)R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA(3)R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [S-35]GTP gamma S binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a k(on)-k(off)-K-D kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA(3)R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds' binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA(3)R antagonists in the early phase of drug discovery

Advances in GPCR Modeling Evaluated by the GPCR Dock 2013 Assessment: Meeting New Challenges

Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems

Structure-Affinity Relationships and Structure-Kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A(3) Receptor Antagonists

We expanded on a series of pyrido[2,1-f]purine-2,4-clione derivatives as human adenosine A(3) receptor (hA(3)R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA(3)R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [S-35]GTP gamma S binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a k(on)-k(off)-K-D kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA(3)R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds' binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA(3)R antagonists in the early phase of drug discovery

Applications of proteochemometrics - from species extrapolation to cell line sensitivity modelling

Medicinal Chemistr