Kinetic profiling of novel spirobenzo-oxazinepiperidinone derivatives as equilibrative nucleoside transporter 1 inhibitors.

Evaluation of kinetic parameters of drug-target binding, kon, koff, and residence time (RT), in addition to the traditional in vitro parameter of affinity is receiving increasing attention in the early stages of drug discovery. Target binding kinetics emerges as a meaningful concept for the evaluation of a ligand's duration of action and more generally drug efficacy and safety. We report the biological evaluation of a novel series of spirobenzo-oxazinepiperidinone derivatives as inhibitors of the human equilibrative nucleoside transporter 1 (hENT1, SLC29A1). The compounds were evaluated in radioligand binding experiments, i.e., displacement, competition association, and washout assays, to evaluate their affinity and binding kinetic parameters. We also linked these pharmacological parameters to the compounds' chemical characteristics, and learned that separate moieties of the molecules governed target affinity and binding kinetics. Among the 29 compounds tested, 28 stood out with high affinity and a long residence time of 87 min. These findings reveal the importance of supplementing affinity data with binding kinetics at transport proteins such as hENT1.

Discovery of an Allosteric, Inactive Conformation-Selective Inhibitor of Full-Length HPK1 Utilizing a Kinase Cascade Assay.

Achieving selectivity across the human kinome is a major hurdle in kinase inhibitor drug discovery. Assays using active, phosphorylated protein kinases bias hits toward poorly selective inhibitors that bind within the highly conserved adenosine triphosphate (ATP) pocket. Targeting inactive (vs active) kinase conformations offers advantages in achieving selectivity because of their more diversified structures. Kinase cascade assays are typically initiated with target kinases in their unphosphorylated inactive forms, which are activated during the assays. Therefore, these assays are capable of identifying inhibitors that preferentially bind to the unphosphorylated form of the enzyme in addition to those that bind to the active form. We applied this cascade assay to the emerging cancer immunotherapy target hematopoietic progenitor kinase 1 (HPK1), a serine/threonine kinase that negatively regulates T cell receptor signaling. Using this approach, we discovered an allosteric, inactive conformation-selective triazolopyrimidinone HPK1 inhibitor, compound 1. Compound 1 binds to unphosphorylated HPK1 >24-fold more potently than active HPK1, is not competitive with ATP, and is highly selective against kinases critical for T cell signaling. Furthermore, compound 1 does not bind to the isolated HPK1 kinase domain alone but requires other domains. Together, these data indicate that 1 is an allosteric HPK1 inhibitor that attenuates kinase autophosphorylation by binding to a pocket consisting of residues within and outside of the kinase domain. Our study demonstrates that cascade assays can lead to the discovery of highly selective kinase inhibitors. The triazolopyrimidinone described in this study may represent a privileged chemical scaffold for further development of potent and selective HPK1 inhibitors.

Assessment of the Fragment Docking Program SEED.

The fragment docking program solvation energy for exhaustive docking (SEED) is evaluated on 15 different protein targets, with a focus on enrichment and the hit rate. It is shown that SEED allows for consistent computational enrichment of fragment libraries, independent of the effective hit rate. Depending on the actual target protein, true positive rates ranging up to 27% are observed at a cutoff value corresponding to the experimental hit rate. The impact of variations in docking protocols and energy filters is discussed in detail. Remaining issues, limitations, and use cases of SEED are also discussed. Our results show that fragment library selection or enhancement for a particular target is likely to benefit from docking with SEED, suggesting that SEED is a useful resource for fragment screening campaigns. A workflow is presented for the use of the program in virtual screening, including filtering and postprocessing to optimize hit rates.

Small Molecule Binding to Alzheimer Risk Factor CD33 Promotes Aß Phagocytosis.

Polymorphism in the microglial receptor CD33 gene has been linked to late-onset Alzheimer disease (AD), and reduced expression of the CD33 sialic acid-binding domain confers protection. Thus, CD33 inhibition might be an effective therapy against disease progression. Progress toward discovery of selective CD33 inhibitors has been hampered by the absence of an atomic resolution structure. We report here the crystal structures of CD33 alone and bound to a subtype-selective sialic acid mimetic called P22 and use them to identify key binding residues by site-directed mutagenesis and binding assays to reveal the molecular basis for its selectivity toward sialylated glycoproteins and glycolipids. We show that P22, when presented on microparticles, increases uptake of the toxic AD peptide, amyloid-ß (Aß), into microglial cells. Thus, the sialic acid-binding site on CD33 is a promising pharmacophore for developing therapeutics that promote clearance of the Aß peptide that is thought to cause AD.

Protocols for the Design of Kinase-focused Compound Libraries.

Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators.

Prediction of Protein Kinase-Ligand Interactions through 2.5D Kinochemometrics.

So far, 518 protein kinases have been identified in the human genome. They share a common mechanism of protein phosphorylation and are involved in many critical biological processes of eukaryotic cells. Deregulation of the kinase phosphorylation function induces severe illnesses such as cancer, diabetes, or inflammatory diseases. Many actors in the pharmaceutical domain have made significant efforts to design potent and selective protein kinase inhibitors as new potential drugs. Because the ATP binding site is highly conserved in the protein kinase family, the design of selective inhibitors remains a challenge and has negatively impacted the progression of drug candidates to late-stage clinical development. The work presented here adopts a 2.5D kinochemometrics (KCM) approach, derived from proteochemometrics (PCM), in which protein kinases are depicted by a novel 3D descriptor and the ligands by 2D fingerprints. We demonstrate in two examples that the protein descriptor successfully classified protein kinases based on their group membership and their Asp-Phe-Gly (DFG) conformation. We also compared the performance of our models with those obtained from a full 2D KCM model and QSAR models. In both cases, the internal validation of the models demonstrated good capabilities to distinguish "active" from "inactive" protein kinase-ligand pairs. However, the external validation performed on two independent data sets showed that the two statistical models tended to overestimate the number of "inactive" pairs.

A Proteometric Analysis of Human Kinome: Insight into Discriminant Conformation-dependent Residues.

Because of the success of imatinib, the first type-II kinase inhibitor approved by the FDA in 2001, sustained efforts have been made by the pharmaceutical industry to discover novel compounds stabilizing the inactive conformation of protein kinases. On the seven type-II inhibitors having reached the market, four were released in 2012, suggesting an acceleration of the research of such a class of compounds. Still, they represent less than a third of the protein kinase inhibitors available to patients today. The identification of key residues involved in the binding of this type of ligands in the kinase active site might ease the design of potent and selective type-II inhibitors. In order to identify those discriminant residues, we have developed a proteometric approach combining residue descriptors of protein kinase sequences and biological activities of various type-II kinase inhibitors. We applied Partial Least Squares (PLS) regression to identify 29 key residues that influence the binding of four type-II inhibitors to most proteins of the kinome. The gatekeeper residue was found to be the most relevant, confirming an essential role in ligand binding as well as in protein kinase conformational changes. Using the newly developed proteometric model, we predicted the propensity of each protein kinase to be inhibited by type-II ligands. The model was further validated using an external data set of protein/ligand activity pairs. Other residues present in the kinase domain, and more specifically in the binding site, have been highlighted by this approach, but their role in biological mechanisms is still unknown.

Extending kinome coverage by analysis of kinase inhibitor broad profiling data.

The explored kinome was extended with broad profiling using the DiscoveRx and Millipore assay panels. The analysis of the profiling of 3368 selected inhibitors on 456 kinases in the DiscoveRx format delivered several insights. First, the coverage depended on the threshold of the selectivity parameter. Second, the relation between hit confirmation rates and inhibitor selectivity showed unexpectedly that higher selectivity can increase the likelihood of false positives. Third, comparing the coverage of a focused to that of a random library showed that the design based on a maximum number of scaffolds was superior to a limited number of scaffolds. Therefore, selective compounds can be used in target validation, enable the jumpstarting of new kinase drug discovery projects, and chart new biological space via phenotypic screening.

Exploring protein kinase conformation using swarm-enhanced sampling molecular dynamics.

Protein plasticity, while often linked to biological function, also provides opportunities for rational design of selective and potent inhibitors of their function. The application of computational methods to the prediction of concealed protein concavities is challenging, as the motions involved can be significant and occur over long time scales. Here we introduce the swarm-enhanced sampling molecular dynamics (sesMD) method as a tool to improve sampling of conformational landscapes. In this approach, a swarm of replica simulations interact cooperatively via a set of pairwise potentials incorporating attractive and repulsive components. We apply the sesMD approach to explore the conformations of the DFG motif in the protein p38α mitogen-activated protein kinase. In contrast to multiple MD simulations, sesMD trajectories sample a range of DFG conformations, some of which map onto existing crystal structures. Simulated structures intermediate between the DFG-in and DFG-out conformations are predicted to have druggable pockets of interest for structure-based ligand design.

The discovery of a selective, small molecule agonist for the MAS-related gene X1 receptor.

The novel 7-transmembrane receptor MrgX1 is located predominantly in the dorsal root ganglion and has consequently been implicated in the perception of pain. Here we describe the discovery and optimization of a small molecule agonist and initial docking studies of this ligand into the receptor in order to provide a suitable lead and tool compound for the elucidation of the physiological function of the receptor.