allosteric ligands of CXCR3. findings may have implications for the design of CXCR3 antagonists.

BACKGROUND AND PURPOSE The chemokine receptor CXCR3 directs migration of T‐cells in response to the ligands CXCL9/Mig, CXCL10/IP‐10 and CXCL11/I‐TAC. Both ligands and receptors are implicated in the pathogenesis of inflammatory disorders, including atherosclerosis and rheumatoid arthritis. Here, we describe the molecular mechanism by which two synthetic small molecule agonists activate CXCR3. EXPERIMENTAL APPROACH As both small molecules are basic, we hypothesized that they formed electrostatic interactions with acidic residues within CXCR3. Nine point mutants of CXCR3 were generated in which an acidic residue was mutated to its amide counterpart. Following transient expression, the ability of the constructs to bind and signal in response to natural and synthetic ligands was examined. KEY RESULTS The CXCR3 mutants D112N, D195N and E196Q were efficiently expressed and responsive in chemotaxis assays to CXCL11 but not to CXCL10 or to either of the synthetic agonists, confirmed with radioligand binding assays. Molecular modelling of both CXCL10 and CXCR3 suggests that the small molecule agonists mimic a region of the ‘30s loop’ (residues 30–40 of CXCL10) which interacts with the intrahelical CXCR3 residue D112, leading to receptor activation. D195 and E196 are located in the second extracellular loop and form putative intramolecular salt bridges required for a CXCR3 conformation that recognizes CXCL10. In contrast, CXCL11 recognition by CXCR3 is largely independent of these residues. CONCLUSION AND IMPLICATIONS We provide here a molecular basis for the observation that CXCL10 and CXCL11 are allosteric ligands of CXCR3. Such findings may have implications for the design of CXCR3 antagonists. LINKED ARTICLE This article is commented on by O'Boyle, pp. 895–897 of this issue. To view this commentary visit http://dx.doi.org/10.1111/j.1476‐5381.2011.01759.x

In silico fragment-based drug discovery has become an integral component of the new fragment-based approach that has evolved over the past decade. Protein structure of high quality is essential in carrying out computational designs, and protein flexibility has been shown to impact prospective designs or docking experiments. Here we introduce methodology to calculate protein normal modes and protein molecular dynamics in torsion space which enable the development of multiple protein states to address the natural flexibility of proteins. We also present two fragment-based sampling methods, grand canonical Monte Carlo and systematic sampling, which are used to study protein-fragment interactions by generating fragment ensembles and we discuss the process by which these ensembles are linked to design ligands.

Fragment-based drug design (FBDD) has become an important and successful approach to drug discovery. In this review, we discuss two classes of simulation technologies that we routinely employ as part our of computational FBDD efforts. The first class centers on simulation methods in torsion space to develop high-quality protein models suitable for FBDD. These algorithms allow for fast molecular dynamics and modal Monte Carlo simulations. The torsion space dynamics techniques have been applied to develop models for the bound conformations of a variety of proteins including the HIV-1 protease, p38 MAP kinase, and the 5'-AMP-activated protein kinase. The second class of simulations is comprised of the Grand Canonical Monte Carlo and systematic sampling methods, which are used to explore the interactions of individual fragments with the protein target. Previously published validation studies for the binding of molecules to T4 lysozyme and the p38 MAP kinase are discussed. We review previous work to computationally assemble whole molecules from fragment binding data, a potential bottleneck in the FBDD approach. One effect of the fragment simulations is that an approximate value for the free energy of binding of a given molecule with the protein may be computed from the fragment simulations, with an estimated standard error approaching 1 kcal/mol, which is comparable to the performance of a variety of other methods reported in the literature. Drug Dev Res 72: 130-137, 2011. (C) 2010 Wiley-Liss, Inc.

We introduce TICRA (transplant-insert-constrain-relax-assemble), a method for modeling the structure of unknown protein-ligand complexes using the X-ray crystal structures of homologous proteins and ligands with known activity. We present results from modeling the structures of protein kinase-inhibitor complexes using p38 and Lck as examples. These examples show that the TICRA method may be used prospectively to create and refine models for protein kinase-inhibitor complexes with an overall backbone rmsd of less than 0.75 Å for the kinase domain, when compared to published X-ray crystal structures. Further refinement of the models of the kinase domains of p38 and Lck in complex with their cognate ligands from the published crystal structures was able to improve the rmsd's of the model complexes to below 0.5 Å. Our results show that TICRA is a useful approach to the problem of structure-based drug design in cases where little structural information is available for the target proteins and the binding mode of active compounds is unknown.

We have designed and synthesized analogues of compound C, a non-specific inhibitor of 5'-AMP-activated protein kinase (AMPK), using a computational fragment-based drug design (FBDD) approach. Synthesizing only twenty-seven analogues yielded a compound that was equipotent to compound C in the inhibition of the human AMPK (hAMPK) α2 subunit in the heterotrimeric complex in vitro, exhibited significantly improved selectivity against a subset of relevant kinases, and demonstrated enhanced cellular inhibition of AMPK.

A novel series of quinolinone-based adenosine A2B receptor antagonists was identified via high throughput screening of an encoded combinatorial compound collection. Synthesis and assay of a series of analogs highlighted essential structural features of the initial hit. Optimization resulted in an A2B antagonist (2i) which exhibited potent activity in a cAMP accumulation assay (5.1 nM) and an IL-8 release assay (0.4 nM).

AMPK has been termed the fuel sensor of mammalian cells because it directly responds to the depletion of the fuel molecule ATP. In previous work, we found that AMPK is strongly activated by tumor-like hypoxia and glucose deprivation, independently of the oxygen response system associated with HIF-1. We also observed high levels of AMPK activity in tumor cells in vivo, using different model tumors. These findings suggested the hypothesis that modulation of AMPK activity could have therapeutic value for the treatment of solid tumors. To investigate this hypothesis, we have been conducting a SAR study of potential small-molecule modulators of AMPK activity. Here we report that the chemotherapeutic drug SU11248 (sunitinib) is at least as potent an inhibitor of AMPK as compound C, which is a commonly used experimental direct inhibitor of the enzyme. We also provide a computational model of the binding pose of SU11248 to an AMPKa subunit, which suggests a structural basis for the affinity of the drug for the ATP site of the catalytic domain. The ability of SU11248 to inhibit AMPK has potential clinical significance-there may be populations of SU11248-treated patients in which AMPK activity is inhibited in normal as well as in tumor tissue.

Importance of the field: The cost of developing new drugs is estimated at similar to $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market.Areas covered in this review: The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development.What the reader will gain: This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms.Take home message: A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.

Naïve Bayesian classifiers are a relatively recent addition to the arsenal of tools available to computational chemists. These classifiers fall into a class of algorithms referred to broadly as machine learning algorithms. Bayesian classifiers may be used in conjunction with classical modeling techniques to assist in the rapid virtual screening of large compound libraries in a systematic manner with a minimum of human intervention. This approach allows computational scientists to concentrate their efforts on their core strengths of model building. Bayesian classifiers have an added advantage of being able to handle a variety of numerical or binary data such as physicochemical properties or molecular fingerprints, making the addition of new parameters to existing models a relatively straightforward process. As a result, during a drug discovery project these classifiers can better evolve with the needs of the projects from general models in the lead finding stages to increasingly precise models in the lead optimization stages that are of particular interest to a specific medicinal chemistry team. Although other machine learning algorithms abound, Bayesian classifiers have been shown to compare favorably under most working conditions and have been shown to be tolerant of noisy experimental data.

As the population of the elderly increases, there is a growing need for drugs to treat a variety of neurological diseases, such as Alzheimer's disease, Parkinson's disease, brain cancer, stroke, and infections in the central nervous system (CNS). Conversely, there is a need to identify brain penetration, and therefore potentially adverse events, from drugs acting on non-CNS targets. Late-stage clinical failures are costly in the drug discovery process, giving rise to the need for models to predict blood-brain barrier (BBB) penetration. In vivo and ex vivo models are expensive, time-consuming, and labor-intensive, giving rise to the development of in vitro and in silico models to aid in drug development early in the discovery process. Recent years have seen an increased emphasis on predictive computational models of CNS penetration. We review the progress in computational models of CNS penetration over the last five years. Computational models reported in the literature usually model the ratio of brain to blood levels for a molecule. These models can be broken down into logBB models, which attempt to predict a discrete value for the logarithm of the brain: blood ratio, and binary classifiers, which classify molecules as either brain-penetrant or non-penetrant according to an arbitrary cutoff. We also discuss whether the brain: blood ratio is an appropriate metric to use in predicting CNS penetration and the need for alternative endpoints that measure the information medicinal chemistry teams are actually interested in, such as the permeability-surface (PS) product and the fraction unbound (f(u)) in the brain.

The profile of a series of triazine and pyrimidine based ROCK inhibitors is described. An initial binding mode was established based on a homology model and the proposed interactions are consistent with the observed SAR. Compounds from the series are potent in a cell migration assay and possess a favorable kinase selectivity. In vivo activity was demonstrated for compound 1A in a spontaneous hypertensive rat model.

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