Cross-reactivity virtual profiling of the human kinome by X-React \u3csup\u3eKIN\u3c/sup\u3e: A chemical systems biology approach

Many drug candidates fail in clinical development due to their insufficient selectivity that may cause undesired side effects. Therefore, modern drug discovery is routinely supported by computational techniques, which can identify alternate molecular targets with a significant potential for cross-reactivity. In particular, the development of highly selective kinase inhibitors is complicated by the strong conservation of the ATP-binding site across the kinase family. In this paper, we describe X-ReactKIN, a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a cross-reactivity virtual profile for the human kinome. To maximize the coverage of the kinome, X-ReactKIN relies solely on the predicted target structures and employs state-of-the-art modeling techniques. Benchmark tests carried out against available selectivity data from high-throughput kinase profiling experiments demonstrate that, for almost 70% of the inhibitors, their alternate molecular targets can be effectively identified in the human kinome with a high (\u3e0.5) sensitivity at the expense of a relatively low false positive rate (\u3c0.5). Furthermore, in a case study, we demonstrate how X-React KIN can support the development of selective inhibitors by optimizing the selection of kinase targets for small-scale counter-screen experiments. The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at http://cssb.biology.gatech.edu/kinomelhm/. © 2010 American Chemical Society

Using Multiple Microenvironments to Find Similar Ligand-Binding Sites: Application to Kinase Inhibitor Binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway

A theoretical entropy score as a single value to express inhibitor selectivity

<p>Abstract</p> <p>Background</p> <p>Designing maximally selective ligands that act on individual targets is the dominant paradigm in drug discovery. Poor selectivity can underlie toxicity and side effects in the clinic, and for this reason compound selectivity is increasingly monitored from very early on in the drug discovery process. To make sense of large amounts of profiling data, and to determine when a compound is sufficiently selective, there is a need for a proper quantitative measure of selectivity.</p> <p>Results</p> <p>Here we propose a new theoretical entropy score that can be calculated from a set of IC₅₀data. In contrast to previous measures such as the 'selectivity score', Gini score, or partition index, the entropy score is non-arbitary, fully exploits IC₅₀data, and is not dependent on a reference enzyme. In addition, the entropy score gives the most robust values with data from different sources, because it is less sensitive to errors. We apply the new score to kinase and nuclear receptor profiling data, and to high-throughput screening data. In addition, through analyzing profiles of clinical compounds, we show quantitatively that a more selective kinase inhibitor is not necessarily more drug-like.</p> <p>Conclusions</p> <p>For quantifying selectivity from panel profiling, a theoretical entropy score is the best method. It is valuable for studying the molecular mechanisms of selectivity, and to steer compound progression in drug discovery programs.</p

Développement de nouvelles approches protéo-chimiométriques appliquées à l'étude des interactions et de la sélectivité des inhibiteurs de kinases

The human kinome contains 518 proteins. They share a common mechanism of protein phosphorylation known to play an important role in cellular signaling pathways. Impaired kinase function is recognized to be involved in severe diseases like cancer. Due to high structural similarity between protein kinases, development of potent and selective kinase inhibitors is a challenging task. The selectivity of kinase inhibitors may lead to side effects potentially harmful. In this thesis, we first developed new selectivity metrics to determine inhibitor selectivity directly from biological inhibition data. Compared to existing metrics, the new selectivity scores can be applied on diverse inhibition data types. Second, we developed a proteometric approach in order to understand why some protein kinases are never inhibited by Type II inhibitors. The statistical model built for this purpose allowed us to identify several discriminant residues of which few of them correspond to experimentally described residues of interest. Third, using a new 3D protein kinase descriptor, we developed and validated novel proteo-chemometrics approaches to study and discover new kinase inhibitors.Le kinome humain comprend 518 protéines. Elles participent au processus de phosphorylation des protéines qui joue un rôle important dans les voies de signalisation cellulaire. Leur dérégulation est connue comme étant une cause de nombreuses maladies graves telle que les cancers. Du fait de leur grande similarité structurale des protéines kinases, il est difficile de développer des inhibiteurs qui soient à la fois efficaces et sélectifs. L’absence de sélectivité conduit le plus souvent à des effets secondaires particulièrement néfastes pour l’organisme. Au cours de cette thèse, nous avons d’abord développé de nouvelles métriques dont le but est de déterminer la sélectivité d’inhibiteurs à partir de données d’inhibition. Elles présentent l’avantage, comparées à d’autres métriques, d’être applicables sur n’importe quel type de données. Dans un deuxième temps, nous avons développé une approche protéométrique dans le but de comprendre pourquoi certaines protéines kinases ne sont jamais inhibées par des inhibiteurs de Type II. Le modèle statistique mis en place nous a permis d’identifier plusieurs résidus discriminants dont certains déjà décrits expérimentalement dans la littérature. Dans un troisième temps, nous avons développé un nouveau descripteur 3D de protéines kinases avec lequel nous avons mis en place et validé des modèles protéo-chimiométriques visant à étudier et découvrir de nouveaux inhibiteurs