Protein kinases are key regulatory nodes in cellular networks and their function has been shown to be intimately coupled with their structural flexibility. However, understanding the key structural mechanisms of large conformational transitions remains a difficult task. CDK2 is a crucial regulator of cell cycle. Its activity is finely tuned by Cyclin E/A and the catalytic segment phosphorylation, whereas its deregulation occurs in many types of cancer. ATP competitive inhibitors have failed to be approved for clinical use due to toxicity issues raised by a lack of selectivity. However, in the last few years type III allosteric inhibitors have emerged as an alternative strategy to selectively modulate CDK2 activity. In this study we have investigated the conformational variability of CDK2. A low dimensional conformational landscape of CDK2 was modeled using classical multidimensional scaling on a set of 255 crystal structures. Microsecond-scale plain and accelerated MD simulations were used to populate this landscape by using an out-of-sample extension of multidimensional scaling. CDK2 was simulated in the apo-form and in complex with the allosteric inhibitor 8-anilino-1-napthalenesulfonic acid (ANS). The apo-CDK2 landscape analysis showed a conformational equilibrium between an Src-like inactive conformation and an active-like form. These two states are separated by different metastable states that share hybrid structural features with both forms of the kinase. In contrast, the CDK2/ANS complex landscape is compatible with a conformational selection picture where the binding of ANS in proximity of the αC helix causes a population shift toward the inactive conformation. Interestingly, the new metastable states could enlarge the pool of candidate structures for the development of selective allosteric CDK2 inhibitors. The method here presented should not be limited to the CDK2 case but could be used to systematically unmask similar mechanisms throughout the human kinome.

The availability of well-characterized allosteric modulators is crucial for investigating the allosteric regulation of protein function. In a recently identified inactive conformation of cyclin-dependent kinase 2 (CDK2), an open allosteric pocket was detected and proposed as a site to accommodate allosteric inhibitors. Previous structure-based approaches allowed the identification of a hit compound expected to bind to this pocket. Herein we report the characterization of this compound by X-ray crystallography, which surprisingly provided a chemical structure different from that previously reported. Therefore, the compound was synthesized and completely characterized. X-ray structures of the synthesized and purchased compounds were found to be superimposable. A reaction mechanism was proposed to explain the formation of the structure indicated by crystallography. Moreover, a stereoselective synthesis was developed to evaluate the biological activity of the pure stereoisomers. Modeling studies were performed to unveil the details of the interaction with CDK2. The activity of the obtained compounds was evaluated with various biological assays. Mutagenesis experiments confirmed binding to the allosteric pocket. Finally, the allosteric ligands were shown to inhibit the growth of lung (A549) and ovarian (SKOV3) cancer cell lines. Therefore, this report presents a thorough chemical and biological characterization of the first small-molecule ligands to be used as probes to study the allosteric modulation of CDK2 activity.

Due to the well-known structure-function paradigm, conformational equilibrium plays a major role in molecular recognition. Therefore, a deep understanding of the conformational profile of small organic molecules is an essential prerequisite to modern computer-assisted drug design. However, a thorough analysis and a meaningful representation of the conformational landscape of drug-like molecules remains a challenge. The thermodynamic equilibrium of conformational states can be described in terms of probability density function (PDF) defined in the space of the relevant degrees of freedom of the system. In principle, this PDF could be estimated by traditional histogram methods, which are, however, hampered by several limitations when the variables forming the space are more than two or three. Here, we present an unsupervised parametric fitting procedure based on cluster analysis, aimed at estimating the PDF in the conformational space of small drug-like molecules with low sensitivity to data dimensionality. Indeed, data are represented in the dihedral space of the molecule and clustered using a simple adaptation of the standard k-means algorithm for periodic data. In the final step of the analysis, the PDF is derived as a linear combination of multivariate circular Gaussian distributions. We show that exploiting the analytic properties of Gaussian distributions, the proposed approach makes it possible to analyze the conformational ensemble in higher dimensional spaces with several advantages over the histogram-based methods. The posterior analysis of the PDF also helps identify a minimal subset of variables able to provide a meaningful representation of the conformational space. We tested our approach on alanine dipeptide, alanine tetrapeptide, and rilpivirine with satisfactory results compared to standard histogram-based methods and to those based on chemical intuition.

The inside cover picture shows the first small‐molecule allosteric inhibitor of cyclin‐dependent kinase 2 (CDK2) binding within an allosteric pocket in the proximity of the αC helix and distinct from the ATP binding site. The target compound was fully characterized through chemical synthesis, X‐ray crystallography, modeling studies, and biological evaluations. It was able to inhibit the growth of different cancer cell lines as a racemic mixture and as pure enantiomers. More importantly, it allowed for the fine tuning of CDK2 activity and served as an excellent probe to study the allosteric modulation of the kinase, which is one of the most important targets interfering with cell cycle progression. More information can be found in the Full Paper by Giulio Rastelli et al. on page 33 in Issue 1, 2017 (DOI: 10.1002/cmdc.201600474).