Open Access

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases.

The P300/CBP-associated factor plays a central role in retroviral infection and cancer development, and the C-terminal bromodomain provides an opportunity for selective targeting. Here, we report several new classes of acetyl-lysine mimetic ligands ranging from mM to low micromolar affinity that were identified using fragment screening approaches. The binding modes of the most attractive fragments were determined using high resolution crystal structures providing chemical starting points and structural models for the development of potent and selective PCAF inhibitors.

TAp63a, a homolog of the p53 tumor suppressor, is a quality control factor in the female germline. Remarkably, already undamaged oocytes express high levels of the protein, suggesting that TAp63a’s activity is under tight control of an inhibitory mechanism. Biochemical studies have proposed that inhibition requires the C-terminal transactivation inhibitory domain. However, the structural mechanism of TAp63a inhibition remains unknown. Here, we show that TAp63a is kept in an inactive dimeric state. We reveal that relief of inhibition leads to tetramer formation with ~20-fold higher DNA affinity. In vivo, phosphorylation-triggered tetramerization of TAp63a is not reversible by dephosphorylation. Furthermore, we show that a helix in the oligomerization domain of p63 is crucial for tetramer stabilization and competes with the transactivation domain for the same binding site. Our results demonstrate how TAp63a is inhibited by complex domain-domain interactions that provide the basis for regulating quality control in oocytes.

E3 ligases constitute a large and diverse family of proteins that play a central role in regulating protein homeostasis by recruiting substrate proteins via recruitment domains to the proteasomal degradation machinery. Small molecules can either inhibit, modulate or hijack E3 function. The latter class of small molecules led to the development of selective protein degraders, such as PROTACs (PROteolysis TArgeting Chimeras), that recruit protein targets to the ubiquitin system leading to a new class of pharmacologically active drugs and to new therapeutic options. Recent efforts have focused on the E3 family of Baculovirus IAP Repeat (BIR) domains that comprise a structurally conserved but diverse 70 amino acid long protein interaction domain. In the human proteome, 16 BIR domains have been identified, among them promising drug targets such as the Inhibitors of Apoptosis (IAP) family, that typically contain three BIR domains (BIR1, BIR2, and BIR3). To date, this target area lacks assay tools that would allow comprehensive evaluation of inhibitor selectivity. As a consequence, the selectivity of current BIR domain targeting inhibitors is unknown. To this end, we developed assays that allow determination of inhibitor selectivity in vitro as well as in cellulo. Using this toolbox, we have characterized available BIR domain inhibitors. The characterized chemical starting points and selectivity data will be the basis for the generation of new chemical probes for IAP proteins with well-characterized mode of action and provide the basis for future drug discovery efforts and the development of PROTACs and molecular glues.

Binding free energy calculations that make use of alchemical pathways are becoming increasingly feasible thanks to advances in hardware and algorithms. Although relative binding free energy (RBFE) calculations are starting to find widespread use, absolute binding free energy (ABFE) calculations are still being explored mainly in academic settings due to the high computational requirements and still uncertain predictive value. However, in some drug design scenarios, RBFE calculations are not applicable and ABFE calculations could provide an alternative. Computationally cheaper end-point calculations in implicit solvent, such as molecular mechanics Poisson–Boltzmann surface area (MMPBSA) calculations, could too be used if one is primarily interested in a relative ranking of affinities. Here, we compare MMPBSA calculations to previously performed absolute alchemical free energy calculations in their ability to correlate with experimental binding free energies for three sets of bromodomain–inhibitor pairs. Different MMPBSA approaches have been considered, including a standard single-trajectory protocol, a protocol that includes a binding entropy estimate, and protocols that take into account the ligand hydration shell. Despite the improvements observed with the latter two MMPBSA approaches, ABFE calculations were found to be overall superior in obtaining correlation with experimental affinities for the test cases considered. A difference in weighted average Pearson () and Spearman () correlations of 0.25 and 0.31 was observed when using a standard single-trajectory MMPBSA setup ( = 0.64 and = 0.66 for ABFE; = 0.39 and = 0.35 for MMPBSA). The best performing MMPBSA protocols returned weighted average Pearson and Spearman correlations that were about 0.1 inferior to ABFE calculations: = 0.55 and = 0.56 when including an entropy estimate, and = 0.53 and = 0.55 when including explicit water molecules. Overall, the study suggests that ABFE calculations are indeed the more accurate approach, yet there is also value in MMPBSA calculations considering the lower compute requirements, and if agreement to experimental affinities in absolute terms is not of interest. Moreover, for the specific protein–ligand systems considered in this study, we find that including an explicit ligand hydration shell or a binding entropy estimate in the MMPBSA calculations resulted in significant performance improvements at a negligible computational cost.