Structure-Based and Knowledge-Informed Design of B-Raf Inhibitors Devoid of Deleterious PXR Binding

Structure-Based De Novo Design for the Discovery of Miniprotein Inhibitors Targeting Oncogenic Mutant BRAF

Being a component of the Ras/Raf/MEK/ERK signaling pathway crucial for cellular responses, the VRAF murine sarcoma viral oncogene homologue B1 (BRAF) kinase has emerged as a promising target for anticancer drug discovery due to oncogenic mutations that lead to pathway hyperactivation. Despite the discovery of several small-molecule BRAF kinase inhibitors targeting oncogenic mutants, their clinical utility has been limited by challenges such as off-target effects and suboptimal pharmacological properties. This study focuses on identifying miniprotein inhibitors for the oncogenic V600E mutant BRAF, leveraging their potential as versatile drug candidates. Using a structure-based de novo design approach based on binding affinity to V600E mutant BRAF and hydration energy, 39 candidate miniprotein inhibitors comprising three helices and 69 amino acids were generated from the substructure of the endogenous ligand protein (14-3-3). Through in vitro binding and kinase inhibition assays, two miniproteins (63 and 76) were discovered as novel inhibitors of V600E mutant BRAF with low-micromolar activity, with miniprotein 76 demonstrating a specific impediment to MEK1 phosphorylation in mammalian cells. These findings highlight miniprotein 76 as a potential lead compound for developing new cancer therapeutics, and the structural features contributing to its biochemical potency against V600E mutant BRAF are discussed in detail.

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization

Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design. In a large-scale benchmark evaluation on ZINC, CASF, and PDBbind test sets, FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of the generated molecules and can recover over 92% of the original molecules in the PDBbind test set (with at least five atoms). FFLOM also exhibits excellent potential applicability in several practical scenarios encompassing fragment linking, PROTAC design, R-group growing, and R-group optimization. In all four cases, FFLOM can perfectly reconstruct the ground-truth compounds and generate over 74% of molecules with novel fragments, some of which have higher binding affinity than the ground truth.

Structure and RAF family kinase isoform selectivity of type II RAF inhibitors tovorafenib and naporafenib

Upon activation by RAS, RAF family kinases initiate signaling through the MAP kinase cascade to control cell growth, proliferation, and differentiation. Among RAF isoforms (ARAF, BRAF, and CRAF), oncogenic mutations are by far most frequent in BRAF. The BRAFV600E mutation drives more than half of all malignant melanoma and is also found in many other cancers. Selective inhibitors of BRAFV600E (vemurafenib, dabrafenib, encorafenib) are used clinically for these indications, but they are not effective inhibitors in the context of oncogenic RAS, which drives dimerization and activation of RAF, nor for malignancies driven by aberrantly dimerized truncation/fusion variants of BRAF. By contrast, a number of "type II" RAF inhibitors have been developed as potent inhibitors of RAF dimers. Here, we compare potency of type II inhibitors tovorafenib (TAK-580) and naporafenib (LHX254) in biochemical assays against the three RAF isoforms and describe crystal structures of both compounds in complex with BRAF. We find that tovorafenib and naporafenib are most potent against CRAF but markedly less potent against ARAF. Crystal structures of both compounds with BRAFV600E or WT BRAF reveal the details of their molecular interactions, including the expected type II-binding mode, with full occupancy of both subunits of the BRAF dimer. Our findings have important clinical ramifications. Type II RAF inhibitors are generally regarded as pan-RAF inhibitors, but our studies of these two agents, together with recent work with type II inhibitors belvarafenib and naporafenib, indicate that relative sparing of ARAF may be a property of multiple drugs of this class.

Design and Optimization of 1H-1,2,3-Triazole-4-carboxamides as Novel, Potent, and Selective Inverse Agonists and Antagonists of PXR

The pregnane X receptor (PXR) is a key regulator of drug metabolism. Many drugs bind to and activate PXR, causing adverse drug responses. This suggests that PXR inhibitors have therapeutic value, but potent PXR inhibitors have so far been lacking. Herein, we report the structural optimization of a series of 1H-1,2,3-triazole-4-carboxamides compounds that led to the discovery of compound 85 as a selective and the most potent inverse agonist and antagonist of PXR, with low nanomolar IC50 values for binding and cellular activity. Importantly, compound 89, a close analog of 85, is a selective and pure antagonist with low nanomolar IC50 values for binding and cellular activity. This study has provided novel, selective, and most potent PXR inhibitors (a dual inverse agonist/antagonist and a pure antagonist) for use in basic research and future clinical studies and also shed light on how to reduce the binding affinity of a compound to PXR.

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

Target Hopping from Protein Kinases to PXR: Identification of Small-Molecule Protein Kinase Inhibitors as Selective Modulators of Pregnane X Receptor from TüKIC Library

Small-molecule protein kinase inhibitors are used for the treatment of cancer, but off-target effects hinder their clinical use. Especially off-target activation of the pregnane X receptor (PXR) has to be considered, as it not only governs drug metabolism and elimination, but also can promote tumor growth and cancer drug resistance. Consequently, PXR antagonism has been proposed for improving cancer drug therapy. Here we aimed to identify small-molecule kinase inhibitors of the Tübingen Kinase Inhibitor Collection (TüKIC) compound library that would act also as PXR antagonists. By a combination of in silico screen and confirmatory cellular reporter gene assays, we identified four novel PXR antagonists and a structurally related agonist with a common phenylaminobenzosuberone scaffold. Further characterization using biochemical ligand binding and cellular protein interaction assays classified the novel compounds as mixed competitive/noncompetitive, passive antagonists, which bind PXR directly and disrupt its interaction with coregulatory proteins. Expression analysis of prototypical PXR target genes ABCB1 and CYP3A4 in LS174T colorectal cancer cells and HepaRG hepatocytes revealed novel antagonists as selective receptor modulators, which showed gene- and tissue-specific effects. These results demonstrate the possibility of dual PXR and protein kinase inhibitors, which might represent added value in cancer therapy.