Using stability-based proteomics, this study shows how protein folding stability-based profiling can expand the actionable target landscape of small molecules beyond canonical covalent interactions, uncovering noncovalent off-targets that may underlie response heterogeneity and treatment-associated toxicity.
Abstract
KRASG12C inhibitors have demonstrated meaningful clinical benefit in KRASG12C-mutant non-small cell lung cancer (NSCLC), yet responses remain heterogeneous and treatment-associated toxicities persist for reasons that are incompletely understood. Cysteine profiling indicates that these covalent inhibitors are highly selective for mutant KRAS; however, such approaches cannot detect noncovalent engagement of additional non-RAS proteins. Here, we used a protein-folding stability profiling technique, stability of proteins from rates of oxidation (SPROX), to identify protein targets of the clinical KRASG12C inhibitor, divarasib (GDC-6036), in KRAS-mutant NSCLC lysates. SPROX revealed a focused set of candidate interactors, including the essential splicing factor RBM39, which was reproducibly stabilized at both divarasib concentrations tested. We subsequently confirmed that divarasib directly and noncovalently binds to RBM39 protein. In NSCLC cells, divarasib increased RBM39 protein abundance and antagonized RBM39 degradation induced by the aryl-sulfonamide molecular glue indisulam through a post-transcriptional mechanism. Divarasib and RBM39 degraders reciprocally antagonized each other’s cytotoxicity, and RBM39 knockdown modestly reduced divarasib-induced cell death. Mechanistically, divarasib-mediated RBM39 stabilization regulated both INSR expression and alternative splicing, altered downstream insulin receptor signaling, and contributed to divarasib-associated cytotoxicity. Consistent with these findings, RBM39 and INSR expression were positively correlated across multiple human cancer types. Collectively, these findings identify RBM39 as a previously unrecognized noncovalent target of divarasib and uncover an RBM39–INSR signaling axis that modulates cellular responses to both divarasib and RBM39 degraders. Significance Covalent KRASG12C inhibitors are widely considered highly target-selective because adduct-based chemoproteomics approaches almost exclusively identify mutant KRAS as their target, detecting few proteins beyond it. However, these methods cannot capture noncovalent interactions and can sometimes miss covalent interactors. Using stability-based proteomics, we show that the clinical KRASG12C inhibitor divarasib targets RBM39, an essential splicing factor degraded by anticancer aryl-sulfonamide molecular glues. Divarasib opposes degrader-induced RBM39 loss, attenuates degrader cytotoxicity, and preserves RBM39-dependent regulation of the insulin receptor. These findings uncover a previously unrecognized non-KRAS axis of divarasib activity that is invisible to conventional target-deconvolution methods. They also suggest potential liability associated with combining RBM39 degraders with divarasib or other KRASG12C inhibitors that engage RBM39. More broadly, our study illustrates how protein folding stability-based profiling can expand the actionable target landscape of small molecules beyond canonical covalent interactions, uncovering noncovalent off-targets that may underlie response heterogeneity and treatment-associated toxicity. HIGHLIGHTS Protein folding stability profiling identifies RBM39 as a noncovalent target of divarasib. Divarasib stabilizes RBM39 and blocks its degradation by aryl-sulfonamide glues. Divarasib and RBM39 degraders reciprocally antagonize each other’s cytotoxicity. RBM39 supports insulin receptor expression and divarasib-induced cell death. eTOC BLURB Chen et al. apply protein folding stability-based proteomics to the covalent KRASG12C inhibitor divarasib and identify the splicing factor RBM39 as a noncovalent target of divarasib. Divarasib stabilizes RBM39, antagonizes aryl-sulfonamide degraders, and preserves RBM39-dependent insulin receptor signaling, revealing a non-KRAS axis invisible to adduct-based target deconvolution.
This study constructed a pH-responsive P-TN/SF@Fe-Cur composite coating that demonstrated significant anti-infective, anti-inflammatory, antioxidant, pro-angiogenic, and pro-osteogenic effects in rat subcutaneous infection and femoral defect models.
ProteinReasoner is developed, a multimodal generative protein foundation model that sequentially connects amino acid sequence, evolutionary constraints and three-dimensional structure within a shared autoregressive architecture and suggests a general route towards reasoning across interdependent representations in other scientific domains.
Chaozhong Liu, Linlin Chao, Shaomin Ji et al.· bioRxiv· 1 citation
Due to its importance and wide adoption, wheat cultivation is promptly required to shift towards sustainable practices, reducing the dependency on chemical components. Among bio-based solutions aimed at securing the sustainability of wheat cultivation, biostimulants offer a versatile platform of eco-friendly tools assuring sustainability and profitability. Microalgae present a concrete example of a biostimulant source due to their richness in metabolites and high value products. Therefore, this study evaluated the biostimulant potential of eleven eco-extracts prepared from soil-isolated microalgae strains. Eco-extracts applied via soil drench at low dose (0.1 g/L) were investigated for their biostimulant effects on wheat growth, physiology, yield, and quality under controlled conditions. Results demonstrated significant ameliorations in treated plants as compared to the control, with no phytoinhibitory effects. Remarkable enhancements were notable in growth parameters such as shoot and root lengths (+40-70%), physiological traits such as total chlorophyll and stomatal conductance (+7-52%), yield components in the example of grain number per spike and thousand grain weight (+17-103%), and grain quality namely protein and polyphenol content (+2-fold to 4-fold). Similarly, phosphorus accumulation and uptake were significantly improved, while soil physicochemical status was ameliorated, indicating enhanced fertility. Multivariate analysis and composite index ranking marked Chlorella sp. GA18, Chlorella sp. GA65, Scenedesmus sp. GA69, and Chlorococcum sp. GA63 as eco-extracts with consistent performances across all plant traits. These findings highlighted the promising potential of integrating microalgae-based eco-friendly extracts in sustainable wheat cultivation.
Amer Chabili, Z. Hakkoum, F. Minaoui et al.· Plant Science· 1 citation
Effective control of fluid flows is critical across transportation, energy and medicine, where it can increase lift, reduce drag, enhance mixing and attenuate noise1-3. Yet fluids are notoriously difficult to control because they involve high-dimensional, nonlinear and multiscale dynamics that resist conventional approaches4-6. Reinforcement learning has driven remarkable progress in fields such as protein folding and complex games, which have shared benchmarks and standardized environments7-10. Fluid dynamics has lacked such infrastructure, so each controller is typically tuned to a single geometry and operating condition, making progress difficult to accumulate, transfer and compare11-13. Here we introduce HydroGym, a solver-independent reinforcement learning platform providing more than 60 validated, openly available flow control environments spanning from canonical laminar flows to complex turbulent flows, with systematic progression in the Reynolds number up to Re = 4 × 105, and Mach number variations in two and three dimensions. Across these environments, agents repeatedly discover robust control principles, including boundary layer manipulation, disruption of acoustic feedback and reorganization of turbulent wakes. Critically, we demonstrate a proof of concept for zero-shot transfer, in which agents that are trained exclusively in inexpensive surrogate environments are deployed to challenging real-world scenarios such as a three-dimensional wing section. We achieve a 38% reduction in local skin friction while reducing exploration costs by four orders of magnitude compared with direct on-wing optimization. As this transfer exploits shared near-wall physics, the breadth of generalization remains open, suggesting a new pathway for research toward policy generalization across computationally prohibitive simulation environments. By offering a common, extensible foundation for reproducible research, HydroGym moves flow control from isolated case studies toward a cohesive community effort.
Christian Lagemann, Sajeda Mokbel, Miro Gondrum et al.· Nature· 1 citation
ABSTRACT Microplastics (MPs) accumulation in ecosystem and human organs poses urgent environmental and health risks, yet few enzymes efficiently degrade polyethylene terephthalate (PET) under physiological conditions. We leveraged deep learning to mine unexplored sequence space across 246 million proteins, discovering AhPETase, an evolutionarily distinct hydrolase with low homology (<50% sequence identity) to known PET‐degrading enzymes. This noncanonical biocatalyst efficiently depolymerizes PET at 37°C, outperforming all typical PETases and achieving a 7.76‐fold enhancement over IsPETase, one of the most representative mesophilic PETases. Additionally, engineered variant AhPETaseM1 retains functional activity for over 20 days under physiological conditions and can degrade post‐consumer PET MPs 34‐fold faster than recombinant human‐derived enzyme MG8 (rMG8) under equal enzyme loading. Critically, it reversed PET‐induced toxicity in human lung and colon cells, establishing the first proof‐of‐concept for enzymatic MPs detoxification.
Yuxuan Wang, Shijie He, Yuheng Chang et al.· Advancement of science· 0 citations
Current evidence linking mitochondrial dysfunction, ER stress, and ER-mitochondrial crosstalk to the pathogenesis of chronic pain is summarized and their potentials as therapeutic targets are discussed.
A. Yadawa, Sufang Liu, F. Tao· Brain Science· 0 citations