Targeting cancer with small molecule pan-KRAS degraders

Popow, Johannes; Farnaby, William; Gollner, Andreas; Kofink, Christiane; Fischer, Gerhard; Wurm, Melanie; Zollman, David; Wijaya, Andre; Mischerikow, Nikolai; Hasenoehrl, Carina; Prokofeva, Polina; Arnhof, Heribert; Arce-Solano, Silvia; Bell, Sammy; Boeck, Georg; Diers, Emelyne; Frost, Aileen B; Goodwin-Tindall, Jake; Karolyi-Oezguer, Jale; Khan, Shakil; Klawatsch, Theresa; Koegl, Manfred; Kousek, Roland; Kratochvil, Barbara; Kropatsch, Katrin; Lauber, Arnel A; McLennan, Ross; Olt, Sabine; Peter, Daniel; Petermann, Oliver; Roessler, Vanessa; Stolt-Bergner, Peggy; Strack, Patrick; Strauss, Eva; Trainor, Nicole; Vetma, Vesna; Whitworth, Claire; Zhong, Siying; Quant, Jens; Weinstabl, Harald; Küster, Bernhard; Ettmayer, Peter; Ciulli, Alessio

doi:10.1101/2023.10.24.563163

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…Increased degradation rates were also seen for other HiBit-tagged proteins tested, such as SMARCA2 and SMARCA4 (Figure 1C). Note that other proteins, such as KRAS, were not destabilized by the tag 22 . Thus, while the HiBit-tag does not generally make proteins short-lived, it points towards the necessity of carefully assessing the half-lives of fusion proteins in comparison with the endogenous proteins.…”

Section: Resultsmentioning

confidence: 99%

Confounding factors in targeted degradation of short-lived proteins

Vetma,

Casarez-Perez,

Eliaš

et al. 2024

Preprint

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SummaryTargeted protein degradation has recently emerged as a novel option in drug discovery. Natural protein half-life is expected to affect the efficacy of degrading agents, but to what extent it influences target protein degradation has not been systematically explored. Using mathematical modelling of protein degradation, we demonstrate that the natural half-life of a target protein has a dramatic effect on the level of protein degradation induced by a degrader agent which can pose significant hurdles to screening efforts. Moreover, we show that upon screening for degraders of short-lived proteins, agents that stall protein synthesis, such as GSPT1 degraders and generally cytotoxic compounds, deceptively appear as protein degrading agents. This is exemplified by the disappearance of short-lived proteins such as MCL1 and MDM2 upon GSPT1 degradation and upon treatment with cytotoxic agents such as doxorubicin. These findings have implications for target selection as well as for the type of control experiments required to conclude that a novel agent works as a bona fide targeted protein degrader.

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Section: Resultsmentioning

confidence: 99%

Confounding factors in targeted degradation of short-lived proteins

Vetma,

Casarez-Perez,

Eliaš

et al. 2024

Preprint

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“…Based on the degradation of endogenous SMARCA2 the model correctly approximates the less efficient degradation observed for the more short-lived HiBit-SMARCA2, which has a half-life of approximately 4 h (Figure B). This emphasizes the fact that the half-life of a POI should be known prior to estimating k inact and K i and highlights the need for higher k inact values to achieve significant maximal degradation of short-lived proteins …”

Section: Resultsmentioning

confidence: 99%

“…This emphasizes the fact that the half-life of a POI should be known prior to estimating k inact and K i and highlights the need for higher k inact values to achieve significant maximal degradation of short-lived proteins. 24 Stalling of Protein Synthesis via GSPT1 Degradation Causes a Drop in the Levels of Short-Lived Proteins. In attempts to discover PROTACs that degrade CRAF, a library of 6500 compounds was generated by fusing a total of 20 different RAF binders to several E3 ligase binders, including binders of CRBN, VHL, IAP, and MDM2.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Confounding Factors in Targeted Degradation of Short-Lived Proteins

Vetma,

Perez,

Eliaš

et al. 2024

ACS Chem. Biol.

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Targeted protein degradation has recently emerged as a novel option in drug discovery. Natural protein half-life is expected to affect the efficacy of degrading agents, but to what extent it influences target protein degradation has not been systematically explored. Using simple mathematical modeling of protein degradation, we find that the natural half-life of a target protein has a dramatic effect on the level of protein degradation induced by a degrader agent which can pose significant hurdles to screening efforts. Moreover, we show that upon screening for degraders of short-lived proteins, agents that stall protein synthesis, such as GSPT1 degraders and generally cytotoxic compounds, deceptively appear as protein-degrading agents. This is exemplified by the disappearance of short-lived proteins such as MCL1 and MDM2 upon GSPT1 degradation and upon treatment with cytotoxic agents such as doxorubicin. These findings have implications for target selection as well as for the type of control experiments required to conclude that a novel agent works as a bona fide targeted protein degrader.

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“…Here, we demonstrate how atomic sensitivities can guide rational changes to the PROTAC linker to minimize PROTAC ionizability. 5A-B) 28 . Our model predicts this PROTAC has a pKa of 6.51 (P-1, Figure 4B) , suggesting protonatability at physiologic pH values.…”

Section: Atomic Sensitivity Analysis Can Inform Lead Compound Optimiz...mentioning

confidence: 99%

Interpretable deep-learning pKa prediction for small molecule drugs via atomic sensitivity analysis

DeCorte,

Brown,

Meiler

2024

Preprint

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Machine learning (ML) models play a crucial role in predicting properties essential to drug development, such as a drug’s logscale acid-dissociation constant (pKa). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability, in part due to a dependence on explicit encodings of input molecules’ molecular substructures. To this end, atomic-resolution information is accessible in chemical structures by observing model response to atomic perturbations of an input molecule; however, no methods exist that systematically utilize this information for model and molecular analysis. Here, we present BCL-XpKa, a substructure-independent, deep neural network (DNN)-based pKa predictor that generalizes well to novel small molecules. BCL-XpKa discretizes pKa prediction from a regression problem into a multitask-classification problem, which accumulates data for prediction at biologically relevant pH values and records the model’s uncertainty in its prediction as a discrete distribution for each pKa prediction. BCL-XpKa outperforms modern ML pKa predictors and accurately models the effects of common molecular modifications on a molecule’s ionizability. We then leverage BCL-XpKa’s substructure independence to introduce atomic sensitivity analysis (ASA), which quickly decomposes a molecule’s predicted pKa value into its respective atomic contributions without model retraining. When paired with BCL-XpKa, ASA informs that BCL-XpKa has implicitly learned high-resolution information about molecular substructures. We further demonstrate ASA’s utility in structure preparation for protein-ligand docking by identifying ionization sites in 97.8% and 83.4% of complex small molecule acids and bases. We then apply ASA with BCL-XpKa to understand the physicochemical liabilities and guide optimization of a recently published KRAS-degrading PROTAC.

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Targeting cancer with small molecule pan-KRAS degraders

Cited by 9 publications

References 40 publications

Confounding factors in targeted degradation of short-lived proteins

Confounding factors in targeted degradation of short-lived proteins

Confounding Factors in Targeted Degradation of Short-Lived Proteins

Interpretable deep-learning pKa prediction for small molecule drugs via atomic sensitivity analysis

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