The activity and abundance of >160,000 variants are quantify the activity and abundance of >160,000 variants to construct complete maps of the energetic and allosteric architectures of five human kinase domains: SRC, FGR, JNK2/MAPK9, ZAK/MAP3K20, and TSSK2.
Abstract
Protein kinases are central to biological regulation, dysregulated in many diseases, and the targets of a hundred clinically-approved drugs. Structural conservation of kinase active sites makes the development of specific inhibitors challenging. Targeting functional secondary sites can increase specificity, reduce toxicity, overcome resistance mutations, and also activate kinases. However, the functional secondary sites to target in most kinases are unknown, and the conservation of allosteric networks in kinases and other proteins that share the same structural fold is unclear. Here, we quantify the activity and abundance of >160,000 variants to construct complete maps of the energetic and allosteric architectures of five human kinase domains: SRC, FGR, JNK2/MAPK9, ZAK/MAP3K20, and TSSK2. For inhibition, all five kinases have distance-dependent but anisotropic allostery and each kinase has a unique allosteric architecture, surface, and set of pockets to therapeutically target. A set of functional secondary sites is conserved in all five proteins, but other allosteric pockets are protein-specific or switch from inhibitory to activating in different proteins. The differences in the energetic architectures are particularly striking for activation, where the allosteric maps are highly diverged. The allosteric architecture of each kinase is therefore unique, with a distinct set of functional secondary sites to regulate and therapeutically target.
The structural and medicinal chemistry principles underlying (i) allosteric inhibition and (ii) proximity-induced degradation are summarized, with an emphasis on design logic, structure-activity relationships, and key liabilities in the beyond rule of five space.
Mei Zhou, Linshan Li, Xiao-Juan Tang et al.· Future Medicinal Chemistry· 0 citations
Dynamical network analysis is used to identify key residues involved in a dynamic allostery between the N- and C-lobes that connects the major functional units of the MAP kinase p38α, providing mechanistic insight into p38α allostery and suggesting viable opportunities for the rational design of allosteric modulators of MAP kinases.
Suchandra Roy Acharyya, J. Weisner, Rafael C. Bernardi et al.· Nature Communications· 0 citations
Genetic variants throughout the multi-domain protein leucine-rich repeat kinase 2 (LRRK2) gene are the most common cause of autosomal dominant Parkinson’s disease, and the most prevalent Parkinson’s-associated LRRK2 variants enhance kinase activity. Hence, the kinase domain has been extensively targeted for therapeutic development. However, clinical progression of LRRK2 kinase inhibitors has been limited by on-target peripheral toxicities linked to strong kinase suppression, raising the question of whether targeting LRRK2 kinase activity through other means could offer a more tunable therapeutic window. LRRK2 is one of two proteins in the human proteome that possess a Roc-COR GTPase domain in tandem with a kinase domain, and the GTPase domain has been shown to intramolecularly regulate kinase output. Here, we ask whether the Roc-COR GTPase can be targeted as an alternative approach to downregulate kinase activity. We utilize a chemical genetic approach to sensitize LRRK2 to existing GTPase inhibitors and show that pharmacologically engaging the Roc-COR GTPase domain in cells decreases but does not fully inhibit LRRK2-mediated Rab10 T73 phosphorylation. This study lays the groundwork for future efforts directed at the development of pharmacological inhibitors for the LRRK2 GTPase, providing an alternative approach for therapies targeting LRRK2-driven Parkinson’s disease.
Rachel E. Prorok, Lawrence Y. Zhu, Vickie Bowcut et al.· bioRxiv· 0 citations
Protein kinases are among the most intensively pursued therapeutic targets in oncology and beyond, yet selectivity remains largely unsolved; around 536 Manning kinase domains share a conserved ATP-binding pocket, making it difficult to target one without hitting others. Allosteric binding modes which exploit conformational states unique to individual kinases or narrow kinase subfamilies offer a principled route to selectivity. Yet the absence of a kinome-wide structural landscape of these pockets limits their translational potential. Here we construct a machine learning-guided structural atlas of 11,945 kinase-inhibitor complexes, training an Extra Trees classifier on pocket residue interaction with ligand to assign all seven canonical binding modes and resolve allosteric subclasses with pharmacological precision. Our structural analysis reveals that approximately 303 Manning kinase domains have known inhibitors bound to them, representing ∼56.5% of the Manning kinase domain; of these, only 26% (78 kinases) are targeted by non-ATP-competitive allosteric inhibitors, indicating substantial unexplored pharmacological space. Integrating these classifications with the Manning kinome phylogeny, we demonstrate that evolutionary proximity is a statistically significant predictor of shared allosteric pocket accessibility. Because a functionally similar target has similar conformational dynamics and similar druggable pockets, ‘phylogenetic druggability transfer’ can act as a strong signal for identifying whether a given kinase can be targeted using an allosteric inhibitor or not. In addition, this work repositions the kinome phylogeny as a map of pharmacological opportunity and provides a reusable predictive framework for drug discovery programs - including orthosteric, allosteric, covalent, bifunctional inhibitors, and chemical degrader - across the understudied kinome. Significance Statement Most kinase drugs bind to pocket shared across the kinome, making selectivity difficult. Allosteric pockets are more selective, but their presence and accessibility are not understood across kinome. Testing a candidate against a specific binding mode requires a dedicated assay, which is expensive and time consuming, and wasted effort if that pocket is not actually accessible. We classified thousands of kinase structures by binding mode and mapped them onto the kinome phylogenetic tree. Closely related kinases often share the same druggable pockets, so this map allows us to predict likely pockets in related kinases, helping prioritize assays and identify repurposing opportunities, making drug discovery more efficient.
Rudrakshi Chauhan, Rajiah Aldrin Denny, N. A. Murugan· bioRxiv· 0 citations
This study maps the landscape of GPCR classification and function by translating the majority of the GPCR structures into structure graphs and analyzing over 2 million structural contacts to capture key aspects of the structural communication within the GPCR superfamily with implications in drug discovery.
Sara Gentile, Angelo Felline, Sara Mazzali et al.· JACS Au· 0 citations
Together, these examples define an expanding pharmacological vocabulary for kinase drug discovery: one that asks not only whether a molecule inhibits its target kinase, but how ligand binding rewires protein stability, interactions, localisation, and function.
J. Diepeveen, Z. Kozicka· Chimia (Basel)· 0 citations
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