Defining An Expanded RAS Conformational Landscape Based on Over 700 Experimentally Determined Structures of KRAS, NRAS, and HRAS

Abstract

ABSTRACTFor many human cancers and tumor-associated diseases, mutations in the RAS isoforms (KRAS, NRAS, and HRAS) are the most common oncogenic alterations, making these proteins high-priority therapeutic targets. Effectively targeting the RAS isoforms requires an exact understanding of their active, inactive, and druggable conformations. However, there is no structure-guided catalogue of RAS conformations to guide therapeutic targeting or examining the structural impact of RAS mutations. We present an expanded classification of RAS conformations based on analyzing their catalytic switch 1 (SW1) and switch 2 (SW2) loops. From all 721 available human KRAS, NRAS, and HRAS structures in the Protein Data Bank (PDB) (206 RAS-protein complexes, 190 inhibitor-bound, and 325 unbound, including 204 WT and 517 mutated structures), we created a broad conformational classification based on the spatial positions of residue Y32 in SW1 and residue Y71 in SW2. Subsequently, we defined additional conformational subsets (some previously undescribed) by clustering all well modeled SW1 and SW2 loops using a density-based machine learning algorithm with a backbone dihedral-based distance metric. In all, we identified three SW1 conformations and nine SW2 conformations, each which are associated with different nucleotide states (GTP-bound, nucleotide-free, and GDP-bound) and specific bound proteins or inhibitor sites. The GTP-bound SW1 conformation can be further subdivided based on the hydrogen (H)-bond type made between residue Y32 and the GTP γ-phosphate: water-mediated, direct, or no H-bond. Further analyzing these structures clarified the catalytic impact of the G12D and G12V RAS mutations, and the inhibitor chemistries that bind to each druggable RAS conformation. To facilitate future RAS structural analyses, we have created a web database, called Rascore, presenting an updated and searchable dataset of human KRAS, NRAS, and HRAS structures in the PDB, and which includes a page for analyzing user uploaded RAS structures by our algorithm (http://dunbrack.fccc.edu/rascore/).SignificanceAnalyzing >700 experimentally determined RAS structures helped define an expanded landscape of active, inactive and druggable RAS conformations, the structural impact of common RAS mutations, and previously uncharacterized RAS-inhibitor binding modes.