Mechanism-Based Redesign of GAP to Activate Oncogenic Ras

Abstract

Ras GTPases play a crucial role in cell signaling pathways. Mutations of the Ras gene occur in about one third of cancerous cell lines and are often associated with detrimental clinical prognosis. Hot spot residues Gly12, Gly13, and Gln61 cover 97% of oncogenic mutations, which impair the enzymatic activity in Ras. Using QM/MM free energy calculations, we present a two-step mechanism for the GTP hydrolysis catalyzed by the wild-type Ras.GAP complex. We found that the deprotonation of the catalytic water takes place via the Gln61 as a transient Brønsted base. We also determined the reaction profiles for key oncogenic Ras mutants G12D and G12C using QM/MM minimizations, matching the experimentally observed loss of catalytic activity, thereby validating our reaction mechanism. Using the optimized reaction paths, we devised a fast and accurate procedure to design GAP mutants that activate G12D Ras. We replaced GAP residues near the active site and determined the activation barrier for 190 single mutants. We furthermore built a machine learning for ultrafast screening, by fast prediction of the barrier heights, tested both on the single and double mutations. This work demonstrates that fast and accurate screening can be accomplished via QM/MM reaction path optimizations to design protein sequences with increased catalytic activity. Several GAP mutations are predicted to re-enable catalysis in oncogenic G12D, offering a promising avenue to overcome aberrant Ras-driven signal transduction by activating enzymatic activity instead of inhibition. The outlined computational screening protocol is readily applicable for designing ligands and cofactors analogously.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

“…Consequently, these methods need to be fully integrated with machine learning techniques into the enzyme design framework to streamline the effective refinement of the catalytic properties of the designed enzymes. ,,,,,,, On the activity prediction side, regression models, including linear regression and neural networks, have long been used to decipher the sequence–activity relationship of enzymes. Such relationships have then been used to guide (a) the optimization of enzyme variants , and (b) the directed evolution of enzyme activity ,, and product enantioselectivity. , …”

“… 30 , 272 , 329 , 330 , 333 , 367 , 370 , 371 On the activity prediction side, regression models, including linear regression and neural networks, have long been used to decipher the sequence–activity relationship of enzymes. Such relationships have then been used to guide (a) the optimization of enzyme variants 372 , 373 and (b) the directed evolution of enzyme activity 362 , 374 , 375 and product enantioselectivity. 376 , 377 …”

Systematic QM/MM Study for Predicting 31P NMR Chemical Shifts of Adenosine Nucleotides in Solution and Stages of ATP Hydrolysis in a Protein Environment

“…In general, ATP hydrolysis has been the subject of many experimental and computational studies that provided unique insights into the mechanism and the energetic landscape of the catalytic reaction. − The question of whether hydrolysis follows an associative, dissociative, or concerted pathway has sparked controversial debates in the literature. Despite numerous quantum mechanical/molecular mechanical (QM/MM) free energy studies on the general mechanism of phosphor ester hydrolysis, the question of whether ATP hydrolysis proceeds through the formation of short- or long-lived intermediates or no intermediates at all remains unanswered. − Reaction intermediates were postulated for ATPases many decades ago, , but escaped experimental observation for a long time. Recently, the focus of experimental studies became to capture intermediate states within the ATP hydrolysis cycle. , In human ATPase p97, monitoring the enzymatic activity via real-time nuclear magnetic resonance (NMR) led to the observation of a reaction intermediate with a lifetime of approximately 1 min .…”

Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning

“…KRAS has similar structures to HRAS and NRAS, primarily composed of the effector lobe and allosteric lobe. The effector lobe, including residues 1-86, is located at the Nterminal region of the catalytic domain, which forms three common secondary structures, namely the P-loop (9)(10)(11)(12)(13)(14)(15)(16)(17)(18), switch domain 1 (SW1: residue 28-40), and switch domain 2 (SW2: residue 59-75), as depicted in Figure 1A. The allosteric lobe, including residues 87-166, consists of an α-helical dimerization interface and nucleotide base binding motifs (Figure 1A).…”

“…The allosteric lobe, including residues 87-166, consists of an α-helical dimerization interface and nucleotide base binding motifs (Figure 1A). In terms of function, the effector lobe participates in the binding of KRAS to GTPase activating proteins (GAPs) and guanosine exchange factors (GEFs), while the allosteric lobe is responsible for relaying information through protein interactions [11][12][13][14][15][16]. Point mutations and residue modifications can directly affect the activity of KRASB by disturbing the conformational dynamics of the switch domains SW1 and SW2 [17][18][19][20][21][22][23][24][25][26].…”