Determining KRAS4B-Targeting Compound Specificity by Top-Down Mass Spectrometry.

We present a novel method to determine engagement and specificity of KRAS4B-targeting compounds in vitro. By employing top-down mass spectrometry (MS), which analyzes intact and modified protein molecules (proteoforms), we can directly visualize and confidently characterize each KRAS4B species within compound-treated samples. Moreover, by employing targeted MS2 fragmentation, we can precisely localize each compound molecule to a specific residue on a given KRAS4B proteoform. This method allows us to comprehensively evaluate compound specificity, clearly detect nonspecific binding events, and determine the order and frequency with which they occur. We provide two proof-of-concept examples of our method employing publicly available compounds, along with detailed protocols for sample preparation, top-down MS data acquisition, targeted proteoform MS2 fragmentation, and analysis of the resulting data. Our results demonstrate the concentration dependence of KRAS4B-compound engagement and highlight the ability of top-down MS to directly map compound binding location(s) without disrupting the KRAS4B primary structure. Our hope is that this novel method may help accelerate the identification of new successful targeted inhibitors for KRAS4B and other RAS isoforms.

Natural Product Graveoline Modulates Kirsten Rat Sarcoma Viral Oncogene Homologue (KRAS) Membrane Association: Insights from Advanced Spectroscopic Studies.

The KRAS gene plays a pivotal role in numerous cancers by encoding a GTPase that upon association with the plasma membrane activates the MAPK pathway, promoting cellular proliferation. In our study, we investigated small molecules that disrupt KRAS's membrane interaction, hypothesizing that such disruption could in turn inhibit mutant RAS signaling. Native mass spectrometry screening of KRAS-FMe identified compounds with a preference for interacting with the hypervariable region (HVR), and surface plasmon resonance (SPR) further refined our selection to graveoline as a compound exhibiting preferential HVR binding. Subsequent nuclear magnetic resonance (NMR) analysis showed that graveoline's interaction with KRAS depends on C-terminal O-methylation. Moreover, our findings revealed multiple interaction sites, suggesting weak engagement with the KRAS G domain. Using nanodiscs as a membrane mimetic, further characterization through NMR and Förster resonance energy transfer (FRET) studies demonstrated graveoline's ability to perturb KRAS membrane interaction in a biochemical setting. Our biophysical approach sheds light on the intricate molecular mechanisms underlying KRAS-ligand interactions, providing valuable insights into understanding the KRAS-associated pathophysiology. These findings contribute to the translational aspect of our study, offering potential avenues for further research targeting KRAS membrane association with the potential to lead to a new class of RAS therapeutics.

Progress in Targeting KRAS Directly.

RAS research has entered the world of translational and clinical science. Progress has been based on our appreciation of the role of RAS mutations in different types of cancer and the effects of these mutations on the biochemical, structural, and biophysical properties of the RAS proteins themselves, particularly KRAS, on which most attention has been focused. This knowledge base, while still growing, has enabled creative chemical approaches to targeting KRAS directly. Our understanding of RAS signaling pathways in normal and cancer cells plays an important role for developing RAS inhibitors but also continues to reveal new approaches to targeting RAS through disruption of signaling complexes and downstream pathways.

A Top-Down Proteomic Assay to Evaluate KRAS4B-Compound Engagement.

Development of new targeted inhibitors for oncogenic KRAS mutants may benefit from insight into how a given mutation influences the accessibility of protein residues and how compounds interact with mutant or wild-type KRAS proteins. Targeted proteomic analysis, a key validation step in the KRAS inhibitor development process, typically involves both intact mass- and peptide-based methods to confirm compound localization or quantify binding. However, these methods may not always provide a clear picture of the compound binding affinity for KRAS, how specific the compound is to the target KRAS residue, and how experimental conditions may impact these factors. To address this, we have developed a novel top-down proteomic assay to evaluate in vitro KRAS4B-compound engagement while assessing relative quantitation in parallel. We present two applications to demonstrate the capabilities of our assay: maleimide-biotin labeling of a KRAS4BG12D cysteine mutant panel and treatment of three KRAS4B proteins (WT, G12C, and G13C) with small molecule compounds. Our results show the time- or concentration-dependence of KRAS4B-compound engagement in context of the intact protein molecule while directly mapping the compound binding site.

Structural dynamics of RAF1-HSP90-CDC37 and HSP90 complexes reveal asymmetric client interactions and key structural elements.

RAF kinases are integral to the RAS-MAPK signaling pathway, and proper RAF1 folding relies on its interaction with the chaperone HSP90 and the cochaperone CDC37. Understanding the intricate molecular interactions governing RAF1 folding is crucial for comprehending this process. Here, we present a cryo-EM structure of the closed-state RAF1-HSP90-CDC37 complex, where the C-lobe of the RAF1 kinase domain binds to one side of the HSP90 dimer, and an unfolded N-lobe segment of the RAF1 kinase domain threads through the center of the HSP90 dimer. CDC37 binds to the kinase C-lobe, mimicking the N-lobe with its HxNI motif. We also describe structures of HSP90 dimers without RAF1 and CDC37, displaying only N-terminal and middle domains, which we term the semi-open state. Employing 1 µs atomistic simulations, energetic decomposition, and comparative structural analysis, we elucidate the dynamics and interactions within these complexes. Our quantitative analysis reveals that CDC37 bridges the HSP90-RAF1 interaction, RAF1 binds HSP90 asymmetrically, and that HSP90 structural elements engage RAF1's unfolded region. Additionally, N- and C-terminal interactions stabilize HSP90 dimers, and molecular interactions in HSP90 dimers rearrange between the closed and semi-open states. Our findings provide valuable insight into the contributions of HSP90 and CDC37 in mediating client folding.

Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Comparative analysis of KRAS4a and KRAS4b splice variants reveals distinctive structural and functional properties.

KRAS, the most frequently mutated oncogene in human cancer, produces two isoforms, KRAS4a and KRAS4b, through alternative splicing. These isoforms differ in exon 4, which encodes the final 15 residues of the G-domain and hypervariable regions (HVRs), vital for trafficking and membrane localization. While KRAS4b has been extensively studied, KRAS4a has been largely overlooked. Our multidisciplinary study compared the structural and functional characteristics of KRAS4a and KRAS4b, revealing distinct structural properties and thermal stability. Position 151 influences KRAS4a's thermal stability, while position 153 affects binding to RAF1 CRD protein. Nuclear magnetic resonance analysis identified localized structural differences near sequence variations and provided a solution-state conformational ensemble. Notably, KRAS4a exhibits substantial transcript abundance in bile ducts, liver, and stomach, with transcript levels approaching KRAS4b in the colon and rectum. Functional disparities were observed in full-length KRAS variants, highlighting the impact of HVR variations on interaction with trafficking proteins and downstream effectors like RAF and PI3K within cells.

Revealing the mechanism of action of a first-in-class covalent inhibitor of KRASG12C (ON) and other functional properties of oncogenic KRAS by 31P NMR.

Individual oncogenic KRAS mutants confer distinct differences in biochemical properties and signaling for reasons that are not well understood. KRAS activity is closely coupled to protein dynamics and is regulated through two interconverting conformations: state 1 (inactive, effector binding deficient) and state 2 (active, effector binding enabled). Here, we use 31P NMR to delineate the differences in state 1 and state 2 populations present in WT and common KRAS oncogenic mutants (G12C, G12D, G12V, G13D, and Q61L) bound to its natural substrate GTP or a commonly used nonhydrolyzable analog GppNHp (guanosine-5'-[(ß,γ)-imido] triphosphate). Our results show that GppNHp-bound proteins exhibit significant state 1 population, whereas GTP-bound KRAS is primarily (90% or more) in state 2 conformation. This observation suggests that the predominance of state 1 shown here and in other studies is related to GppNHp and is most likely nonexistent in cells. We characterize the impact of this differential conformational equilibrium of oncogenic KRAS on RAF1 kinase effector RAS-binding domain and intrinsic hydrolysis. Through a KRAS G12C drug discovery, we have identified a novel small-molecule inhibitor, BBO-8956, which is effective against both GDP- and GTP-bound KRAS G12C. We show that binding of this inhibitor significantly perturbs state 1-state 2 equilibrium and induces an inactive state 1 conformation in GTP-bound KRAS G12C. In the presence of BBO-8956, RAF1-RAS-binding domain is unable to induce a signaling competent state 2 conformation within the ternary complex, demonstrating the mechanism of action for this novel and active-conformation inhibitor.

Reduced dynamic complexity allows structure elucidation of an excited state of KRASG13D.

Localized dynamics of RAS, including regions distal to the nucleotide-binding site, is of high interest for elucidating the mechanisms by which RAS proteins interact with effectors and regulators and for designing inhibitors. Among several oncogenic mutants, methyl relaxation dispersion experiments reveal highly synchronized conformational dynamics in the active (GMPPNP-bound) KRASG13D, which suggests an exchange between two conformational states in solution. Methyl and 31P NMR spectra of active KRASG13D in solution confirm a two-state ensemble interconverting on the millisecond timescale, with a major Pγ atom peak corresponding to the dominant State 1 conformation and a secondary peak indicating an intermediate state different from the known State 2 conformation recognized by RAS effectors. High-resolution crystal structures of active KRASG13D and KRASG13D-RAF1 RBD complex provide snapshots of the State 1 and 2 conformations, respectively. We use residual dipolar couplings to solve and cross-validate the structure of the intermediate state of active KRASG13D, showing a conformation distinct from those of States 1 and 2 outside the known flexible switch regions. The dynamic coupling between the conformational exchange in the effector lobe and the breathing motion in the allosteric lobe is further validated by a secondary mutation in the allosteric lobe, which affects the conformational population equilibrium.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

Destabilizing NF1 variants act in a dominant negative manner through neurofibromin dimerization.

The majority of pathogenic mutations in the neurofibromatosis type I (NF1) gene reduce total neurofibromin protein expression through premature truncation or microdeletion, but it is less well understood how loss-of-function missense variants drive NF1 disease. We have found that patient variants in codons 844 to 848, which correlate with a severe phenotype, cause protein instability and exert an additional dominant-negative action whereby wild-type neurofibromin also becomes destabilized through protein dimerization. We have used our neurofibromin cryogenic electron microscopy structure to predict and validate other patient variants that act through a similar mechanism. This provides a foundation for understanding genotype-phenotype correlations and has important implications for patient counseling, disease management, and therapeutics.

Structure of the SHOC2-MRAS-PP1C complex provides insights into RAF activation and Noonan syndrome.

SHOC2 acts as a strong synthetic lethal interactor with MEK inhibitors in multiple KRAS cancer cell lines. SHOC2 forms a heterotrimeric complex with MRAS and PP1C that is essential for regulating RAF and MAPK-pathway activation by dephosphorylating a specific phosphoserine on RAF kinases. Here we present the high-resolution crystal structure of the SHOC2-MRAS-PP1C (SMP) complex and apo-SHOC2. Our structures reveal that SHOC2, MRAS, and PP1C form a stable ternary complex in which all three proteins synergistically interact with each other. Our results show that dephosphorylation of RAF substrates by PP1C is enhanced upon interacting with SHOC2 and MRAS. The SMP complex forms only when MRAS is in an active state and is dependent on SHOC2 functioning as a scaffolding protein in the complex by bringing PP1C and MRAS together. Our results provide structural insights into the role of the SMP complex in RAF activation and how mutations found in Noonan syndrome enhance complex formation, and reveal new avenues for therapeutic interventions.

Exploring CRD mobility during RAS/RAF engagement at the membrane.

During the activation of mitogen-activated protein kinase (MAPK) signaling, the RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF bind to active RAS at the plasma membrane. The orientation of RAS at the membrane may be critical for formation of the RAS-RBDCRD complex and subsequent signaling. To explore how RAS membrane orientation relates to the protein dynamics within the RAS-RBDCRD complex, we perform multiscale coarse-grained and all-atom molecular dynamics (MD) simulations of KRAS4b bound to the RBD and CRD domains of RAF-1, both in solution and anchored to a model plasma membrane. Solution MD simulations describe dynamic KRAS4b-CRD conformations, suggesting that the CRD has sufficient flexibility in this environment to substantially change its binding interface with KRAS4b. In contrast, when the ternary complex is anchored to the membrane, the mobility of the CRD relative to KRAS4b is restricted, resulting in fewer distinct KRAS4b-CRD conformations. These simulations implicate membrane orientations of the ternary complex that are consistent with NMR measurements. While a crystal structure-like conformation is observed in both solution and membrane simulations, a particular intermolecular rearrangement of the ternary complex is observed only when it is anchored to the membrane. This configuration emerges when the CRD hydrophobic loops are inserted into the membrane and helices α3-5 of KRAS4b are solvent exposed. This membrane-specific configuration is stabilized by KRAS4b-CRD contacts that are not observed in the crystal structure. These results suggest modulatory interplay between the CRD and plasma membrane that correlate with RAS/RAF complex structure and dynamics, and potentially influence subsequent steps in the activation of MAPK signaling.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Insights into the Cross Talk between Effector and Allosteric Lobes of KRAS from Methyl Conformational Dynamics.

KRAS is the most frequently mutated RAS protein in cancer patients, and it is estimated that about 20% of the cancer patients in the United States carried mutant RAS proteins. To accelerate therapeutic development, structures and dynamics of RAS proteins had been extensively studied by various biophysical techniques for decades. Although 31P NMR studies revealed population equilibrium of the two major states in the active GMPPNP-bound form, more complex conformational dynamics in RAS proteins and oncogenic mutants subtly modulate the interactions with their downstream effectors. We established a set of customized NMR relaxation dispersion techniques to efficiently and systematically examine the ms-µs conformational dynamics of RAS proteins. This method allowed us to observe varying synchronized motions that connect the effector and allosteric lobes in KRAS. We demonstrated the role of conformational dynamics of KRAS in controlling its interaction with the Ras-binding domain of the downstream effector RAF1, the first kinase in the MAPK pathway. This allows one to explain, as well as to predict, the altered binding affinities of various KRAS mutants, which was neither previously reported nor apparent from the structural perspective.

Recapitulation of cell-like KRAS4b membrane dynamics on complex biomimetic membranes.

Understanding the spatiotemporal distribution and dynamics of RAS on the plasma membrane (PM) is the key for elucidating the molecular mechanisms of the RAS signaling pathway. Single particle tracking (SPT) experiments show that in cells, KRAS diffuses in at least three interchanging states on the cellular PM; however, KRAS remains monomeric and always shows homogeneous diffusion on artificial membranes. Here, we show for the first time on a supported lipid bilayer composed of heterogeneous lipid components that we can recapitulate the three-state diffusion of KRAS seen in cells. The use of a biologically relevant eight-lipid system opens a new frontier in the biophysical studies of RAS and other membrane associated proteins on a biomimetic system that recapitulates the complexity of a cellular PM.

RAS at 40: Update from the RAS Initiative.

SUMMARY: The RAS Initiative was launched in 2013 to address unmet clinical needs of patients with KRAS-driven cancers. The Initiative is based at Frederick National Laboratory for Cancer Research in Frederick, MD, and involves multiple collaborations with the RAS research community in academia and industry with the shared goal of developing RAS therapies.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques. We demonstrate here a multiscale simulation infrastructure that uses machine learning to create a scale-bridging ensemble of over 100,000 simulations of active wild-type KRAS on a complex, asymmetric membrane. Initialized and validated with experimental data (including a new structure of active wild-type KRAS), these simulations represent a substantial advance in the ability to characterize RAS-membrane biology. We report distinctive patterns of local lipid composition that correlate with interfacially promiscuous RAS multimerization. These lipid fingerprints are coupled to RAS dynamics, predicted to influence effector binding, and therefore may be a mechanism for regulating cell signaling cascades.