The free energy landscape in translational science: how can somatic mutations result in constitutive oncogenic activation?

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Early Stages of Misfolding of PAP248-286 at two different pH values: An Insight from Molecular Dynamics Simulations

PAP248-286 peptides, which are highly abundant in human semen, aggregate and form amyloid fibrils that enhance HIV infection. Previous experimental studies have shown that the infection-promoting activity of PAP248-286 begins to increase well before amyloid formation takes place and that pH plays a key role in the enhancement of PAP248-286-related infection. Hence, understanding the early stages of misfolding of the PAP2482-86 peptide is crucial. To this end, we have performed 60 independent MD simulations for a total of 24 µs at two different pH values (4.2 and 7.2). Our data shows that early stages of misfolding of the PAP248-286 peptide is a multistage process and that the first step of the process is a transition from an “I-shaped” structure to a “U-shaped” structure. We further observed that the structure of PAP248-286 at the two different pH values shows significantly different features. At pH 4.2, the peptide has less intra-molecular H-bonds and a reduced α-helical content than at pH 7.2. Moreover, differences in intra-peptide residues contacts are also observed at the two pH values. Finally, free energy landscape analysis shows that there are more local minima in the energy surface of the peptide at pH 7.2 than at pH 4.2. Overall, the present study elucidates the early stages of misfolding of the PAP248-286 peptide at the atomic level, thus possibly opening new avenues in structure-based drug discovery against HIV infection.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

Inhibition of Nonfunctional Ras

Intuitively, functional states should be targeted; not nonfunctional ones. So why could drugging the inactive K-Ras4BG12Cwork-but drugging the inactive kinase will likely not? The reason is the distinct oncogenic mechanisms. Kinase driver mutations work by stabilizing the active state and/or destabilizing the inactive state. Either way, oncogenic kinases are mostly in the active state. Ras driver mutations work by quelling its deactivation mechanisms, GTP hydrolysis, and nucleotide exchange. Covalent inhibitors that bind to the inactive GDP-bound K-Ras4BG12C conformation can thus work. By contrast, in kinases, allosteric inhibitors work by altering the active-site conformation to favor orthosteric drugs. From the translational standpoint this distinction is vital: it expedites effective pharmaceutical development and extends the drug classification based on the mechanism of action. Collectively, here we postulate that drug action relates to blocking the mechanism of activation, not to whether the protein is in the active or inactive state.

Conformational gating, dynamics and allostery in human monoacylglycerol lipase

Inhibition of human Monoacylglycerol Lipase (hMGL) offers a novel approach for treating neurological diseases. The design of inhibitors, targeting active-inactive conformational transitions of the enzyme, can be aided by understanding the interplay between structure and dynamics. Here, we report the effects of mutations within the catalytic triad on structure, conformational gating and dynamics of hMGL by combining kinetics, NMR, and HDX-MS data with metadynamics simulations. We found that point mutations alter delicate conformational equilibria between active and inactive states. HDX-MS reveals regions of the hMGL that become substantially more dynamic upon substitution of catalytic acid Asp-239 by alanine. These regions, located far from the catalytic triad, include not only loops but also rigid α-helixes and β-strands, suggesting their involvement in allosteric regulation as channels for long-range signal transmission. The results identify the existence of a preorganized global communication network comprising of tertiary (residue-residue contacts) and quaternary (rigid-body contacts) networks that mediate robust, rapid intraprotein signal transmission. Catalytic Asp-239 controls hMGL allosteric communications and may be considered as an essential residue for the integration and transmission of information to enzymes' remote regions, in addition to its well-known role to facilitate Ser-122 activation. Our findings may assist in the identification of new druggable sites in hMGL.

Understanding the Pyrimethamine Drug Resistance Mechanism via Combined Molecular Dynamics and Dynamic Residue Network Analysis

In this era of precision medicine, insights into the resistance mechanism of drugs are integral for the development of potent therapeutics. Here, we sought to understand the contribution of four point mutations (N51I, C59R, S108N, and I164L) within the active site of the malaria parasite enzyme dihydrofolate reductase (DHFR) towards the resistance of the antimalarial drug pyrimethamine. Homology modeling was used to obtain full-length models of wild type (WT) and mutant DHFR. Molecular docking was employed to dock pyrimethamine onto the generated structures. Subsequent all-atom molecular dynamics (MD) simulations and binding free-energy computations highlighted that pyrimethamine's stability and affinity inversely relates to the number of mutations within its binding site and, hence, resistance severity. Generally, mutations led to reduced binding affinity to pyrimethamine and increased conformational plasticity of DHFR. Next, dynamic residue network analysis (DRN) was applied to determine the impact of mutations and pyrimethamine binding on communication dispositions of DHFR residues. DRN revealed residues with distinctive communication profiles, distinguishing WT from drug-resistant mutants as well as pyrimethamine-bound from pyrimethamine-free models. Our results provide a new perspective on the understanding of mutation-induced drug resistance.

Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue-residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

Protein ensembles link genotype to phenotype

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

Exploring Gatekeeper Mutations in EGFR through Computer Simulations

The emergence of resistance against drugs that inhibit a particular protein is a major problem in targeted therapy. There is a clear need for rigorous methods to predict the likelihood of specific drug-resistance mutations arising in response to the binding of a drug. In this work we attempt to develop a robust computational protocol for predicting drug resistant mutations at the gatekeeper position (T790) in EGFR. We explore how mutations at this site affects interactions with ATP and three drugs that are currently used in clinics. We found, as expected, that certain mutations are not tolerated structurally, while some other mutations interfere with the natural substrate and hence are unlikely to be selected for. However, we found five possible mutations that are well tolerated structurally and energetically. Two of these mutations were predicted to have increased affinity for the drugs over ATP, as has been reported earlier. By reproducing the trends in the experimental binding affinities of the data, the methods chosen here are able to correctly predict the effects of these mutations on the binding affinities of the drugs. However, the increased affinity does not always translate into increased efficacy, because the efficacy is affected by several other factors such as binding kinetics, competition with ATP, and residence times. The computational methods used in the current study are able to reproduce or predict the effects of mutations on the binding affinities. However, a different set of methods is required to predict the kinetics of drug binding.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Discorhabdin N, a South African Natural Compound, for Hsp72 and Hsc70 Allosteric Modulation: Combined Study of Molecular Modeling and Dynamic Residue Network Analysis

The human heat shock proteins (Hsps), predominantly Hsp72 and Hsp90, have been strongly implicated in various critical stages of oncogenesis and progression of human cancers. While drug development has extensively focused on Hsp90 as a potential anticancer target, much less effort has been put against Hsp72. This work investigated the therapeutic potential of Hsp72 and its constitutive isoform, Hsc70, via in silico-based screening against the South African Natural Compounds Database (SANCDB). A comparative modeling approach was used to obtain nearly full-length 3D structures of the closed conformation of Hsp72 and Hsc70 proteins. Molecular docking of SANCDB compounds identified one potential allosteric modulator, Discorhabdin N, binding to the allosteric β substrate binding domain (SBDβ) back pocket, with good binding affinities in both cases. This allosteric region was identified in one of our previous studies. Subsequent all-atom molecular dynamics simulations and free energy calculations exhibited promising protein⁻ligand association characteristics, indicative of strong binding qualities. Further, we utilised dynamic residue network analysis (DRN) to highlight protein regions actively involved in cross-domain communication. Most residues identified agreed with known allosteric signal regulators from literature, and were further investigated for the purpose of deducing meaningful insights into the allosteric modulation properties of Discorhabdin N.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.

Autoinhibition in Ras effectors Raf, PI3Kα, and RASSF5: a comprehensive review underscoring the challenges in pharmacological intervention

Autoinhibition is an effective mechanism that guards proteins against spurious activation. Despite its ubiquity, the distinct organizations of the autoinhibited states and their release mechanisms differ. Signaling is most responsive to the cell environment only if a small shift in the equilibrium is required to switch the system from an inactive (occluded) to an active (exposed) state. Ras signaling follows this paradigm. This underscores the challenge in pharmacological intervention to exploit and enhance autoinhibited states. Here, we review autoinhibition and release mechanisms at the membrane focusing on three representative Ras effectors, Raf protein kinase, PI3Kα lipid kinase, and NORE1A (RASSF5) tumor suppressor, and point to the ramifications to drug discovery. We further touch on Ras upstream and downstream signaling, Ras activation, and the Ras superfamily in this light, altogether providing a broad outlook of the principles and complexities of autoinhibition.

Conformational landscape of the epidermal growth factor receptor kinase reveals a mutant specific allosteric pocket

An oncogenic mutant-specific druggable allosteric pocket captured by MD simulations.

Activating mutations within the epidermal growth factor receptor (EGFR) kinase domain give rise to several cancers including Non-Small Cell Lung Cancer (NSCLC). Small molecule inhibitors targeted at these mutants have proven to be clinically successful drugs. These molecules are ATP competitive and rapidly result in the emergence of resistance. Recently Jia et al. [Nature, 2016, 534, 129–132] reported a small molecule inhibitor (called EAI045) that binds at an allosteric pocket, does not compete with ATP and displays high potency and selectivity towards certain activating mutants (L858R, T790M, L858R/T790M) of EGFR, with IC₅₀ values ranging from 3 nM to 49 nM. We present here a study combining extensive molecular dynamics simulations with binding assays to provide a structural basis underlying the mechanism of binding of this molecule. It appears that in mutants, conformational destabilization of the short helix (that carries Leu858 in the wildtype), is key to the exposure of the allosteric pocket which otherwise is occluded by a set of sidechains including L858. We extend this hypothesis to show that a similar mechanism would enable the molecule to inhibit EGFR^L861Q which is another oncogenic mutant and validate this with binding experiments. The screening of the human structural kinome revealed at least 12 other oncogenic kinases which carry at least one activating mutant in this disorder-prone region and hence would be amenable to allosteric inhibition by molecules such as EAI045. Our study characterizes a druggable allosteric pocket which appears to be specific to certain oncogenic mutants of the EGFR and holds therapeutic potential.

Molecular Dynamics Simulations and Dynamic Network Analysis Reveal the Allosteric Unbinding of Monobody to H-Ras Triggered by R135K Mutation

Ras proteins, as small GTPases, mediate cell proliferation, survival and differentiation. Ras mutations have been associated with a broad spectrum of human cancers and thus targeting Ras represents a potential way forward for cancer therapy. A recently reported monobody NS1 allosterically disrupts the Ras-mediated signaling pathway, but its efficacy is reduced by R135K mutation in H-Ras. However, the detailed mechanism is unresolved. Here, using molecular dynamics (MD) simulations and dynamic network analysis, we explored the molecular mechanism for the unbinding of NS1 to H-Ras and shed light on the underlying allosteric network in H-Ras. MD simulations revealed that the overall structures of the two complexes did not change significantly, but the H-Ras–NS1 interface underwent significant conformational alteration in the mutant Binding free energy analysis showed that NS1 binding was unfavored after R135K mutation, which resulted in the unfavorable binding of NS1. Furthermore, the critical residues on H-Ras responsible for the loss of binding of NS1 were identified. Importantly, the allosteric networks for these important residues were revealed, which yielded a novel insight into the allosteric regulatory mechanism of H-Ras.

Site-to-site interdomain communication may mediate different loss-of-function mechanisms in a cancer-associated NQO1 polymorphism

Disease associated genetic variations often cause intracellular enzyme inactivation, dysregulation and instability. However, allosteric communication of mutational effects to distant functional sites leading to loss-of-function remains poorly understood. We characterize here interdomain site-to-site communication by which a common cancer-associated single nucleotide polymorphism (c.C609T/p.P187S) reduces the activity and stability in vivo of NAD(P)H:quinone oxidoreductase 1 (NQO1). NQO1 is a FAD-dependent, two-domain multifunctional stress protein acting as a Phase II enzyme, activating cancer pro-drugs and stabilizing p53 and p73α oncosuppressors. We show that p.P187S causes structural and dynamic changes communicated to functional sites far from the mutated site, affecting the FAD binding site located at the N-terminal domain (NTD) and accelerating proteasomal degradation through dynamic effects on the C-terminal domain (CTD). Structural protein:protein interaction studies reveal that the cancer-associated polymorphism does not abolish the interaction with p73α, indicating that oncosuppressor destabilization largely mirrors the low intracellular stability of p.P187S. In conclusion, we show how a single disease associated amino acid change may allosterically perturb several functional sites in an oligomeric and multidomain protein. These results have important implications for the understanding of loss-of-function genetic diseases and the identification of novel structural hot spots as targets for pharmacological intervention.

Localized structural frustration for evaluating the impact of sequence variants

Population-scale sequencing is increasingly uncovering large numbers of rare single-nucleotide variants (SNVs) in coding regions of the genome. The rarity of these variants makes it challenging to evaluate their deleteriousness with conventional phenotype-genotype associations. Protein structures provide a way of addressing this challenge. Previous efforts have focused on globally quantifying the impact of SNVs on protein stability. However, local perturbations may severely impact protein functionality without strongly disrupting global stability (e.g. in relation to catalysis or allostery). Here, we describe a workflow in which localized frustration, quantifying unfavorable local interactions, is employed as a metric to investigate such effects. Using this workflow on the Protein Databank, we find that frustration produces many immediately intuitive results: for instance, disease-related SNVs create stronger changes in localized frustration than non-disease related variants, and rare SNVs tend to disrupt local interactions to a larger extent than common variants. Less obviously, we observe that somatic SNVs associated with oncogenes and tumor suppressor genes (TSGs) induce very different changes in frustration. In particular, those associated with TSGs change the frustration more in the core than the surface (by introducing loss-of-function events), whereas those associated with oncogenes manifest the opposite pattern, creating gain-of-function events.

Oncogenic Mutations Differentially Affect Bax Monomer, Dimer, and Oligomeric Pore Formation in the Membrane

Dysfunction of Bax, a pro-apoptotic regulator of cellular metabolism is implicated in neurodegenerative diseases and cancer. We have constructed the first atomistic models of the Bax oligomeric pore consisting with experimental residue-residue distances. The models are stable, capturing well double electron-electron resonance (DEER) spectroscopy measurements and provide structural details in line with the DEER data. Comparison with the latest experimental results revealed that our models agree well with both Bax and Bak pores, pointed to a converged structural arrangement for Bax and Bak pore formation. Using multi-scale molecular dynamics simulations, we probed mutational effects on Bax transformation from monomer → dimer → membrane pore formation at atomic resolution. We observe that two cancer-related mutations, G40E and S118I, allosterically destabilize the monomer and stabilize an off-pathway swapped dimer, preventing productive pore formation. This observation suggests a mechanism whereby the mutations may work mainly by over-stabilizing the monomer → dimer transformation toward an unproductive off-pathway swapped-dimer state. Our observations point to misfolded Bax states, shedding light on the molecular mechanism of Bax mutation-elicited cancer. Most importantly, the structure of the Bax pore facilitates future study of releases cytochrome C in atomic detail.

Advances in NMR Methods To Map Allosteric Sites: From Models to Translation

The last five years have witnessed major developments in the understanding of the allosteric phenomenon, broadly defined as coupling between remote molecular sites. Such advances have been driven not only by new theoretical models and pharmacological applications of allostery, but also by progress in the experimental approaches designed to map allosteric sites and transitions. Among these techniques, NMR spectroscopy has played a major role given its unique near-atomic resolution and sensitivity to the dynamics that underlie allosteric couplings. Here, we highlight recent progress in the NMR methods tailored to investigate allostery with the goal of offering an overview of which NMR approaches are best suited for which allosterically relevant questions. The picture of the allosteric "NMR toolbox" is provided starting from one of the simplest models of allostery (i.e., the four-state thermodynamic cycle) and continuing to more complex multistate mechanisms. We also review how such an "NMR toolbox" has assisted the elucidation of the allosteric molecular basis for disease-related mutations and the discovery of novel leads for allosteric drugs. From this overview, it is clear that NMR plays a central role not only in experimentally validating transformative theories of allostery, but also in tapping the full translational potential of allosteric systems.

The Structural Basis of Oncogenic Mutations G12, G13 and Q61 in Small GTPase K-Ras4B

Ras mediates cell proliferation, survival and differentiation. Mutations in K-Ras4B are predominant at residues G12, G13 and Q61. Even though all impair GAP-assisted GTP → GDP hydrolysis, the mutation frequencies of K-Ras4B in human cancers vary. Here we aim to figure out their mechanisms and differential oncogenicity. In total, we performed 6.4 μs molecular dynamics simulations on the wild-type K-Ras4B (K-Ras4B(WT)-GTP/GDP) catalytic domain, the K-Ras4B(WT)-GTP-GAP complex, and the mutants (K-Ras4B(G12C/G12D/G12V)-GTP/GDP, K-Ras4B(G13D)-GTP/GDP, K-Ras4B(Q61H)-GTP/GDP) and their complexes with GAP. In addition, we simulated 'exchanged' nucleotide states. These comprehensive simulations reveal that in solution K-Ras4B(WT)-GTP exists in two, active and inactive, conformations. Oncogenic mutations differentially elicit an inactive-to-active conformational transition in K-Ras4B-GTP; in K-Ras4B(G12C/G12D)-GDP they expose the bound nucleotide which facilitates the GDP-to-GTP exchange. These mechanisms may help elucidate the differential mutational statistics in K-Ras4B-driven cancers. Exchanged nucleotide simulations reveal that the conformational transition is more accessible in the GTP-to-GDP than in the GDP-to-GTP exchange. Importantly, GAP not only donates its R789 arginine finger, but stabilizes the catalytically-competent conformation and pre-organizes catalytic residue Q61; mutations disturb the R789/Q61 organization, impairing GAP-mediated GTP hydrolysis. Together, our simulations help provide a mechanistic explanation of key mutational events in one of the most oncogenic proteins in cancer.

Ras Conformational Ensembles, Allostery, and Signaling

Ras proteins are classical members of small GTPases that function as molecular switches by alternating between inactive GDP-bound and active GTP-bound states. Ras activation is regulated by guanine nucleotide exchange factors that catalyze the exchange of GDP by GTP, and inactivation is terminated by GTPase-activating proteins that accelerate the intrinsic GTP hydrolysis rate by orders of magnitude. In this review, we focus on data that have accumulated over the past few years pertaining to the conformational ensembles and the allosteric regulation of Ras proteins and their interpretation from our conformational landscape standpoint. The Ras ensemble embodies all states, including the ligand-bound conformations, the activated (or inactivated) allosteric modulated states, post-translationally modified states, mutational states, transition states, and nonfunctional states serving as a reservoir for emerging functions. The ensemble is shifted by distinct mutational events, cofactors, post-translational modifications, and different membrane compositions. A better understanding of Ras biology can contribute to therapeutic strategies.

GTP-Dependent K-Ras Dimerization

Ras proteins recruit and activate effectors, including Raf, that transmit receptor-initiated signals. Monomeric Ras can bind Raf; however, activation of Raf requires its dimerization. It has been suspected that dimeric Ras may promote dimerization and activation of Raf. Here, we show that the GTP-bound catalytic domain of K-Ras4B, a highly oncogenic splice variant of the K-Ras isoform, forms stable homodimers. We observe two major dimer interfaces. The first, highly populated β-sheet dimer interface is at the Switch I and effector binding regions, overlapping the binding surfaces of Raf, PI3K, RalGDS, and additional effectors. This interface has to be inhibitory to such effectors. The second, helical interface also overlaps the binding sites of some effectors. This interface may promote activation of Raf. Our data reveal how Ras self-association can regulate effector binding and activity, and suggest that disruption of the helical dimer interface by drugs may abate Raf signaling in cancer.

Mechanistic Insights into R776H Mediated Activation of Epidermal Growth Factor Receptor Kinase

The epidermal growth factor receptor (EGFR) kinase is activated by a variety of mutations in human cancers. R776H is one such recurrent mutation (R752H in another numbering system) in the αC-β4 loop of the tyrosine kinase domain that activates EGFR in the absence of the activating EGF ligand. However, the mechanistic details of how R776H contributes to kinase activation are not well understood. Here using cell-based cotransfection assays, we show that the R776H mutation activates EGFR in a dimerization-dependent manner by preferentially adopting the acceptor position in the asymmetric dimer. The acceptor function, but not the donor function, is enhanced for the R776H mutant, supporting the "superacceptor" hypothesis proposed for oncogenic mutations in EGFR. We also find that phosphorylation of monomeric EGFR is increased by R776H mutation, providing insights into EGFR lateral phosphorylation and oligomerization. On the basis of molecular modeling and molecular dynamics simulation, we propose a model in which loss of key autoinhibitory αC-helix capping interaction and alteration of coconserved cis regulatory interactions between the kinase domain and the flanking regulatory segments contribute to mutational activation. Since the R776 equivalent position is mutated in ErbB2 and ErbB4, our studies have implications for understanding kinase mutational activation in other ErbB family members as well.

Illuminating the molecular mechanisms of tyrosine kinase inhibitor resistance for the FGFR1 gatekeeper mutation: the Achilles' heel of targeted therapy

Human fibroblast growth factor receptors (FGFRs) 1-4 are a family of receptor tyrosine kinases that can serve as drivers of tumorigenesis. In particular, FGFR1 gene amplification has been implicated in squamous cell lung and breast cancers. Tyrosine kinase inhibitors (TKIs) targeting FGFR1, including AZD4547 and E3810 (Lucitanib), are currently in early phase clinical trials. Unfortunately, drug resistance limits the long-term success of TKIs, with mutations at the "gatekeeper" residue leading to tumor progression. Here we show the first structural and kinetic characterization of the FGFR1 gatekeeper mutation, V561M FGFR1. The V561M mutation confers a 38-fold increase in autophosphorylation achieved at least in part by a network of interacting residues forming a hydrophobic spine to stabilize the active conformation. Moreover, kinetic assays established that the V561M mutation confers significant resistance to E3810, while retaining affinity for AZD4547. Structural analyses of these TKIs with wild type (WT) and gatekeeper mutant forms of FGFR1 offer clues to developing inhibitors that maintain potency against gatekeeper mutations. We show that AZD4547 affinity is preserved by V561M FGFR1 due to a flexible linker that allows multiple inhibitor binding modes. This is the first example of a TKI binding in distinct conformations to WT and gatekeeper mutant forms of FGFR, highlighting adaptable regions in both the inhibitor and binding pocket crucial for drug design. Exploiting inhibitor flexibility to overcome drug resistance has been a successful strategy for combatting diseases such as AIDS and may be an important approach for designing inhibitors effective against kinase gatekeeper mutations.

'Latent drivers' expand the cancer mutational landscape

A major challenge facing the community involves identification of mutations that drive cancer. Analyses of cancer genomes to detect, and distinguish, 'driver' from 'passenger' mutations are daunting tasks. Here we suggest that there is a third 'latent driver' category. 'Latent driver' mutations behave as passengers, and do not confer a cancer hallmark. However, coupled with other emerging mutations, they drive cancer development and drug resistance. 'Latent drivers' emerge prior to and during cancer evolution. These allosteric mutations can work through 'AND' all-or-none or incremental 'Graded' logic gate mechanisms. Current diagnostic platforms generally assume that actionable 'driver' mutations are those appearing most frequently in cancer. We propose that 'latent driver' detection may help forecast cancer progression and modify personalized drug regimes.

Structure-based network analysis of activation mechanisms in the ErbB family of receptor tyrosine kinases: the regulatory spine residues are global mediators of structural stability and allosteric interactions

The ErbB protein tyrosine kinases are among the most important cell signaling families and mutation-induced modulation of their activity is associated with diverse functions in biological networks and human disease. We have combined molecular dynamics simulations of the ErbB kinases with the protein structure network modeling to characterize the reorganization of the residue interaction networks during conformational equilibrium changes in the normal and oncogenic forms. Structural stability and network analyses have identified local communities integrated around high centrality sites that correspond to the regulatory spine residues. This analysis has provided a quantitative insight to the mechanism of mutation-induced “superacceptor” activity in oncogenic EGFR dimers. We have found that kinase activation may be determined by allosteric interactions between modules of structurally stable residues that synchronize the dynamics in the nucleotide binding site and the αC-helix with the collective motions of the integrating αF-helix and the substrate binding site. The results of this study have pointed to a central role of the conserved His-Arg-Asp (HRD) motif in the catalytic loop and the Asp-Phe-Gly (DFG) motif as key mediators of structural stability and allosteric communications in the ErbB kinases. We have determined that residues that are indispensable for kinase regulation and catalysis often corresponded to the high centrality nodes within the protein structure network and could be distinguished by their unique network signatures. The optimal communication pathways are also controlled by these nodes and may ensure efficient allosteric signaling in the functional kinase state. Structure-based network analysis has quantified subtle effects of ATP binding on conformational dynamics and stability of the EGFR structures. Consistent with the NMR studies, we have found that nucleotide-induced modulation of the residue interaction networks is not limited to the ATP site, and may enhance allosteric cooperativity with the substrate binding region by increasing communication capabilities of mediating residues.

The structural basis for cancer treatment decisions

Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.