K128 ubiquitination constrains RAS activity by expanding its binding interface with GAP proteins.

The RAS pathway is among the most frequently activated signaling nodes in cancer. However, the mechanisms that alter RAS activity in human pathologies are not entirely understood. The most prevalent post-translational modification within the GTPase core domain of NRAS and KRAS is ubiquitination at lysine 128 (K128), which is significantly decreased in cancer samples compared to normal tissue. Here, we found that K128 ubiquitination creates an additional binding interface for RAS GTPase-activating proteins (GAPs), NF1 and RASA1, thus increasing RAS binding to GAP proteins and promoting GAP-mediated GTP hydrolysis. Stimulation of cultured cancer cells with growth factors or cytokines transiently induces K128 ubiquitination and restricts the extent of wild-type RAS activation in a GAP-dependent manner. In KRAS mutant cells, K128 ubiquitination limits tumor growth by restricting RAL/ TBK1 signaling and negatively regulating the autocrine circuit induced by mutant KRAS. Reduction of K128 ubiquitination activates both wild-type and mutant RAS signaling and elicits a senescence-associated secretory phenotype, promoting RAS-driven pancreatic tumorigenesis.

Allostery in disease and in drug discovery.

Allostery is largely associated with conformational and functional transitions in individual proteins. This concept can be extended to consider the impact of conformational perturbations on cellular function and disease states. Here, we clarify the concept of allostery and how it controls physiological activities. We focus on the challenging questions of how allostery can both cause disease and contribute to development of new therapeutics. We aim to increase the awareness of the linkage between disease symptoms on the cellular level and specific aberrant allosteric actions on the molecular level and to emphasize the potential of allosteric drugs in innovative therapies.

Direct K-Ras Inhibitors to Treat Cancers: Progress, New Insights, and Approaches to Treat Resistance.

Here we discuss approaches to K-Ras inhibition and drug resistance scenarios. A breakthrough offered a covalent drug against K-RasG12C. Subsequent innovations harnessed same-allele drug combinations, as well as cotargeting K-RasG12C with a companion drug to upstream regulators or downstream kinases. However, primary, adaptive, and acquired resistance inevitably emerge. The preexisting mutation load can explain how even exceedingly rare mutations with unobservable effects can promote drug resistance, seeding growth of insensitive cell clones, and proliferation. Statistics confirm the expectation that most resistance-related mutations are in cis, pointing to the high probability of cooperative, same-allele effects. In addition to targeted Ras inhibitors and drug combinations, bifunctional molecules and innovative tri-complex inhibitors to target Ras mutants are also under development. Since the identities and potential contributions of preexisting and evolving mutations are unknown, selecting a pharmacologic combination is taxing. Collectively, our broad review outlines considerations and provides new insights into pharmacology and resistance.

IKKα-deficient lung adenocarcinomas generate an immunosuppressive microenvironment by overproducing Treg-inducing cytokines.

The tumor microenvironment (TME) provides potential targets for cancer therapy. However, how signals originating in cancer cells affect tumor-directed immunity is largely unknown. Deletions in the CHUK locus, coding for IκB kinase α (IKKα), correlate with reduced lung adenocarcinoma (ADC) patient survival and promote KrasG12D-initiated ADC development in mice, but it is unknown how reduced IKKα expression affects the TME. Here, we report that low IKKα expression in human and mouse lung ADC cells correlates with increased monocyte-derived macrophage and regulatory T cell (Treg) scores and elevated transcription of genes coding for macrophage-recruiting and Treg-inducing cytokines (CSF1, CCL22, TNF, and IL-23A). By stimulating recruitment of monocyte-derived macrophages from the bone marrow and enforcing a TNF/TNFR2/c-Rel signaling cascade that stimulates Treg generation, these cytokines promote lung ADC progression. Depletion of TNFR2, c-Rel, or TNF in CD4+ T cells or monocyte-derived macrophages dampens Treg generation and lung tumorigenesis. Treg depletion also attenuates carcinogenesis. In conclusion, reduced cancer cell IKKα activity enhances formation of a protumorigenic TME through a pathway whose constituents may serve as therapeutic targets for KRAS-initiated lung ADC.

Are Parallel Proliferation Pathways Redundant?

Are the receptor tyrosine kinase (RTK) and JAK-STAT-driven proliferation pathways 'parallel' or 'redundant'? And what about those of K-Ras4B versus N-Ras? 'Parallel' proliferation pathways accomplish a similar drug resistance outcome. Thus, are they 'redundant'? In this paper, it is argued that there is a fundamental distinction between 'parallel' and 'redundant'. Cellular proliferation pathways are influenced by the genome sequence, 3D organization and chromatin accessibility, and determined by protein availability prior to cancer emergence. In the opinion presented, if they operate the same downstream protein families, they are redundant; if evolutionary-independent, they are parallel. Thus, RTK and JAK-STAT-driven proliferation pathways are parallel; those of Ras isoforms are redundant. Our Precision Medicine Call to map cancer proliferation pathways is vastly important since it can expedite effective therapeutics.

Molecular mechanism of regulation of RhoA GTPase by phosphorylation of RhoGDI.

Rho-specific guanine nucleotide dissociation inhibitors (RhoGDIs) play a crucial role in the regulation of Rho family GTPases. They act as negative regulators that prevent the activation of Rho GTPases by forming complexes with the inactive GDP-bound state of GTPase. Release of Rho GTPase from the RhoGDI-bound complex is necessary for Rho GTPase activation. Biochemical studies provide evidence of a "phosphorylation code," where phosphorylation of some specific residues of RhoGDI selectively releases its GTPase partner (RhoA, Rac1, Cdc42, etc.). This work attempts to understand the molecular mechanism behind this specific phosphorylation-induced reduction in binding affinity. Using several microseconds long atomistic molecular dynamics simulations of the wild-type and phosphorylated states of the RhoA-RhoGDI complex, we propose a molecular-interaction-based mechanistic model for the dissociation of the complex. Phosphorylation induces major structural changes, particularly in the positively charged polybasic region (PBR) of RhoA and the negatively charged N-terminal region of RhoGDI that contribute most to the binding affinity. Molecular mechanics Poisson-Boltzmann surface area binding energy calculations show a significant weakening of interaction on phosphorylation at the RhoA-specific site of RhoGDI. In contrast, phosphorylation at a Rac1-specific site does not affect the overall binding affinity significantly, which confirms the presence of a phosphorylation code. RhoA-specific phosphorylation leads to a reduction in the number of contacts between the PBR of RhoA and the N-terminal region of RhoGDI, which manifests a reduction of the binding affinity. Using hydrogen bond occupancy analysis and energetic perturbation network, we propose a mechanistic model for the allosteric response, i.e., long-range signal propagation from the site of phosphorylation to the PBR and buried geranylgeranyl group in the form of rearrangement and rewiring of hydrogen bonds and salt bridges. Our results highlight the crucial role of specific electrostatic interactions in manifestation of the phosphorylation code.

Single cell spatial biology over developmental time can decipher pediatric brain pathologies.

Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.

Allosteric Activation of RhoA Complexed with p115-RhoGEF Deciphered by Conformational Dynamics.

The Ras homologue family member A (RhoA) is a member of the Rho family, a subgroup of the Ras superfamily. RhoA interacts with the 115 kDa guanine nucleotide exchange factor (p115-RhoGEF), which assists in activation and binding with downstream effectors. Here, we use molecular dynamics (MD) simulations and essential dynamics analysis of the inactive RhoA-GDP and active RhoA-GTP, when bound to p115-RhoGEF to decipher the mechanism of RhoA activation at the structural level. We observe that inactive RhoA-GDP maintains its position near the catalytic site on the Dbl homology (DH) domain of p115-RhoGEF through the interaction of its Switch I region with the DH domain. We further show that the active RhoA-GTP is engaged in more interactions with the p115-RhoGEF membrane-bound Pleckstrin homology (PH) domain as compared to RhoA-GDP. We hypothesize that the role of the interactions between the active RhoA-GTP and the PH domain is to help release it from the DH domain upon activation. Our results support this premise, and our simulations uncover the beginning of this process and provide structural details. They also point to allosteric communication pathways that take part in RhoA activation to promote and strengthen the interaction between the active RhoA-GTP and the PH domain. Allosteric regulation also occurs among other members of the Rho superfamily. Collectively, we suggest that in the activation process, the role of the RhoA-GTP interaction with the PH domain is to release RhoA-GTP from the DH domain after activation, making it available to downstream effectors.

SHP2 clinical phenotype, cancer, or RASopathies, can be predicted by mutant conformational propensities.

SHP2 phosphatase promotes full activation of the RTK-dependent Ras/MAPK pathway. Its mutations can drive cancer and RASopathies, a group of neurodevelopmental disorders (NDDs). Here we ask how same residue mutations in SHP2 can lead to both cancer and NDD phenotypes, and whether we can predict what the outcome will be. We collected and analyzed mutation data from the literature and cancer databases and performed molecular dynamics simulations of SHP2 mutants. We show that both cancer and Noonan syndrome (NS, a RASopathy) mutations favor catalysis-prone conformations. As to cancer versus RASopathies, we demonstrate that cancer mutations are more likely to accelerate SHP2 activation than the NS mutations at the same genomic loci, in line with NMR data for K-Ras4B more aggressive mutations. The compiled experimental data and dynamic features of SHP2 mutants lead us to propose that different from strong oncogenic mutations, SHP2 activation by NS mutations is less likely to induce a transition of the ensemble from the SHP2 inactive state to the active state. Strong signaling promotes cell proliferation, a hallmark of cancer. Weak, or moderate signals are associated with differentiation. In embryonic neural cells, dysregulated differentiation is connected to NDDs. Our innovative work offers structural guidelines for identifying and correlating mutations with clinical outcomes, and an explanation for why bearers of RASopathy mutations may have a higher probability of cancer. Finally, we propose a drug strategy against SHP2 variants-promoting cancer and RASopathies.

HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry.

SUMMARY: HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins. AVAILABILITY AND IMPLEMENTATION: HMI-PRED 2.0 provides user-friendly graphic interfaces for predicting, visualizing and analyzing host-microbe interactions. HMI-PRED 2.0 is available at https://hmipred.org/.

SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions.

SUMMARY: We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. AVAILABILITY AND IMPLEMENTATION: The server is available at https://interactome.ku.edu.tr/sars/.

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis.

Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aß, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.

The mechanism of activation of MEK1 by B-Raf and KSR1.

MEK1 interactions with B-Raf and KSR1 are key steps in Ras/Raf/MEK/ERK signaling. Despite this, vital mechanistic details of how these execute signal transduction are still enigmatic. Among these is why, despite B-Raf and KSR1 kinase domains similarity, the B-Raf/MEK1 and KSR1/MEK1 complexes have distinct contributions to MEK1 activation, and broadly, what is KSR1's role. Our molecular dynamics simulations clarify these still unresolved ambiguities. Our results reveal that the proline-rich (P-rich) loop of MEK1 plays a decisive role in MEK1 activation loop (A-loop) phosphorylation. In the inactive B-Raf/MEK1 heterodimer, the collapsed A-loop of B-Raf interacts with the P-rich loop and A-loop of MEK1, minimizing MEK1 A-loop fluctuation and preventing it from phosphorylation. In the active B-Raf/MEK1 heterodimer, the P-rich loop moves in concert with the A-loop of B-Raf as it extends. This reduces the number of residues interacting with MEK1 A-loop, allowing increased A-loop fluctuation, and bringing Ser222 closer to ATP for phosphorylation. B-Raf αG-helix Arg662 promotes MEK1 activation by orienting Ser218 towards ATP. In KSR1/MEK1, the KSR1 αG-helix has Ala826 in place of B-Raf Arg662. This difference results in much fewer interactions between KSR1 αG-helix and MEK1 A-loop, thus a more flexible A-loop. We postulate that if KSR1 were to adopt an active configuration with an extended A-loop as seen in other protein kinases, then the MEK1 P-rich loop would extend in a similar manner, as seen in the active B-Raf/MEK1 heterodimer. This would result in highly flexible MEK1 A-loop, and KSR1 functioning as an active, B-Raf-like, kinase.

KRAS Activating Signaling Triggers Arteriovenous Malformations.

The underlying genetic causes and altered signaling pathways of brain arteriovenous malformations remain unknown. A study published in The New England Journal of Medicine reported that KRAS somatic mutations (p.Gly12Val/Asp) were identified in brain arteriovenous malformations of human subjects and endothelial cell-enriched cultures, which might specifically activate the MAPK (mitogen-activated protein kinase)-ERK (extracellular signal-regulated kinase) signaling pathway in brain endothelial cells.

The structural basis of BCR-ABL recruitment of GRB2 in chronic myelogenous leukemia.

BCR-ABL drives chronic myeloid leukemia (CML). BCR binding to GRB2 transduces signaling via the Ras/MAPK pathway. Despite considerable data confirming the binding, molecular-level understanding of exactly how the two proteins interact, and, especially, what are the determinants of the specificity of the SH2GRB2 domain-phosphorylated BCR (pBCR) recognition are still open questions. Yet, this is vastly important for understanding binding selectivity, and for predicting the phosphorylated receptors, or peptides, that are likely to bind. Here, we uncover these determinants and ascertain to what extent they relate to the affinity of the interaction. Toward this end, we modeled the complexes of the pBCR and SH2GRB2 and other pY/Y-peptide-SH2 complexes and compared their specificity and affinity. We observed that pBCR's 176FpYVNV180 motif is favorable and specific to SH2GRB2, similar to pEGFR, but not other complexes. SH2GRB2 contains two binding pockets: pY-binding recognition pocket triggers binding, and the specificity pocket whose interaction is governed by N179 in pBCR and W121 in SH2GRB2. Our proposed motif with optimal affinity to SH2GRB2 is E/D-pY-E/V-N-I/L. Collectively, we provide the structural basis of BCR-ABL recruitment of GRB2, outline its specificity hallmarks, and delineate a blueprint for prediction of BCR-binding scaffolds and for therapeutic peptide design.

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.

A network-based deep learning methodology for stratification of tumor mutations.

MOTIVATION: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. RESULTS: We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients' somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein-protein interactome with a network embedding approach and constructed the patients' vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients' vectors. The AUC value is around 0.89 in the prediction of the patient's cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients' features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients' vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Markov State Models and Molecular Dynamics Simulations Reveal the Conformational Transition of the Intrinsically Disordered Hypervariable Region of K-Ras4B to the Ordered Conformation.

K-Ras4B, the most frequently mutated Ras isoform in human tumors, plays a vital part in cell growth, differentiation, and survival. Its tail, the C-terminal hypervariable region (HVR), is involved in anchoring K-Ras4B at the cellular plasma membrane and in isoform-specific protein-protein interactions and signaling. In the inactive guanosine diphosphate-bound state, the intrinsically disordered HVR interacts with the catalytic domain at the effector-binding region, rendering K-Ras4B in its autoinhibited state. Activation releases the HVR from the catalytic domain, with its ensemble favoring an ordered α-helical structure. The large-scale conformational transition of the HVR from the intrinsically disordered to the ordered conformation remains poorly understood. Here, we deploy a computational scheme that integrates a transition path-generation algorithm, extensive molecular dynamics simulation, and Markov state model analysis to investigate the conformational landscape of the HVR transition pathway. Our findings reveal a stepwise pathway for the HVR transition and uncover several key conformational substates along the transition pathway. Importantly, key interactions between the HVR and the catalytic domain are unraveled, highlighting the pathogenesis of K-Ras4B mild mutations in several congenital developmental anomaly syndromes. Together, these findings provide a deeper understanding of the HVR transition mechanism and the regulation of K-Ras4B activity at an atomic level.

Anticancer drug resistance: An update and perspective.

Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.

Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health.

Recent remarkable advances in genome sequencing have enabled detailed maps of identified and interpreted genomic variation, dubbed "mutanomes." The availability of thousands of exome/genome sequencing data has prompted the emergence of new challenges in the identification of novel druggable targets and therapeutic strategies. Typically, mutanomes are viewed as one- or two-dimensional. The three-dimensional protein structural view of personal mutanomes sheds light on the functional consequences of clinically actionable mutations revealed in tumor diagnosis and followed up in personalized treatments, in a mutanome-informed manner. In this review, we describe the protein structural landscape of personal mutanomes and provide expert opinions on rational strategies for more streamlined oncological drug discovery and molecularly targeted therapies for each individual and each tumor. We provide the structural mechanism of orthosteric versus allosteric drugs at the atom-level via targeting specific somatic alterations for combating drug resistance and the "undruggable" challenges in solid and hematologic neoplasias. We discuss computational biophysics strategies for innovative mutanome-informed cancer immunotherapies and combination immunotherapies. Finally, we highlight a personal mutanome infrastructure for the emerging development of personalized cancer medicine using a breast cancer case study.