Conformational Selection Mechanism Provides Structural Insights into the Optimization of APC-Asef Inhibitors.

Metastasis is the major cause of death in colorectal cancer and it has been proven that inhibiting an interaction between adenomatous polyposis coli (APC) and Rho guanine nucleotide exchange factor 4 (Asef) efficaciously restrain metastasis. However, current inhibitors cannot achieve a satisfying effect in vivo and need to be optimized. In the present study, we applied molecular dynamics (MD) simulations and extensive analyses to apo and holo APC systems in order to reveal the inhibitor mechanism in detail and provide insights into optimization. MD simulations suggested that apo APC takes on a broad array of conformations and inhibitors stabilize conformation selectively. Representative structures in trajectories show specific APC-ligand interactions, explaining the different binding process. The stability and dynamic properties of systems elucidate the inherent factors of the conformation selection mechanism. Binding free energy analysis quantitatively confirms key interface residues and guide optimization. This study elucidates the conformation selection mechanism in APC-Asef inhibition and provides insights into peptide-based drug design.

Using machine learning models to identify the risk of depression in middle-aged and older adults with frequent and infrequent nicotine use: A cross-sectional study.

BACKGROUND: Depression is very prevalent in middle-aged and older smokers. Therefore, we aimed to identify the risk of depression among middle-aged and older adults with frequent and infrequent nicotine use, as this is quite necessary for supporting their well-being. METHODS: This study included a total of 10,821 participants, which were derived from the China Health and Retirement Longitudinal Study Wave 5, 2020 (CHARLS-5). Five machine learning (ML) algorithms were employed. Some metrics were used to evaluate the performance of models, including area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), specificity, accuracy. RESULTS: 10,821 participants (6472 males, 4349 females) had a mean age of 60.47 ± 8.98, with a score of 8.90 ± 6.53 on depression scale. For middle-aged and older adults with frequent nicotine use, random forest (RF) achieved the highest AUC value, PPV and specificity (0.75, 0.74 and 0.88, respectively). For the other group, support vector machines (SVM) showed the highest PPV (0.74), and relatively high accuracy and specificity (0.72 and 0.87, respectively). Feature importance analysis indicated that "dissatisfaction with life" was the most important variable of identifying the risk of depression in the SVM model, while "attitude towards expected life span" was the most important one in the RF model. LIMITATIONS: CHARLS-5 was collected during the COVID-19, so our results may be influenced by the pandemic. CONCLUSIONS: This study indicated that certain ML models can ideally identify the risk of depression in middle-aged and older adults, which holds significant value for their health management.

Genomic and transcriptomic profiling of peripheral T cell lymphoma reveals distinct molecular and microenvironment subtypes.

Peripheral T cell lymphoma (PTCL) is a heterogeneous group of non-Hodgkin's lymphomas varying in clinical, phenotypic, and genetic features. The molecular pathogenesis and the role of the tumor microenvironment in PTCL are poorly understood, with limited biomarkers available for genetic subtyping and targeted therapies. Through an integrated genomic and transcriptomic study of 221 PTCL patients, we delineate the genetic landscape of PTCL, enabling molecular and microenvironment classification. According to the mutational status of RHOA, TET2, histone-modifying, and immune-related genes, PTCL is divided into 4 molecular subtypes with discrete patterns of gene expression, biological aberrations, and vulnerabilities to targeted agents. We also perform an unsupervised clustering on the microenvironment transcriptional signatures and categorize PTCL into 4 lymphoma microenvironment subtypes based on characteristic activation of oncogenic pathways and composition of immune communities. Our findings highlight the potential clinical rationale of future precision medicine strategies that target both molecular and microenvironment alterations in PTCL.

Cardiovascular toxicity of tyrosine kinase inhibitors during cancer treatment: Potential involvement of TRPM7.

Receptor tyrosine kinases (RTKs) are a class of membrane spanning cell-surface receptors that transmit extracellular signals through the membrane to trigger diverse intracellular signaling through tyrosine kinases (TKs), and play important role in cancer development. Therapeutic approaches targeting RTKs such as vascular endothelial growth factor receptor (VEGFR), epidermal growth factor receptor (EGFR), and platelet-derived growth factor receptor (PDGFR), and TKs, such as c-Src, ABL, JAK, are widely used to treat human cancers. Despite favorable benefits in cancer treatment that prolong survival, these tyrosine kinase inhibitors (TKIs) and monoclonal antibodies targeting RTKs are also accompanied by adverse effects, including cardiovascular toxicity. Mechanisms underlying TKI-induced cardiovascular toxicity remain unclear. The transient receptor potential melastatin-subfamily member 7 (TRPM7) is a ubiquitously expressed chanzyme consisting of a membrane-based ion channel and intracellular α-kinase. TRPM7 is a cation channel that regulates transmembrane Mg2+ and Ca2+ and is involved in a variety of (patho)physiological processes in the cardiovascular system, contributing to hypertension, cardiac fibrosis, inflammation, and atrial arrhythmias. Of importance, we and others demonstrated significant cross-talk between TRPM7, RTKs, and TK signaling in different cell types including vascular smooth muscle cells (VSMCs), which might be a link between TKIs and their cardiovascular effects. In this review, we summarize the implications of RTK inhibitors (RTKIs) and TKIs in cardiovascular toxicities during anti-cancer treatment, with a focus on the potential role of TRPM7/Mg2+ as a mediator of RTKI/TKI-induced cardiovascular toxicity. We also describe the important role of TRPM7 in cancer development and cardiovascular diseases, and the interaction between TRPM7 and RTKs, providing insights for possible mechanisms underlying cardiovascular disease in cancer patients treated with RTKI/TKIs.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells.

SIRT6 belongs to the conserved NAD+-dependent deacetylase superfamily and mediates multiple biological and pathological processes. Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics, which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases. Here, developing a reversed allosteric strategy AlloReverse, we identified a cryptic allosteric site, Pocket Z, which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD+. Based on Pocket Z, we discovered an SIRT6 allosteric inhibitor named JYQ-42. JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation, with an IC50 of 2.33 µmol/L. JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production. JYQ-42, to our knowledge, is the most potent and selective allosteric SIRT6 inhibitor. This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.

Untangling Dual-Targeting Therapeutic Mechanism of Epidermal Growth Factor Receptor (EGFR) Based on Reversed Allosteric Communication.

Dual-targeting therapeutics by coadministration of allosteric and orthosteric drugs is drawing increased attention as a revolutionary strategy for overcoming the drug-resistance problems. It was further observed that the occupation of orthosteric sites by therapeutics agents has the potential to enhance allosteric ligand binding, which leads to improved potency of allosteric drugs. Epidermal growth factor receptor (EGFR), as one of the most critical anti-cancer targets belonging to the receptor tyrosine kinase family, represents a quintessential example. It was revealed that osimertinib, an ATP-competitive covalent EGFR inhibitor, remarkably enhanced the affinity of a recently developed allosteric inhibitor JBJ-04-125-02 for EGFRL858R/T790M. Here, we utilized extensive large-scale molecular dynamics simulations and the reversed allosteric communication to untangle the detailed molecular underpinning, in which occupation of osimertinib at the orthosteric site altered the overall conformational ensemble of EGFR mutant and reshaped the allosteric site via long-distance signaling. A unique intermediate state resembling the active conformation was identified, which was further stabilized by osimertinib loading. Based on the allosteric communication pathway, we predicted a novel allosteric site positioned around K867, E868, H893, and K960 within the intermediate state. Its correlation with the orthosteric site was validated by both structural and energetic analysis, and its low sequence conservation indicated the potential for selective targeting across the human kinome. Together, these findings not only provided a mechanistic basis for future clinical application of the dual-targeting therapeutics, but also explored an innovative perception of allosteric inhibition of tyrosine kinase signaling.

Targeting RAS phosphorylation in cancer therapy: Mechanisms and modulators.

RAS, a member of the small GTPase family, functions as a binary switch by shifting between inactive GDP-loaded and active GTP-loaded state. RAS gain-of-function mutations are one of the leading causes in human oncogenesis, accounting for ∼19% of the global cancer burden. As a well-recognized target in malignancy, RAS has been intensively studied in the past decades. Despite the sustained efforts, many failures occurred in the earlier exploration and resulted in an 'undruggable' feature of RAS proteins. Phosphorylation at several residues has been recently determined as regulators for wild-type and mutated RAS proteins. Therefore, the development of RAS inhibitors directly targeting the RAS mutants or towards upstream regulatory kinases supplies a novel direction for tackling the anti-RAS difficulties. A better understanding of RAS phosphorylation can contribute to future therapeutic strategies. In this review, we comprehensively summarized the current advances in RAS phosphorylation and provided mechanistic insights into the signaling transduction of associated pathways. Importantly, the preclinical and clinical success in developing anti-RAS drugs targeting the upstream kinases and potential directions of harnessing allostery to target RAS phosphorylation sites were also discussed.

Mechanistic insights into the effect of phosphorylation on Ras conformational dynamics and its interactions with cell signaling proteins.

Ras undergoes interconversion between the active GTP-bound state and the inactive GDP-bound state. This GTPase cycle, which controls the activities of Ras, is accelerated by Ras GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (SOS). Oncogenic Ras mutations could affect the GTPase cycle and impair Ras functions. Additionally, Src-induced K-Ras Y32/64 dual phosphorylation has been reported to disrupt GTPase cycle and hinder Ras downstream signaling. However, the underlying mechanisms remain unclear. To address this, we performed molecular dynamics simulations (~30 µs in total) on unphosphorylated and phosphorylated K-Ras4B in GTP- and GDP-bound states, and on their complexes with GTPase cycle regulators (GAP and SOS) and the effector protein Raf. We found that K-Ras4B dual phosphorylation mainly alters the conformation at the nucleotide binding site and creates disorder at the catalytic site, resulting in the enlargement of GDP binding pocket and the retard of Ras-GTP intrinsic hydrolysis. We observed phosphorylation-induced shift in the distribution of Ras-GTP inactive-active sub-states and recognized potential druggable pockets in the phosphorylated Ras-GTP. Moreover, decreased catalytic competence or signal delivery abilities due to reduced binding affinities and/or distorted catalytic conformations of GAP, SOS and Raf were observed. In addition, the allosteric pathway from Ras/Raf interface to the distal Raf L4 loop was compromised by Ras phosphorylation. These results reveal the mechanisms by which phosphorylation influences the intrinsic or GAP/SOS catalyzed transformations between GTP- and GDP-bound states of Ras and its signal transduction to Raf. Our findings project Ras phosphorylation as a target for cancer drug discovery.

Emergence of allosteric drug-resistance mutations: new challenges for allosteric drug discovery.

Allosteric drugs have several significant advantages over traditional orthosteric drugs, encompassing higher selectivity and lower toxicity. Although allosteric drugs have potential advantages as therapeutic agents to treat human diseases, allosteric drug-resistance mutations still occur, rendering these drugs ineffective. Here, we review the emergence of allosteric drug-resistance mutations with an emphasis on examples covering clinically important therapeutic targets, including Breakpoint cluster region-Abelson tyrosine kinase (Bcr-Abl), Akt kinase [also called Protein Kinase B (PKB)], isocitrate dehydrogenase (IDH), MAPK/ERK kinase (MEK), and SRC homology 2 domain-containing phosphatase 2 (SHP2). We also discuss challenges associated with tackling allosteric drug resistance and the possible strategies to overcome this issue.

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs).

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

Combining Allosteric and Orthosteric Drugs to Overcome Drug Resistance.

Historically, most drugs target protein orthosteric sites. The gradual emergence of resistance hampers their therapeutic effectiveness, posing a challenge to drug development. Coadministration of allosteric and orthosteric drugs provides a revolutionary strategy to circumvent drug resistance, as drugs targeting the topologically distinct allosteric sites can restore or even enhance the efficacy of orthosteric drugs. Here, we comprehensively review the latest successful examples of such combination treatments against drug resistance, with a focus on their modes of action and the underlying structural mechanisms. Our work supplies an innovative insight into such promising methodology against the recalcitrant drug resistance conundrum and will be instructive for future clinical therapeutics.

Discovery of cryptic allosteric sites using reversed allosteric communication by a combined computational and experimental strategy.

Allostery, which is one of the most direct and efficient methods to fine-tune protein functions, has gained increasing recognition in drug discovery. However, there are several challenges associated with the identification of allosteric sites, which is the fundamental cornerstone of drug design. Previous studies on allosteric site predictions have focused on communication signals propagating from the allosteric sites to the orthosteric sites. However, recent biochemical studies have revealed that allosteric coupling is bidirectional and that orthosteric perturbations can modulate allosteric sites through reversed allosteric communication. Here, we proposed a new framework for the prediction of allosteric sites based on reversed allosteric communication using a combination of computational and experimental strategies (molecular dynamics simulations, Markov state models, and site-directed mutagenesis). The desirable performance of our approach was demonstrated by predicting the known allosteric site of the small molecule MDL-801 in nicotinamide dinucleotide (NAD+)-dependent protein lysine deacetylase sirtuin 6 (Sirt6). A potential novel cryptic allosteric site located around the L116, R119, and S120 residues within the dynamic ensemble of Sirt6 was identified. The allosteric effect of the predicted site was further quantified and validated using both computational and experimental approaches. This study proposed a state-of-the-art computational pipeline for detecting allosteric sites based on reversed allosteric communication. This method enabled the identification of a previously uncharacterized potential cryptic allosteric site on Sirt6, which provides a starting point for allosteric drug design that can aid the identification of candidate pockets in other therapeutic targets.