Small Molecule Drug Targets Research Articles

Insight of the molecular mechanism of inhibitors located at different allosteric sites regulating the activity of wild type and mutant KRAS (G12)

As the important hub of many cellular signaling networks, KRAS (Kirsten rat sarcoma viral oncogene homologue) has been identified as a tumor biomarker. It is the frequently mutated oncogene in human cancers, and KRAS protein activation caused by mutations, such as G12D, has been found in many human tumors tissues. Although, there are two specific allosteric sites (AS1 and AS2) on the KRAS protein that can be used as the targets for inhibitor development, the difference of regulatory mechanisms between two individual allosteric sites still not be reported. Here, using molecular dynamics simulations combined with molecular mechanics generalized born surface area (MM/GBSA) analysis, we found that both of the inhibitors, located at AS1 and AS2, were able to reduce the binding free energy between wild type, mutant KRAS (G12/D/V/S/C) and GTP remarkably, however the effect of inhibitors on the binding free energy between wild type, mutant KRAS and GDP was limited. In addition, the degree of decrease of binding free energy between KRAS and GTP caused by inhibitors at AS2 was significantly greater than that caused by inhibitors at AS1. Further analysis revealed that both inhibitors at AS1 and AS2 were able to regulate the fluctuation of Switch Ⅰ and Switch Ⅱ to expand the pocket of the orthosteric site (GTP binding site), thereby reducing the binding of KRAS to GTP. Noteworthy there was significant differences in the regulatory preferences on Switch Ⅰ and Switch Ⅱ between two type inhibitor. The inhibitor at AS2 mainly regulated Switch Ⅱ to affect the pocket of the orthosteric site, while the inhibitor at AS1 mainly expand the pocket of the orthosteric site by regulating the fluctuation of Switch Ⅰ. Our study compared the differences between two type inhibitors in regulating the KRAS protein activity and revealed the advantages of the AS2 as the small molecule drug target, aiming to provide theoretical guidance for the research of novel KRAS protein inhibitors.

A non-antibiotic erythromycin derivative improves muscle endurance by regulating endogenous anti-fatigue protein orosomucoid in mice.

At present, there are no official approved drugs for improving muscle endurance. Our previous research found acute phase protein orosomucoid (ORM) is an endogenous anti-fatigue protein, and macrolides antibiotics erythromycin can elevate ORM level to increase muscle bioenergetics and endurance parameters. Here, we further designed, synthesized and screened a new erythromycin derivative named HMS-01, which lost its antibacterial activity in vitro and in vivo. Data showed that HMS-01 could time- and dose-dependently prolong mice forced-swimming time and running time, and improve fatigue index in isolated soleus muscle. Moreover, HMS-01 treatment could increase the glycogen content, mitochondria number and function in liver and skeletal muscle, as well as ORM level in these tissues and sera. In Orm-deficient mice, the anti-fatigue and glycogen-elevation activity of HMS-01 disappeared. Therefore, HMS-01 might act as a promising small molecule drug targeting ORM to enhance muscle endurance.

Recent advances in protein precipitation-based methods for drug-target screening

Drug targets are biological macromolecules that bind drug molecules in vivo. Therefore, the system-wide identification of drug targets plays a vital role in fully understanding the mechanism of drug action, efficacy, and side effects. The unbiased screening of drug targets may accelerate the process of drug discovery and candidate screening. Mass spectrometry is a key tool for large-scale protein identification and accurate quantification owing to its high acquisition speed, resolution, and sensitivity. Mass spectrometry-based proteomics has been widely used for drug-target screening. It can systematically identify the protein-target landscape of a drug and elucidate drug-protein interactions. Commonly used drug-target characterization methods, such as labeling-based affinity enrichment, require the chemical derivatization of drug molecules, which is not only time-consuming but may also affect the affinity of the drug towards its targets. Furthermore, the spatial effects of the derivatization groups may block interactions between the drug and its targets. Considering the disadvantages of affinity-enrichment methods, strategies that do not require chemical derivatization have received widespread attention. Proteins may undergo denaturation, unfolding, and precipitation under different conditions such as high temperatures, extreme pH, denaturants, and mechanical stress. Binding to small-molecule drugs may alter the folding balance of target proteins. The conformational stability of target proteins can be stabilized by binding with drugs, and protein-drug complexes are more resistant than free proteins to the precipitation induced by different conditions. Based on this mechanism, various large-scale drug-target identification methods using protein precipitation have been developed by combining proteomics and mass spectrometry analysis, including thermal proteome profiling and solvent-, mechanical stress-, and pH-induced protein precipitation. These methods have been successfully applied to the characterization of small-molecule drug targets. In this review, we describe the protein precipitation-based methods used for the high-throughput discovery of drug targets and elucidation of the interactions between drugs and proteins in the past decade. We also summarize the characteristics of each method and discuss their application potential in drug-efficacy evaluation and drug discovery.

Antibodies expand the scope of angiotensin receptor pharmacology.

G-protein-coupled receptors (GPCRs) are key regulators of human physiology and are the targets of many small-molecule research compounds and therapeutic drugs. While most of these ligands bind to their target GPCR with high affinity, selectivity is often limited at the receptor, tissue and cellular levels. Antibodies have the potential to address these limitations but their properties as GPCR ligands remain poorly characterized. Here, using protein engineering, pharmacological assays and structural studies, we develop maternally selective heavy-chain-only antibody ('nanobody') antagonists against the angiotensin II type I receptor and uncover the unusual molecular basis of their receptor antagonism. We further show that our nanobodies can simultaneously bind to angiotensin II type I receptor with specific small-molecule antagonists and demonstrate that ligand selectivity can be readily tuned. Our work illustrates that antibody fragments can exhibit rich and evolvable pharmacology, attesting to their potential as next-generation GPCR modulators.

Designing combinational herbal drugs based on target space analysis.

Traditional oriental medicines (TOMs) are a medical practice that follows different philosophies to pharmaceutical drugs and they have been in use for many years in different parts of the world. In this study, by integrating TOM formula and pharmaceutical drugs, we performed target space analysis between TOM formula target space and small-molecule drug target space. To do so, we manually curated 46 TOM formulas that are known to treat Anxiety, Diabetes mellitus, Epilepsy, Hypertension, Obesity, and Schizophrenia. Then, we employed Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties such as human ether-a-go-go related gene (hERG) inhibition, Carcinogenicity, and AMES toxicity to filter out potentially toxic herbal ingredients. The target space analysis was performed between TOM formula and small-molecule drugs: (i) both are known to treat the same disease, and (ii) each known to treat different diseases. Statistical significance of the overlapped target space between the TOM formula and small-molecule drugs was measured using supportvalue. Support value distribution from randomly selected target space was calculated to validate the result. Furthermore, the Si-Wu-Tang (SWT) formula and published literature were also used to evaluate our results. This study tried to provide scientific evidence about the effectiveness of the TOM formula to treat the main indication with side effects that could come from the use of small-molecule drugs. The target space analysis between TOM formula and small-molecule drugs in which both are known to treat the same disease shows that many targets overlapped between the two medications with a support value of 0.84 and weighted average support of 0.72 for a TOM formula known to treat Epilepsy. Furthermore, support value distribution from randomly selected target spacesin this analysis showed that the number of overlapped targets is much higher between TOM formula and small-molecule drugs that are known to treat the same disease than in randomly selected target spaces. Moreover, scientific literature was also used to evaluate the medicinal efficacy of individual herbs. This study provides an evidence to the effectiveness of a TOM formula to treat the main indication as well asside effects associated with the use of pharmaceutical drugs, as demonstrated through target space analysis.

Heterogeneous graph inference with range constrainted [formula omitted]-collaborative matrix factorization for small molecule-miRNA association prediction

MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed. However, most existing methods do not adequately address the inherent sparsity in SM-miRNA association networks, and imprecise SM/miRNA similarity metrics reduce the accuracy of predicting SM-miRNA associations. In this research, we proposed a heterogeneous graph inference with range constrained L2,1-collaborative matrix factorization (HGIRCLMF) method to predict potential SM-miRNA associations. First, we computed the multi-source similarities of SM/miRNA and integrated these similarity information into a comprehensive SM/miRNA similarity. This step improved the accuracy of SM and miRNA similarity, ensuring reliability for the subsequent inference of the heterogeneity map. Second, we used a range constrained L2,1-collaborative matrix factorization (RCLMF) model to pre-populate the SM-miRNA association matrix with missing values. In this step, we developed a novel matrix decomposition method that enhances the robustness and formative nature of SM-miRNA edges between SM networks and miRNA networks. Next, we built a well-established SM-miRNA heterogeneous network utilizing the processed biological information. Finally, HGIRCLMF used this network data to infer unknown association pair scores. We implemented four cross-validation experiments on two distinct datasets, and HGIRCLMF acquired the highest areas under the curve, surpassing six state-of-the-art computational approaches. Furthermore, we performed three case studies to validate the predictive power of our method in practical application.

SPAUTIN-1 alleviates LPS-induced acute lung injury by inhibiting NF-κB pathway in neutrophils

BackgroundAcute lung injury (ALI) is an inflammatory condition characterized by acute damage to lung tissue. SPAUTIN-1, recognized as a small molecule drug targeting autophagy and USP10/13, has been reported for its potential to inhibit oxidative stress damage in various tissue injuries. However, the role and mechanism of SPAUTIN-1 in ALI remain unclear. This study aims to elucidate the protective effects of SPAUTIN-1 on ALI, with a particular focus on its role and mechanism in pulmonary inflammatory responses. MethodsLipopolysaccharides (LPS) were employed to induce inflammation-mediated ALI. Bleomycin was used to induce non-inflammation-mediated ALI. The mechanism of SPAUTIN-1 action was identified through RNA-Sequencing and subsequently validated in mouse primary cells. Tert-butyl hydroperoxide (TBHP) was utilized to create an in vitro model of lung epithelial cell oxidative stress with MLE-12 cells. ResultsSPAUTIN-1 significantly mitigated LPS-induced lung injury and inflammatory responses, attenuated necroptosis and apoptosis in lung epithelial cells, and inhibited autophagy in leukocytes and epithelial cells. However, SPAUTIN-1 exhibited no significant effect on bleomycin-induced lung injury. RNA-sequencing results demonstrated that SPAUTIN-1 significantly inhibited the NF-κB signaling pathway in leukocytes, a finding consistently confirmed by mouse primary cell assays. In vitro experiments further revealed that SPAUTIN-1 effectively mitigated oxidative stress injury in MLE-12 cells induced by TBHP. ConclusionSPAUTIN-1 alleviated LPS-induced inflammatory injury by inhibiting the NF-κB pathway in leukocytes and protected epithelial cells from oxidative damage, positioning it as a potential therapeutic candidate for ALI.

Prognostic and immunological roles of RSPO1 in pan-cancer and its correlation with LUAD proliferation and metastasis.

Aberrant RSPO1 expression is implicated in tumor progression across various cancers and correlates with anti-cancer immune cell characteristics. However, the specific role of R-spondin 1 (RSPO1) in lung adenocarcinoma (LUAD) remains unclear. In this study, we utilized data from The Cancer Genome Atlas (TCGA) to assess RSPO1 expression across 33 tumor types. Kaplan-Meier (K-M) analysis revealed the prognostic significance of RSPO1 in various cancers. Using statistical software R, we examined RSPO1's associations with immune cell infiltration, methylation, mutation, and competing endogenous RNA (ceRNA) networks. Exploration via the Tumor Immune Single Cell Hub (TISCH) database uncovered RSPO1's link to the tumor microenvironment (TME) and identified potential small molecule drug targets. We further investigated RSPO1's impact on LUAD cell proliferation, metastasis, and the Wnt pathway in vitro. Our findings highlight RSPO1's role in cancer progression and suggest its potential as both a prognostic marker and therapeutic target in LUAD, implicating the modulation of the Wnt pathway.

Label-Free Identification of Biochemical Footprints of Key Mutations in Acute Myeloid Leukemia Using Raman Spectroscopy

Introduction: With increasing understanding of the complexity and heterogeneity of acute myeloid leukemia (AML), the diagnostic process becomes more complicated and time-consuming. Therefore, there is a need to supplement AML diagnostics with a quick method to identify key, druggable genetic lesions that serve as a target for small molecule drugs. To address this need, we used Raman spectroscopy (RS)-based imaging methods. RS uses non-elastic scattering of laser light on cell biomolecules. When coupled to computational image segmentation and analysis, composition of the analyzed fragment of the cytoplasm can be characterized. In this way, by mapping the focal surface of the cell, we obtain a multidimensional image containing information about the distribution of its biochemical components. In our study, we utilized RS microscopy to investigate how single mutations affect the Raman spectral images of leukemic cells. The primary objective was to determine whether this technique could be utilized to identify mutations in key genes related to leukemia development. Methods: Genetic models of AML were generated by introducing wild-type and mutated forms of three frequently mutated and/or druggable genes (FLT3, IDH1/2, and MLL) into myeloid cell lines (THP1, HEL, HL60). The presence of the desired phenotype was confirmed using flow cytometry, immunoblotting, high-throughput kinase activity assay (Pamgene), and confocal microscopy. For RS experiments, the cells were fixed, and subjected to Raman imaging measurements using the WITec Alpha 300 microscope. Reference or control samples consisted of cells with wild-type genes (WT). The recorded hyperspectral data was subjected to multidimensional chemometric analysis, including k-means cluster analysis (KMCA), principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). To validate the findings from the cell models, primary AML cells from 30 patients diagnosed with FLT-3 ITD, MLL or IDH1/2 mutations were examined with RS. Peripheral blood mononuclear cells (PBMC) isolated from healthy donors blood served as reference. Results: The spectral differences between cells with wild-type FLT3 and FLT3/ITD were subtle, yet significant. The results revealed distinct variances in lipid (1440, 1240, 1660, and 2850 cm -1) and hemoprotein content (740-760 cm -1). Additionally, we successfully distinguished cells harboring IDH1/2 and MLL mutations from those with the wild-type genes. In these cases Raman bands corresponding to protein and nucleic acids composition were changed in comparison to control cells. Raman features in PCA loadings were also related to proteins and nucleic acids. Using the PLS-DA method and simple AI model, we built successful algorithm for distinguishing transgenic models carrying mutation from wild-type cells. PCA analysis also enabled us to distinguish AML primary cells from PBMC. Analysis weather primary cells carrying the selected mutations can be distinguished from non-mutated AML cells is concordant with our findings on models, however, it requires further analysis in larger group of patients to increase statistical power of these observations. Conclusions: Collectively, these findings suggest that Raman spectroscopy coupled with appropriate chemometric analysis may serve as rapid, label-free tool for identification of key druggable mutations in AML. Acknowledgements: The studies were performed as a part of the “ Label-free and rapid optical imaging, detection and sorting of leukemia cells” project carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

Small molecule inhibitors for cancer immunotherapy and associated biomarkers - the current status.

Following the success of cancer immunotherapy using large molecules against immune checkpoint inhibitors, the concept of using small molecules to interfere with intracellular negative regulators of anti-tumor immune responses has emerged in recent years. The main targets for small molecule drugs currently include enzymes of negative feedback loops in signaling pathways of immune cells and proteins that promote immunosuppressive signals within the tumor microenvironment. In the adaptive immune system, negative regulators of T cell receptor signaling (MAP4K1, DGKα/ζ, CBL-B, PTPN2, PTPN22, SHP1), co-receptor signaling (CBL-B) and cytokine signaling (PTPN2) have been preclinically validated as promising targets and initial clinical trials with small molecule inhibitors are underway. To enhance innate anti-tumor immune responses, inhibitory immunomodulation of cGAS/STING has been in the focus, and inhibitors of ENPP1 and TREX1 have reached the clinic. In addition, immunosuppressive signals via adenosine can be counteracted by CD39 and CD73 inhibition, while suppression via intratumoral immunosuppressive prostaglandin E can be targeted by EP2/EP4 antagonists. Here, we present the status of the most promising small molecule drug candidates for cancer immunotherapy, all residing relatively early in development, and the potential of relevant biomarkers.

Mapping Peptide-Protein Interactions by Amine-Reactive Cleavable Photoaffinity Reagents.

Photoaffinity labeling followed by tandem mass spectrometry is an often used strategy to identify protein targets of small-molecule drugs or drug candidates, which, under ideal conditions, enables the identification of the actual drug binding site. In the case of bioactive peptides, however, identifying the distinct binding site is hampered because of complex fragmentation patterns during tandem mass spectrometry. We here report the development and use of small cleavable photoaffinity reagents that allow functionalization of bioactive peptides for light-induced covalent binding to their protein targets. Upon cleavage of the covalently linked peptide drug, a chemical remnant of a defined mass remains on the bound amino acid, which is then used to unambiguously identify the drug binding site. Applying our approach to known peptide-drug/protein pairs with reported crystal structures, such as the calmodulin-melittin interaction, we were able to validate the identified binding sites based on structural models. Overall, our cleavable photoaffinity labeling strategy represents a powerful tool to enable the identification of protein targets and specific binding sites of a wide variety of bioactive peptides in the future.

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA-small molecule interactions. We recently developed fingeRNAt-a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions and encodes them as structural interaction fingerprint (SIFt). Here, we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We also employed Explainable Artificial Intelligence (XAI)-the SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations and other methods to help understand the decision-making process behind the predictive models. We conducted a case study in which we applied XAI on a predictive model of ligand binding to human immunodeficiency virus type 1 trans-activation response element RNA to distinguish between residues and interaction types important for binding. We also used XAI to indicate whether an interaction has a positive or negative effect on binding prediction and to quantify its impact. Our results obtained using all XAI methods were consistent with the literature data, demonstrating the utility and importance of XAI in medicinal chemistry and bioinformatics.

Immunization with a Trypanosoma cruzi cyclophilin-19 deletion mutant protects against acute Chagas disease in mice

Human infection with the protozoan parasite Trypanosoma cruzi causes Chagas disease for which there are no prophylactic vaccines. Cyclophilin 19 is a secreted cis-trans peptidyl isomerase expressed in all life stages of Trypanosoma cruzi. This protein in the insect stage leads to the inactivation of insect anti-parasitic peptides and parasite transformation whereas in the intracellular amastigotes it participates in generating ROS promoting the growth of parasites. We have generated a parasite mutant with depleted expression of Cyp19 by removal of 2 of 3 genes encoding this protein using double allelic homologous recombination. The mutant parasite line failed to replicate when inoculated into host cells in vitro or in mice indicating that Cyp19 is critical for infectivity. The mutant parasite line also fails to replicate in or cause clinical disease in immuno-deficient mice further validating their lack of virulence. Repeated inoculation of mutant parasites into immuno-competent mice elicits parasite-specific trypanolytic antibodies and a Th-1 biased immune response and challenge of mutant immunized mice with virulent wild-type parasites is 100% effective at preventing death from acute disease. These results suggest that parasite Cyp19 may be candidate for small molecule drug targeting and that the mutant parasite line may warrant further immunization studies for prevention of Chagas disease.

Study on the role and pharmacology of cuproptosis in gastric cancer.

Gastric cancer has a poor prognosis and high mortality. Cuproptosis, a novel programmed cell death, is rarely studied in gastric cancer. Studying the mechanism of cuproptosis in gastric cancer is conducive to the development of new drugs, improving the prognosis of patients and reducing the burden of disease. The TCGA database was used to obtain transcriptome data from gastric cancer tissues and adjacent tissues. GSE66229 was used for external verification. Overlapping genes were obtained by crossing the genes obtained by differential analysis with those related to copper death. Eight characteristic genes were obtained by three dimensionality reduction methods: lasso, SVM, and random forest. ROC and nomogram were used to estimate the diagnostic efficacy of characteristic genes. The CIBERSORT method was used to assess immune infiltration. ConsensusClusterPlus was used for subtype classification. Discovery Studio software conducts molecular docking between drugs and target proteins. We have established the early diagnosis model of eight characteristic genes (ENTPD3, PDZD4, CNN1, GTPBP4, FPGS, UTP25, CENPW, and FAM111A) for gastric cancer. The results are validated by internal and external data, and the predictive power is good. The subtype classification and immune type analysis of gastric cancer samples were performed based on the consensus clustering method. We identified C2 as an immune subtype and C1 as a non-immune subtype. Small molecule drug targeting based on genes associated with cuproptosis predicts potential therapeutics for gastric cancer. Molecular docking revealed multiple forces between Dasatinib and CNN1. The candidate drug Dasatinib may be effective in treating gastric cancer by affecting the expression of the cuproptosis signature gene.

Exploiting the intrinsic misfolding propensity of the KRAS oncoprotein

Mutant KRAS is a major driver of oncogenesis in a multitude of cancers but remains a challenging target for classical small molecule drugs, motivating the exploration of alternative approaches. Here, we show that aggregation-prone regions (APRs) in the primary sequence of the oncoprotein constitute intrinsic vulnerabilities that can be exploited to misfold KRAS into protein aggregates. Conveniently, this propensity that is present in wild-type KRAS is increased in the common oncogenic mutations at positions 12 and 13. We show that synthetic peptides (Pept-ins™) derived from two distinct KRAS APRs could induce the misfolding and subsequent loss of function of oncogenic KRAS, both of recombinantly produced protein in solution, during cell-free translation and in cancer cells. The Pept-ins exerted antiproliferative activity against a range of mutant KRAS cell lines and abrogated tumor growth in a syngeneic lung adenocarcinoma mouse model driven by mutant KRAS G12V. These findings provide proof-of-concept that the intrinsic misfolding propensity of the KRAS oncoprotein can be exploited to cause its functional inactivation.

ABC transporters: human disease and pharmacotherapeutic potential.

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters are a 48-member superfamily of membrane proteins that actively transport a variety of biological substrates across lipid membranes. Their functional diversity defines an expansive involvement in myriad aspects of human biology. At least 21 ABC transporters underlie rare monogenic disorders, with even more implicated in the predisposition to and symptomology of common and complex diseases. Such broad (patho)physiological relevance places this class of proteins at the intersection of disease causation and therapeutic potential, underlining them as promising targets for drug discovery, as exemplified by the transformative CFTR (ABCC7) modulator therapies for cystic fibrosis. This review will explore the growing relevance of ABC transporters to human disease and their potential as small-molecule drug targets.

Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning.

MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder.

MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.

Structural basis for the allosteric inhibition of hypoxia-inducible factor (HIF)-2 by belzutifan.

Hypoxia-inducible factor (HIF)-2α and its obligate heterodimerization partner aryl hydrocarbon receptor nuclear translocator (ARNT), are both members of the basic helix-loop-helix-PER-ARNT-SIM (bHLH-PAS) transcription factor family. Previous studies have identified HIF-2α as a key oncogenic driver in clear cell renal cell carcinoma (ccRCC), rendering it a promising drug target for this type of kidney cancer. Belzutifan is the first HIF-2α inhibitor approved for treating ccRCC and other cancers associated with the von Hippel-Lindau (VHL) disease. However, the detailed inhibitory mechanism of belzutifan at molecular level is still unclear. Here we obtained the crystal structure of HIF-2α-ARNT heterodimer in complex with belzutifan at 2.75 Å resolution. The complex structure shows that belzutifan binds into the PAS-B pocket of HIF-2α, and it destabilizes the dimerization of HIF-2α and ARNT through allosteric effects mainly mediated by the key residue M252 of HIF-2α near the dimer interface. We further explored the inhibitory effects of belzutifan using biochemical and functional assays. The time-resolved fluorescence energy transfer (TR-FRET)-based binding assay showed that belzutifan disrupts the dimerization of HIF-2α and ARNT with a Ki value of 20 nM. The luciferase reporter assay indicated that belzutifan can efficiently inhibit the transcriptional activity of HIF-2α with an IC50 value of 17 nM. Besides, the real-time PCR assay illustrated that belzutifan can reduce the expression of HIF-2α downstream genes in 786-O kidney cancer cells in a dose-dependent manner. Our work reveals the molecular mechanism by which belzutifan allosterically inhibits HIF-2α and provides valuable information for the subsequent drug development targeting HIF-2α. Significance Statement The bHLH-PAS family of transcription factors are an emerging group of small-molecule drug targets. Belzutifan, originally developed by Peloton Therapeutics, is the first FDA-approved drug directly binding to a bHLH-PAS protein, the hypoxia-inducible factor (HIF)-2α. Based on the protein-drug complex structure, biochemical binding assays, and functional profiling of downstream gene expression, this study reveals the regulatory mechanism of how belzutifan allosterically destabilizes HIF-2α's heterodimerization with its obligate partner protein, thus reducing their transcriptional activity that links to tumor progression.

Cellular Target Deconvolution of Small Molecules Using a Selection-Based Genetic Screening Platform.

Small-molecule drug target identification is an essential and often rate-limiting step in phenotypic drug discovery and remains a major challenge. Here, we report a novel platform for target identification of activators of signaling pathways by leveraging the power of a clustered regularly interspaced short palindromic repeats (CRISPR) knockout library. This platform links the expression of a suicide gene to the small-molecule-activated signaling pathway to create a selection system. With this system, loss-of-function screening using a CRISPR single-guide (sg) RNA library positively enriches cells in which the target has been knocked out. The identities of the drug targets and other essential genes required for the activity of small molecules of interest are then uncovered by sequencing. We tested this platform on BDW568, a newly discovered type-I interferon signaling activator, and identified stimulator of interferon genes (STING) as its target and carboxylesterase 1 (CES1) to be a key metabolizing enzyme required to activate BDW568 for target engagement. The platform we present here can be a general method applicable for target identification for a wide range of small molecules that activate different signaling pathways.