Design, synthesis, and biological evaluation of selective covalent inhibitors of FGFR4.

Aberrant signaling via fibroblast growth factor 19 (FGF19)/fibroblast growth factor receptor 4 (FGFR4) has been identified as a driver of tumorigenesis and the development of many solid tumors, making FGFR4 is a promising target for anticancer therapy. Herein, we designed and synthesized a series of bis-acrylamide covalent FGFR4 inhibitors and evaluated their inhibitory activity against FGFRs, FGFR4 mutants, and their antitumor activity. CXF-007, verified by mass spectrometry and crystal structures to form covalent bonds with Cys552 of FGFR4 and Cys488 of FGFR1, exhibited stronger selectivity and potent inhibitory activity for FGFR4 and FGFR4 cysteine mutants. Moreover, CXF-007 exhibited significant antitumor activity in hepatocellular carcinoma cell lines and breast cancer cell lines through sustained inhibition of the FGFR4 signaling pathway. In summary, our study highlights a novel covalent FGFR4 inhibitor, CXF-007, which has the potential to overcome drug-induced FGFR4 mutations and might provide a new strategy for future anticancer drug discovery.

Structural basis and selectivity of sulfatinib binding to FGFR and CSF-1R.

Acquired drug resistance poses a challenge for single-target FGFR inhibitors, leading to the development of dual- or multi-target FGFR inhibitors. Sulfatinib is a multi-target kinase inhibitor for treating neuroendocrine tumors, selectively targeting FGFR1/CSF-1R. To elucidate the molecular mechanisms behind its binding and kinase selectivity, we determined the crystal structures of sulfatinib with FGFR1/CSF-1R. The results reveal common structural features and distinct conformational adaptability of sulfatinib in response to FGFR1/CSF-1R binding. Further biochemical and structural analyses disclose sensitivity of sulfatinib to FGFR/CSF-1R gatekeeper mutations. The insensitivity of sulfatinib to FGFR gatekeeper mutations highlights the indispensable interactions with the hydrophobic pocket for FGFR selectivity, whereas the rotatory flexibility may enable sulfatinib to overcome CSF-1RT663I. This study not only sheds light on the structural basis governing sulfatinib's FGFR/CSF-1R inhibition, but also provides valuable insights into the rational design of dual- or multi-target FGFR inhibitors with selectivity for CSF-1R and sensitivity to gatekeeper mutations.

Structural insight into the macrocyclic inhibitor TPX-0022 of c-Met and c-Src.

c-Met has been an attractive target of prognostic and therapeutic studies in various cancers. TPX-0022 is a macrocyclic inhibitor of c-Met, c-Src and CSF1R kinases and is currently in phase I/II clinical trials in patients with advanced solid tumors harboring MET gene alterations. In this study, we determined the co-crystal structures of the c-Met/TPX-0022 and c-Src/TPX-0022 complexes to help elucidate the binding mechanism. TPX-0022 binds to the ATP pocket of c-Met and c-Src in a local minimum energy conformation and is stabilized by hydrophobic and hydrogen bond interactions. In addition, TPX-0022 exhibited potent activity against the resistance-relevant c-Met L1195F mutant and moderate activity against the c-Met G1163R, F1200I and Y1230H mutants but weak activity against the c-Met D1228N and Y1230C mutants. Overall, our study reveals the structural mechanism underlying the potency and selectivity of TPX-0022 and the ability to overcome acquire resistance mutations and provides insight into the development of selective c-Met macrocyclic inhibitors.

Structures of p53/BCL-2 complex suggest a mechanism for p53 to antagonize BCL-2 activity.

Mitochondrial apoptosis is strictly controlled by BCL-2 family proteins through a subtle network of protein interactions. The tumor suppressor protein p53 triggers transcription-independent apoptosis through direct interactions with BCL-2 family proteins, but the molecular mechanism is not well understood. In this study, we present three crystal structures of p53-DBD in complex with the anti-apoptotic protein BCL-2 at resolutions of 2.3-2.7 Å. The structures show that two loops of p53-DBD penetrate directly into the BH3-binding pocket of BCL-2. Structure-based mutations at the interface impair the p53/BCL-2 interaction. Specifically, the binding sites for p53 and the pro-apoptotic protein Bax in the BCL-2 pocket are mostly identical. In addition, formation of the p53/BCL-2 complex is negatively correlated with the formation of BCL-2 complexes with pro-apoptotic BCL-2 family members. Defects in the p53/BCL-2 interaction attenuate p53-mediated cell apoptosis. Overall, our study provides a structural basis for the interaction between p53 and BCL-2, and suggests a molecular mechanism by which p53 regulates transcription-independent apoptosis by antagonizing the interaction of BCL-2 with pro-apoptotic BCL-2 family members.

Comprehensive analysis of necroptosis-related lncRNA signature with potential implications in tumor heterogeneity and prediction of prognosis in clear cell renal cell carcinoma.

BACKGROUND: Necroptosis has been reported to play a critical role in occurrence and progression of cancer. The dysregulation of long non-coding RNAs (lncRNAs) is associated with the progression and metastasis of clear cell renal cell carcinoma (CCRCC). However, research on necroptosis-related lncRNAs in the tumor heterogeneity and prognosis of CCRCC is not completely unclear. This study aimed to analysis the tumor heterogeneity among CCRCC subgroups and construct a CCRCC prognostic signature based on necroptosis-related lncRNAs. METHODS: Weighted gene co-expression network analysis (WGCNA) was performed to identify necroptosis-related lncRNAs. A preliminary classification of molecular subgroups was performed by non-negative matrix factorization (NMF) consensus clustering analysis. Comprehensive analyses, including fraction genome altered (FGA), tumor mutational burden (TMB), DNA methylation alterations, copy number variations (CNVs), and single nucleotide polymorphisms (SNPs), were performed to explore the potential factors for tumor heterogeneity among the three subgroups. Subsequently, we constructed a predictive signature by multivariate Cox regression. Nomogram, calibration curves, decision curve analysis (DCA), and time-dependent receiver-operating characteristics (ROC) were used to validate and evaluate the signature. Finally, immune correlation analyses, including immune-related signaling pathways, immune cell infiltration status and immune checkpoint gene expression level, were also performed. RESULTS: Seven necroptosis-related lncRNAs were screened out by WGCNA, and three subgroups were classified by NMF consensus clustering analysis. There were significant differences in survival prognosis, clinicopathological characteristics, enrichments of immune-related signaling pathway, degree of immune cell infiltration, and expression of immune checkpoint genes in the various subgroups. Most importantly, we found that 26 differentially expressed genes (DEGs) among the 3 subgroups were not affected by DNA methylation alterations, CNVs and SNPs. On the contrary, these DEGs were associated with the seven necroptosis-related lncRNAs. Subsequently, the identified RP11-133F8.2 and RP11-283G6.4 by multivariate Cox regression analysis were involved in the risk model, which could serve as an independent prognostic factor for CCRCC. Finally, qRT-PCR confirmed the differential expression of the two lncRNAs. CONCLUSIONS: These findings contributed to understanding the function of necroptosis-related lncRNAs in CCRCC and provided new insights of prognostic evaluation and optimal therapeutic strategy for CCRCC.

IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs.

Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current studies pay scant attention to the following two aspects. First, the interpretability of current models is manifested in providing reasoning paths, which is an essential property of path-based models. However, the comparison of reasoning paths in these models is operated in a black-box fashion. Moreover, contemporary models utilize separate networks to evaluate paths at different hops. Although the network for each hop has the same architecture, each network achieves different parameters for better performance. Different parameters cause identical semantics to have different scores, so models cannot measure identical semantics at different hops equally. Inspired by the observation that reasoning based on multi-hop paths is akin to answering questions step by step, this paper designs an Interpretable Multi-Hop Reasoning (IMR) framework based on consistent basic models for temporal KG forecasting. IMR transforms reasoning based on path searching into stepwise question answering. In addition, IMR develops three indicators according to the characteristics of temporal KGs and reasoning paths: the question matching degree, answer completion level, and path confidence. IMR can uniformly integrate paths of different hops according to the same criteria; IMR can provide the reasoning paths similarly to other interpretable models and further explain the basis for path comparison. We instantiate the framework based on common embedding models such as TransE, RotatE, and ComplEx. While being more explainable, these instantiated models achieve state-of-the-art performance against previous models on four baseline datasets.

Prediction of drug side effects with transductive matrix co-completion.

MOTIVATION: Side effects of drugs could cause severe health problems and the failure of drug development. Drug-target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. RESULTS: We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug-target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. AVAILABILITY AND IMPLEMENTATION: The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Discovery of D6808, a Highly Selective and Potent Macrocyclic c-Met Inhibitor for Gastric Cancer Harboring MET Gene Alteration Treatment.

MET alterations have been validated as a driven factor in NSCLC and gastric cancers. The c-Met inhibitors, capmatinib, tepotinib, and savolitinib, are only approved for the treatment of NSCLC harboring exon 14 skipping mutant MET. We used a molecular hybridization in conjunction with macrocyclization strategy for structural optimization to obtain a series of 2-(2-(quinolin-6-yl)ethyl)pyridazin-3(2H)-one derivatives as new c-Met inhibitors. One of the macrocyclic compounds, D6808, potently inhibited c-Met kinase and MET-amplified Hs746T gastric cancer cells with IC50 values of 2.9 and 0.7 nM, respectively. It also strongly suppressed Ba/F3-Tpr-Met cells harboring resistance-relevant mutations (F1200L/M1250T/H1094Y/F1200I/L1195V) with IC50 values of 4.2, 3.2, 1.0, 39.0, and 33.4 nM, respectively. Furthermore, D6808 exhibited extraordinary target specificity in a Kinome profiling against 373 wild-type kinases and served as a promising macrocycle-based compound for further anticancer drug development.

Structural insight into the ligand binding mechanism of aryl hydrocarbon receptor.

The aryl hydrocarbon receptor (AHR), a member of the basic helix-loop-helix (bHLH) Per-Arnt-Sim (PAS) family of transcription factors, plays important roles in regulating xenobiotic metabolism, cellular differentiation, stem cell maintenance, as well as immunity. More recently, AHR has gained significant interest as a drug target for the development of novel cancer immunotherapy drugs. Detailed understanding of AHR-ligand binding has been hampered for decades by the lack of a three-dimensional structure of the AHR PAS-B domain. Here, we present multiple crystal structures of the Drosophila AHR PAS-B domain, including its apo, ligand-bound, and AHR nuclear translocator (ARNT) PAS-B-bound forms. Together with biochemical and cellular assays, our data reveal structural features of the AHR PAS-B domain, provide insights into the mechanism of AHR ligand binding, and provide the structural basis for the future development of AHR-targeted therapeutics.

A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects.

Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and uses sparse structure learning to model the relationships between side effects. Additionally, the proposed method adopts the adaptive graph regularization strategy to explore the local structure in drug data and fuse multiple types of drug features. An alternating optimization algorithm is proposed to solve the optimization problem. We collected chemical structures and biological pathway features of drugs as the inputs of our method to predict drug side effects. The results of the cross-validation experiment showed that our method could significantly improve the prediction performance compared to the other state-of-the-art methods. Besides, our model is highly interpretable. It could learn the drug neighbourhood relationships, side effect relationships, and drug features related to side effects. We systematically validated the information extracted by the model with independent data. Some prediction results could also be supported by literature reports. The proposed method could be applied to integrate both chemical and biological data to predict side effects and helps improve drug safety.

Characterization of the modification of Kelch-like ECH-associated protein 1 by different fumarates.

Fumarates (fumaric acid esters), primarily dimethyl fumarate (DMF) and monoethyl fumarate (MEF) and its salts, are orally administered systemic agents used for the treatment of psoriasis and multiple sclerosis. It is widely believed that the pharmaceutical activities of fumarates are exerted through the Keap1-Nrf2 pathway. Although it has been revealed that DMF and MEF differentially modify specific Keap1 cysteine residues and result in the differential activation of Nrf2, how the modification of DMF and MEF impacts the biochemical properties of Keap1 has not been well characterized. Here, we found that both DMF and MEF can only modify the BTB domain of Keap1 and that only C151 is accessible for covalent binding in vitro. Dynamic fluorescence scanning (DSF) assays showed that the modification of DMF to Keap1 BTB increased its thermal stability, while the modification of MEF dramatically decreased its thermal stability. Further crystal structures revealed no significant conformational variation between the DMF-modified and MEF-modified BTBs. Overall, our biochemical and structural study provides a better understanding of the covalent modification of fumarates to Keap1 and may suggest fundamentally different mechanisms adopted by fumarates in regulating the Keap1-Nrf2 pathway.

Structural study of ponatinib in inhibiting SRC kinase.

Ponatinib is a multi-target tyrosine kinase inhibitor that targets ABL, SRC, FGFR, and so on. It was designed to overcome the resistance of BCR-ABL mutation to imatinib, especially the gatekeeper mutation ABLT315I. The molecular mechanism by which ponatinib overcomes mutations of BCR-ABL and some other targets has been explained, but little information is known about the characteristics of ponatinib binding to SRC. Here, we showed that ponatinib inhibited wild type SRC kinase but failed to inhibit SRC gatekeeper mutants in both biochemical and cellular assays. We determined the crystal structure of ponatinib in complex with the SRC kinase domain. In addition, by structural analysis, we provided a possible explanation for why ponatinib showed different effects on SRC and other kinases with gatekeeper mutations. The resistance mechanism of SRC gatekeeper mutations to ponatinib may provide meaningful information for designing inhibitors against SRC family kinases in the future.

Structural insight into the molecular mechanism of cilofexor binding to the farnesoid X receptor.

Farnesoid X receptor (FXR) is a bile acid-related nuclear receptor and is considered a promising target to treat several liver disorders. Cilofexor is a selective FXR agonist and has already entered phase III trials in primary sclerosing cholangitis (PSC) patients. Pruritis caused by cilofexor treatment is dose dependent. The binding characteristics of cilofexor with FXR and its pruritogenic mechanism remain unclear. In our research, the affinity of cilofexor bound to FXR was detected using an isothermal titration calorimetry (ITC) assay. The binding mechanism between cilofexor and FXR-LBD is explained by the cocrystal structure of the FXR/cilofexor complex. Structural models indicate the possibility that cilofexor activates Mas-related G protein-coupled receptor X4 (MRGPRX4) or G protein-coupled bile acid receptor 1 (GPBAR1), leading to pruritus. In summary, our analyses provide a molecular mechanism of cilofexor binding to FXR and provide a possible explanation for the dose-dependent pruritis of cilofexor.

Structural insights into the potency and selectivity of covalent pan-FGFR inhibitors.

FIIN-2, TAS-120 (Futibatinib) and PRN1371 are highly potent pan-FGFR covalent inhibitors targeting the p-loop cysteine of FGFR proteins, of which TAS-120 and PRN1371 are currently in clinical trials. It is critical to analyze their target selectivity and their abilities to overcome gatekeeper mutations. In this study, we demonstrate that FIIN-2 and TAS-120 form covalent adducts with SRC, while PRN1371 does not. FIIN-2 and TAS-120 inhibit SRC and YES activities, while PRN1371 does not. Moreover, FIIN-2, TAS-120 and PRN1371 exhibit different potencies against different FGFR gatekeeper mutants. In addition, the co-crystal structures of SRC/FIIN-2, SRC/TAS-120 and FGFR4/PRN1371 complexes reveal structural basis for kinase targeting and gatekeeper mutations. Taken together, our study not only provides insight into the potency and selectivity of covalent pan-FGFR inhibitors, but also sheds light on the development of next-generation FGFR covalent inhibitors with high potency, high selectivity, and stronger ability to overcome gatekeeper mutations.

Structure-based design of a dual-warhead covalent inhibitor of FGFR4.

The fibroblast growth factor 19 (FGF19)/fibroblast growth factor receptor 4 (FGFR4) signaling pathways play critical roles in a variety of cancers, such as hepatocellular carcinoma (HCC). FGFR4 is recognized as a promising target to treat HCC. Currently, all FGFR covalent inhibitors target one of the two cysteines (Cys477 and Cys552). Here, we designed and synthesized a dual-warhead covalent FGFR4 inhibitor, CXF-009, targeting Cys477 and Cys552 of FGFR4. We report the cocrystal structure of FGFR4 with CXF-009, which exhibits a dual-warhead covalent binding mode. CXF-009 exhibited stronger selectivity for FGFR4 than FGFR1-3 and other kinases. CXF-009 can also potently inhibit the single cystine mutants, FGFR4(C477A) and FGFR4(C552A), of FGFR4. In summary, our study provides a dual-warhead covalent FGFR4 inhibitor that can covalently target two cysteines of FGFR4. CXF-009, to our knowledge, is the first reported inhibitor that forms dual-warhead covalent bonds with two cysteine residues in FGFR4. CXF-009 also has the potential to overcome drug induced resistant FGFR4 mutations and might serve as a lead compound for future anticancer drug discovery.

Characterization of the cholangiocarcinoma drug pemigatinib against FGFR gatekeeper mutants.

Fibroblast growth factor receptor (FGFR) dysregulation is involved in a variety of tumorigenesis and development. Cholangiocarcinoma is closely related with FGFR aberrations, and pemigatinib is the first drug approved to target FGFR for the treatment of cholangiocarcinoma. Herein, we undertake biochemical and structural analysis on pemigatinib against FGFRs as well as gatekeeper mutations. The results show that pemigatinib is a potent and selective FGFR1-3 inhibitor. The extensive network of hydrogen bonds and van der Waals contacts found in the FGFR1-pemigatinib binding mode accounts for the high potency. Pemigatinib also has excellent potency against the Val-to-Ile gatekeeper mutation but less potency against the Val-to-Met/Phe gatekeeper mutation in FGFR. Taken together, the inhibitory and structural profiles exemplified by pemigatinib may help to thwart Val-to-Ile gatekeeper mutation-based resistance at earlier administration and to advance the further design and improvement for inhibitors toward FGFRs with gatekeeper mutations.

Structural insight into the molecular mechanism of p53-mediated mitochondrial apoptosis.

The tumor suppressor p53 is mutated in approximately half of all human cancers. p53 can induce apoptosis through mitochondrial membrane permeabilization by interacting with and antagonizing the anti-apoptotic proteins BCL-xL and BCL-2. However, the mechanisms by which p53 induces mitochondrial apoptosis remain elusive. Here, we report a 2.5 Å crystal structure of human p53/BCL-xL complex. In this structure, two p53 molecules interact as a homodimer, and bind one BCL-xL molecule to form a ternary complex with a 2:1 stoichiometry. Mutations at the p53 dimer interface or p53/BCL-xL interface disrupt p53/BCL-xL interaction and p53-mediated apoptosis. Overall, our current findings of the bona fide structure of p53/BCL-xL complex reveal the molecular basis of the interaction between p53 and BCL-xL, and provide insight into p53-mediated mitochondrial apoptosis.

Mechanism of forkhead transcription factors binding to a novel palindromic DNA site.

Forkhead transcription factors bind a canonical consensus DNA motif, RYAAAYA (R = A/G, Y = C/T), as a monomer. However, the molecular mechanisms by which forkhead transcription factors bind DNA as a dimer are not well understood. In this study, we show that FOXO1 recognizes a palindromic DNA element DIV2, and mediates transcriptional regulation. The crystal structure of FOXO1/DIV2 reveals that the FOXO1 DNA binding domain (DBD) binds the DIV2 site as a homodimer. The wing1 region of FOXO1 mediates the dimerization, which enhances FOXO1 DNA binding affinity and complex stability. Further biochemical assays show that FOXO3, FOXM1 and FOXI1 also bind the DIV2 site as homodimer, while FOXC2 can only bind this site as a monomer. Our structural, biochemical and bioinformatics analyses not only provide a novel mechanism by which FOXO1 binds DNA as a homodimer, but also shed light on the target selection of forkhead transcription factors.

Characterization of ibrutinib as a non-covalent inhibitor of SRC-family kinases.

Ibrutinib is a BTK-targeted irreversible inhibitor. In this study, we demonstrate that ibrutinib potently inhibits SRC activity in a non-covalent manner via mass spectrometry and crystallography. The S345C mutation renders SRC to bind covalently with ibrutinib, and restores the potency of ibrutinib against the gatekeeper mutant. The co-crystal structure of ibrutinib/SRC shows Ser345 of SRC did not form covalent bond with ibrutinib, leading to a decrease of potency and loss of the ability to overcome the gatekeeper mutation of SRC. The X-ray crystallographic studies also provide structural insight into why ibrutinib behaves differently against gatekeeper mutants of different kinases.