A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Quality control, safety assessment and preparation approaches of low molecular weight heparin.

Low Molecular Weight Heparins (LMWHs) are well-established for use in the prevention and treatment of thrombotic diseases, and as a substitute for unfractionated heparin (UFH) due to their predictable pharmacokinetics and subcutaneous bioavailability. LMWHs are produced by various depolymerization methods from UFH, resulting in heterogeneous compounds with similar biochemical and pharmacological properties. However, the delicate supply chain of UFH and potential contamination from animal sources require new manufacturing approaches for LMWHs. Various LMWH preparation methods are emerging, such as chemical synthesis, enzymatic or chemical depolymerization and chemoenzymatic synthesis. To establish the sameness of active ingredients in both innovator and generic LMWH products, the Food and Drug Administration has implemented a stringent scientific method of equivalence based on physicochemical properties, heparin source material and depolymerization techniques, disaccharide composition and oligosaccharide mapping, biological and biochemical properties, and in vivo pharmacodynamic profiles. In this review, we discuss currently available LMWHs, potential manufacturing methods, and recent progress for manufacturing quality control of these LMWHs.

Nucleotides as new co-formers in co-amorphous systems: Enhanced dissolution rate, water solubility and physical stability.

Developing co-amorphous systems is an attractive strategy to improve the dissolution rate of poorly water-soluble drugs. Various co-formers have been investigated. However, previous studies revealed that it is a challenge to develop satisfied acidic co-formers, e.g., acidic amino acids showed much poorer co-former properties than neutral and basic amino acids. Only a few acidic co-formers have been reported, such as aspartic acid, glutamic acid, and some other organic acids. Thus, this study aims to explore the possibility of adenosine monophosphate and adenosine diphosphate used as acidic co-formers. Mebendazole, celecoxib and tadalafil were used as the model drugs. The drug-co-former co-amorphous systems were prepared via ball milling and confirmed using XRPD. The dissolution study suggested that the solubility and dissolution rate of the drug-co-formers systems were increased significantly compared to the corresponding crystalline and amorphous drugs. The stability study revealed that using the two nucleotides as co-formers enhanced the physical stability of pure amorphous drugs. Molecular interactions were observed in MEB-co-former and TAD-co-former systems and positively affected the pharmaceutical performance of the investigated co-amorphous systems. In conclusion, the two nucleotides could be promising potential acidic co-formers for co-amorphous systems.

Excitation generated preferential binding sites for ethane on porous carbon-copper porphyrin sorbents: ethane/ethylene adsorptive separation improved by light.

Energy-efficient separation of C2H6/C2H4 is a great challenge, for which adsorptive separation is very promising. C2H6-selective adsorption has big implications, while the design of C2H6-sorbents with ideal adsorption capability, particularly with the C2H6/C2H4-selectivity exceeded 2.0, is still challenging. Instead of the current strategies such as chemical modification or pore space modulation, we propose a new methodology for the design of C2H6-sorbents. With a Cu-TCPP [TCPP = 5,10,15,20-tetrakis(4-carboxyphenyl)porphyrin] framework dispersed onto a microporous carbon and a hierarchical-pore carbon, two composite sorbents are fabricated. The composite sorbents exhibit enhanced C2H6-selective adsorption capabilities with visible light, particularly the composite sorbent based on the hierarchical-pore carbon, whose C2H6 and C2H4 adsorption capacities (0 °C, 1 bar) are targetedly increased by 27% and only 1.8% with visible light, and therefore, an C2H6-selectivity (C2H6/C2H4 = 10/90, v/v) of 4.8 can be realized. With visible light, the adsorption force of the C2H6 molecule can be asymmetrically enhanced by the excitation enriched electron density over the adsorption sites formed via the close interaction between the Cu-TCPP and the carbon layer, whereas that of the C2H4 molecule is symmetrically altered and the forces cancelled each other out. This strategy may open up a new route for energy-efficient adsorptive separation of C2H6/C2H4 with light.

Discovery of a selective TRF2 inhibitor FKB04 induced telomere shortening and senescence in liver cancer cells.

Telomere repeat binding factor 2 (TRF2), a critical element of the shelterin complex, plays a vital role in the maintenance of genome integrity. TRF2 overexpression is found in a wide range of malignant cancers, whereas its down-regulation could cause cell death. Despite its potential role, the selectively small-molecule inhibitors of TRF2 and its therapeutic effects on liver cancer remain largely unknown. Our clinical data combined with bioinformatic analysis demonstrated that TRF2 is overexpressed in liver cancer and that high expression is associated with poor prognosis. Flavokavain B derivative FKB04 potently inhibited TRF2 expression in liver cancer cells while having limited effects on the other five shelterin subunits. Moreover, FKB04 treatment induced telomere shortening and increased the amounts of telomere-free ends, leading to the destruction of T-loop structure. Consequently, FKB04 promoted liver cancer cell senescence without modulating apoptosis levels. In corroboration with these findings, FKB04 inhibited tumor cell growth by promoting telomeric TRF2 deficiency-induced telomere shortening in a mouse xenograft tumor model, with no obvious side effects. These results demonstrate that TRF2 is a potential therapeutic target for liver cancer and suggest that FKB04 may be a selective small-molecule inhibitor of TRF2, showing promise in the treatment of liver cancer.

Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

Evaluation of DNA-protein complex structures using the deep learning method.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

RNet: a network strategy to predict RNA binding preferences.

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

FKBP5 activates mitophagy by ablating PPAR-γ to shape a benign remyelination environment.

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease of the central nervous system (CNS) that is characterized by myelin damage, followed by axonal and ultimately neuronal loss, which has been found to be associated with mitophagy. The etiology and pathology of MS remain elusive. However, the role of FK506 binding protein 5 (FKBP5, also called FKBP51), a newly identified gene associated with MS, in the progression of the disease has not been well defined. Here, we observed that the progress of myelin loss and regeneration in Fkbp5ko mice treated with demyelination for the same amount of time was significantly slower than that in wild-type mice, and that mitophagy plays an important regulatory role in this process. To investigate the mechanism, we discovered that the levels of FKBP5 protein were greatly enhanced in the CNS of cuprizone (CPZ) mice and the myelin-denuded environment stimulates significant activation of the PINK1/Parkin-mediated mitophagy, in which the important regulator, PPAR-γ, is critically regulated by FKBP5. This study reveals the role of FKBP5 in regulating a dynamic pathway of natural restorative regulation of mitophagy through PPAR-γ in pathological demyelinating settings, which may provide potential targets for the treatment of demyelinating diseases.

Photo-Responsive Carbon Capture over Metalloporphyrin-C60 Metal-Organic Frameworks via Charge-Transfer.

Great efforts have been devoted to the study of photo-responsive adsorption, but its current methodology largely depends on the well-defined photochromic units and their photo-driven molecular deformation. Here, a methodology to fabricate nondeforming photo-responsive sorbents is successfully exploited. With C60-fullerene doping in metalloporphyrin metal-organic frameworks (PCN-M, M = Fe, Co, or Ni) and intensively interacting with the metalloporphyrin sites, effective charge-transfer can be achieved over the metalloporphyrin-C60 architectures once excited by the light at 350 to 780 nm. The electron density distribution and the resultant adsorption activity are thus changed by excited states, which are also stable enough to meet the timescale of microscopic adsorption equilibrium. The charge-transfer over Co(II)-porphyrin-C60 is proved to be more efficient than the Fe(II)- and Ni(II)-porphyrin-C60 sites, as well as than all the metalloporphyrin sites, so the CO2 adsorption capacity (CAC; at 0 °C and 1 bar) over the C60-doped PCN-Co can be largely improved from 2.05 mmol g-1 in the darkness to 2.69 mmol g-1 with light, increased by 31%, in contrast to photo-irresponsive CAC over all C60-undoped PCN-M sorbents and only the photo-loss CAC over C60.

Dynamic geometry design of cyclic peptide architectures for RNA structure.

Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications.

Targeting extracellular matrix through phytochemicals: a promising approach of multi-step actions on the treatment and prevention of cancer.

Extracellular matrix (ECM) plays a pivotal and dynamic role in the construction of tumor microenvironment (TME), becoming the focus in cancer research and treatment. Multiple cell signaling in ECM remodeling contribute to uncontrolled proliferation, metastasis, immune evasion and drug resistance of cancer. Targeting trilogy of ECM remodeling could be a new strategy during the early-, middle-, advanced-stages of cancer and overcoming drug resistance. Currently nearly 60% of the alternative anticancer drugs are derived from natural products or active ingredients or structural analogs isolated from plants. According to the characteristics of ECM, this manuscript proposes three phases of whole-process management of cancer, including prevention of cancer development in the early stage of cancer (Phase I); prevent the metastasis of tumor in the middle stage of cancer (Phase II); provide a novel method in the use of immunotherapy for advanced cancer (Phase III), and present novel insights on the contribution of natural products use as innovative strategies to exert anticancer effects by targeting components in ECM. Herein, we focus on trilogy of ECM remodeling and the interaction among ECM, cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs), and sort out the intervention effects of natural products on the ECM and related targets in the tumor progression, provide a reference for the development of new drugs against tumor metastasis and recurrence.

Design, synthesis, and biological evaluation of 1,6-naphthyridine-2-one derivatives as novel FGFR4 inhibitors for the treatment of colorectal cancer.

Aberrant FGFR4 signaling has been implicated in the development of several cancers, making FGFR4 a promising target for cancer therapy. Several FGFR4-selective inhibitors have been developed, yet none of them have been approved. Herein, we report a novel series of 1,6-naphthyridine-2-one derivatives as potent and selective inhibitors targeting FGFR4 kinase. Preliminary structure-activity relationship analysis was conducted. The screening cascades revealed that 19g was the preferred compound among the prepared series. 19g demonstrated excellent kinase selectivity and substantial cytotoxic effect against all tested colorectal cancer cell lines. 19g induced significant tumor inhibition in a HCT116 xenograft mouse model without any apparent toxicity. Notably, 19g exhibited excellent potency in disrupting the phosphorylation of FGFR4 and downstream signaling proteins mediated by FGF18 and FGF19. Compound 19g might be a potential antitumor drug candidate for the treatment of colorectal cancer.

Dissolution changes in drug-amino acid/biotin co-amorphous systems: Decreased/increased dissolution during storage without recrystallization.

Co-amorphous systems have been proven to be a promising strategy to address the poor water solubility of poorly water-soluble drugs. Generally, the initial dissolution behaviors after co-amorphous system preparation and the potential recrystallization during storage are used to evaluate the performance of co-amorphous systems. However, this study reveals that decreased dissolution and unexpected increased dissolution were observed during storage though the co-amorphous systems maintained amorphous form. Three drugs (valsartan, tadalafil, mebendazole) and three co-formers (arginine, tryptophan, biotin) were used to prepare co-amorphous systems and the samples were stored for different times. After stored for 80 d, most of the co-amorphous systems maintained amorphous form, however, decreased and increased intrinsic dissolution rates (IDRs) were both observed in these non-recrystallized co-amorphous systems. The moisture changes of the systems during storage and the possible drug-co-former molecular interactions showed no effect on the dissolution changes, while phase separation might play a role in it. In conclusion, more attention should be paid to the dissolution changes of co-amorphous systems during storage. Focusing on the initial dissolution behaviors after sample preparation and the physical recrystallization during storage is not enough for the development of co-amorphous systems in future.

Macrophage DCLK1 promotes obesity-induced cardiomyopathy via activating RIP2/TAK1 signaling pathway.

Obesity increases the risk for cardiovascular diseases and induces cardiomyopathy. Chronic inflammation plays a significant role in obesity-induced cardiomyopathy and may provide new therapeutic targets for this disease. Doublecortin-like kinase 1 (DCLK1) is an important target for cancer therapy and the role of DCLK1 in obesity and cardiovascular diseases is unclear. Herein, we showed that DCLK1 was overexpressed in the cardiac tissue of obese mice and investigated the role of DCLK1 in obesity-induced cardiomyopathy. We generated DCLK1-deleted mice and showed that macrophage-specific DCLK1 knockout, rather than cardiomyocyte-specific DCLK1 knockout, prevented high-fat diet (HFD)-induced heart dysfunction, cardiac hypertrophy, and fibrosis. RNA sequencing analysis showed that DCLK1 deficiency exerted cardioprotective effects by suppressing RIP2/TAK1 activation and inflammatory responses in macrophages. Upon HFD/palmitate (PA) challenge, macrophage DCLK1 mediates RIP2/TAK1 phosphorylation and subsequent inflammatory cytokine release, which further promotes hypertrophy in cardiomyocytes and fibrogenesis in fibroblasts. Finally, a pharmacological inhibitor of DCLK1 significantly protects hearts in HFD-fed mice. Our study demonstrates a novel role and a pro-inflammatory mechanism of macrophage DCLK1 in obesity-induced cardiomyopathy and identifies DCLK1 as a new therapeutic target for the treatment of this disease. Upon HFD/PA challenge, DCLK1 induces RIP2/TAK1-mediated inflammatory response in macrophages, which subsequently promotes cardiac hypertrophy and fibrosis. Macrophage-specific DCLK1 deletion or pharmacological inhibition of DCLK1 protects hearts in HFD-fed mice.

Enhanced CO Adsorption by Modulating the Electron Density Distribution of Graphite-Copper Porphyrin Sorbents with Light.

The photo-responsive adsorption has emerged as a vibrant area, but its current methodology is limited by the well-defined photochromic units and their molecular deformation driven by photo-stimuli. Herein, a methodology of nondeforming photo-responsiveness is successfully exploited. With the exploiting agent of Cu-TCPP framework assembled on the graphite and strongly interacted with it, the sorbent generates two kinds of adsorption sites, over which the electron density distribution of the graphite layer can be modulated at the c-axis direction, which can further evolve due to photo-stimulated excited states. The excited states are stable enough to meet the timescale of microscopic adsorption equilibrium. Independent of the ultra-low specific surface area of the sorbent (20 m2 g-1 ), the CO adsorption capability can be improved from 0.50 mmol g-1 at the ground state to 1.24 mmol g-1 (0 °C, 1 bar) with the visible light radiation, rather than the photothermal desorption.