Identification of drug-side effect association via restricted Boltzmann machines with penalized term.

In the entire life cycle of drug development, the side effect is one of the major failure factors. Severe side effects of drugs that go undetected until the post-marketing stage leads to around two million patient morbidities every year in the United States. Therefore, there is an urgent need for a method to predict side effects of approved drugs and new drugs. Following this need, we present a new predictor for finding side effects of drugs. Firstly, multiple similarity matrices are constructed based on the association profile feature and drug chemical structure information. Secondly, these similarity matrices are integrated by Centered Kernel Alignment-based Multiple Kernel Learning algorithm. Then, Weighted K nearest known neighbors is utilized to complement the adjacency matrix. Next, we construct Restricted Boltzmann machines (RBM) in drug space and side effect space, respectively, and apply a penalized maximum likelihood approach to train model. At last, the average decision rule was adopted to integrate predictions from RBMs. Comparison results and case studies demonstrate, with four benchmark datasets, that our method can give a more accurate and reliable prediction result.

Chemical Manipulation of the Spin-Crossover Dynamics through Judicious Metal-Ion Dilution.

In 2019, our groups described a unique FeII complex, [Fe(2MeL)(NCBH3)2] (2MeL = N,N'-dimethyl-N,N'-bis(2-pyridylmethyl)-1,2-ethanediamine) possessing a low-spin ground state that is not easily accessible due to the extremely slow dynamics of the high-spin to low-spin phase transition. Herein, we report the successful chemical manipulation of this spin-crossover (SCO) process through controlled metal-ion dilutions. The emergence or suppression of the thermally induced SCO behavior was observed depending on the radius of the metal ion used for the dilution (NiII or ZnII). Reversible photo-switching has been confirmed in all mixed-metal complexes whether the low-spin state is thermally accessible. Remarkably, the dilution with ZnII metal ions stabilizes HS FeII complexes with complete suppression of the thermally induced SCO process without destroying the reversible photoswitchability of the material.

Design and synthesis of N-(1-(6-(substituted phenyl)-pyridazin-3-yl)-piperidine-3-yl)-amine derivatives as JMJD6 inhibitors.

JMJD6 is a member of the JmjC domain-containing family and has been identified as a promising therapeutic target for treating estrogen-induced and triple-negative breast cancer. To develop novel anti-breast cancer agents, we synthesized a class of N-(1-(6-(substituted phenyl)-pyridazine-3-yl)-piperidine-3-yl)-amine derivatives as potential JMJD6 inhibitors. Among them, the anti-cancer compound A29 was an excellent JMJD6 binder (KD = 0.75 ± 0.08 µM). It could upregulate the mRNA and protein levels of p53 and its downstream effectors p21 and PUMA by inhibiting JMJD6. Besides, A29 displayed potent anti-proliferative activities against tested breast cancer cells by the induction of cell apoptosis and cell cycle arrest. Significantly, A29 also promoted a remarkable reduction in tumor growth, with a TGI value of 66.6% (50 mg/kg, i.p.). Taken together, our findings suggest that A29 is a potent JMJD6 inhibitor bearing a new scaffold acting as a promising drug candidate for the treatment of breast cancer.

Design and synthesis of adamantyl-substituted flavonoid derivatives as anti-inflammatory Nur77 modulators: Compound B7 targets Nur77 and improves LPS-induced inflammation in vitro and in vivo.

In continuing our study on discovering new Nur77-targeting anti-inflammatory agents with natural skeletons, we combined adamantyl group and hydroxamic acid moiety with flavonoid nucleus, synthesized three series of flavonoid derivatives with a similar structure like CD437, and evaluated their activities against LPS-induced inflammation. Compound B7 was found to be an excellent Nur77 binder (Kd = 3.55 × 10-7 M) and a potent inhibitor of inflammation, which significantly decreased the production of cytokines in vitro, such as NO, IL-6, IL-1ß, and TNF-α, at concentrations of 1.25, 2.5, and 5 µM. Mechanistically, B7 modulated the colocalization of Nur77 at mitochondria and inhibited the lipopolysaccharides (LPS)-induced inflammation via the blockade of NF-κB activation in a Nur77-dependent manner. Additionally, B7 showed in vivo anti-inflammatory activity in the LPS-induced mice model of acute lung injury (ALI). These data suggest that the Nur77-targeting flavonoid derivatives can be particularly useful for further pharmaceutical development for the treatment of inflammatory diseases such as ALI.

A sequence-based multiple kernel model for identifying DNA-binding proteins.

BACKGROUND: DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. RESULTS: In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([Formula: see text]) and PDB186 ([Formula: see text]), respectively. CONCLUSION: Multiple kernel learning could fuse the complementary information between different features. Compared with existing methods, our method achieves comparable and best results on benchmark data sets.

Discovery of new chalone adamantyl arotinoids having RXRα-modulating and anticancer activities.

In the present study, a new series of chalcone adamantly arotinoids (chalcone AdArs) derived from RAR antagonist MX781, are synthesized, characterized, and evaluated for the biological activities in vitro. The studies of antiproliferative activity and RXRα-binding affinity of target compounds result in the discovery of a lead candidate (WA15), which is a good RXRα binder (Kd = 2.89 × 10-6 M) with potent antiproliferative activity against human cancer cell lines (IC50 ≈ 10 µM) and low toxic to normal LO2 and MRC-5 cells (IC50 > 50 µM). Different from MX781, WA15 eliminates RARα antagonist activity but inhibits 9-cis-RA-induced RXRα transactivation activity in a dose-dependent manner. Compound WA15 is found to be a good apoptosis inducer in various cancer cells and promotes cell apoptosis in an RXRα-independent manner. Besides, WA15 shows the induction of proteasome-dependent RXRα degradation which might enhance the WA15-induced apoptosis. Finally, the immunoblotting indicates that WA15 can inhibit the TNFα-induced IKK activation and IκBα degradation, suggesting that the anticancer activity of WA15 might be related to the inhibition of IKK/NF-κB signal pathway.

PtS2 nanosheets as a peroxidase-mimicking nanozyme for colorimetric determination of hydrogen peroxide and glucose.

Using an ultrasonication-assisted liquid exfoliation method, we have synthesized PtS2 nanosheets with good reproducibility. Herein, intrinsic peroxidase-like activity was for the first time demonstrated for PtS2 nanosheets, which can catalyze H2O2 oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) to generate a colored solution. The catalytic mechanism of PtS2 nanosheets was investigated, which indicated that acceleration of the electron transfer between TMB and H2O2 was the main reason for the peroxidase-like activity of PtS2 nanosheets. Based on these observations, we exploited PtS2 nanosheets integrated into dopamine-functionalized hyaluronic acid (HA-DA) hydrogel microspheres by droplet microfluidics to construct PtS2 nanosheet- and PtS2@HA-DA microsphere-based sensors for highly sensitive determination of H2O2. When coupled with glucose oxidase, we further developed two glucose sensors based on the above two methods. Among them, the linearity of the PtS2 nanosheet-based spectrophotometry was in the range of 0.5 to 150 µM and the limit of detection as low as 0.20 µM. The linearity of the microsphere-based colorimetry was in the range 200 to 12,000 µM with a detection limit of 29.95 µM. Both of the glucose sensors can be applied to the determination of glucose in human serum with reliable results and reproducibility.

Synthesis and discovery of ω-3 polyunsaturated fatty acid- alkanolamine (PUFA-AA) derivatives as anti-inflammatory agents targeting Nur77.

In this work, three series of ω-3 polyunsaturated fatty acid-alkanolamine derivatives (PUFA-AAs) were synthesized, characterized and their anti-inflammatory activity in vivo was evaluated. Compounds 4a, 4f, and 4k exhibited marked anti-inflammatory activity in LPS-stimulated RAW 264.7 cells. The most promising compound 4k dose-dependently suppressed the cytokines with IC50 values in the low micromolar range. Further, 4k exhibited potential in vitro Nur77-binding affinity (Kd = 6.99 × 10-6 M) which is consistent with the result of docking studies. Next, the anti-inflammatory mechanism of 4k was found to be through NF-κB signal pathway in a Nur77-dependent manner. Moreover, we also observed 4k significantly inhibited LPS-induced expression of cytokines (IL-6, TNF-α, and IL-1ß) through suppressing NF-κB activation and attenuated LPS-induced inflammation in mouse acute lung injury (ALI) model. In conclusion, the study strongly suggests that the PUFA-AA derivatives can be particularly as new Nur77 mediators for further treatment in inflammatory diseases.

Longitudinal associations between loneliness and online game addiction among undergraduates: A moderated mediation model.

Online gaming addiction (OGA) has become a noteworthy public concern, especially among university students, thereby requiring in-depth scrutiny. Although previous cross-sectional studies have established a positive link between loneliness and OGA, there is a lack of longitudinal evidence within this field. Moreover, the underlying mechanisms that elucidate the connection between loneliness and OGA are still poorly understood, underscoring a crucial research gap that requires further investigation. Our study utilized a longitudinal approach and developed a moderated mediation model. From September 2021 to September 2022, during the COVID-19 pandemic, data were collected from a sample of Chinese undergraduate students, with 367 participants at T1, 352 at T2, and 340 at T3. Participants completed online self-report surveys, providing valuable data on their levels of loneliness at T1, fear of missing out (FoMO) at T2, sensation seeking at T2, and OGA at T3. Our findings found that loneliness was positively linked to OGA. Further analysis demonstrated that FoMO partially mediated the link between loneliness and OGA. Meanwhile, sensation seeking moderated the pathway from FoMO to later OGA. Specifically, the effect of FoMO on OGA was significant for college students with high sensation seeking. This study offers longitudinal evidence linking loneliness and OGA, which has implications for the development of interventions to reduce OGA.

Ultrasmall CuMn-His Nanozymes with Multienzyme Activity at Neutral pH: Construction of a Colorimetric Sensing Array for Biothiol Detection and Disease Identification.

Biothiol assays offer vital insights into health assessment and facilitate the early detection of potential health issues, thereby enabling timely and effective interventions. In this study, we developed ultrasmall CuMn-Histidine (His) nanozymes with multiple enzymatic activities. CuMn-His enhanced peroxidase (POD)-like activity at neutral pH was achieved through hydrogen bonding and electrostatic effects. In addition, CuMn-His possesses laccase (LAC)-like and superoxide dismutase (SOD)-like activities at neutral pH. Based on three different enzyme mimetic activities of CuMn-His at neutral pH, the colorimetric sensing array without changing the buffer solution was successfully constructed. The array was successfully used for the identification of three biothiols, glutathione (GSH), cysteine (Cys), and homocysteine (Hcy). Subsequently, excellent application results were shown in complex serum and cellular level analyses. This study provides an innovative strategy for the development of ultrasmall bimetallic nanozymes with multiple enzymatic activities and the construction of colorimetric sensing arrays.

Multi-View Kernel Sparse Representation for Identification of Membrane Protein Types.

Membrane proteins are the main undertaker of biomembrane functions and play a vital role in many biological activities of organisms. Prediction of membrane protein types has a great help in determining the function of proteins and understanding the interactions of membrane proteins. However, the biochemical experiment is expensive and not suitable for the large-scale identification of membrane protein types. Therefore, computational methods were used to improve the efficiency of biological experiments. Most existing computational methods only use a single feature of protein, or use multiple features but do not integrate these well. In our study, the protein sequence is described via three different views (features), including amino acid composition, evolutionary information and physicochemical properties of amino acids. To exploit information among all views (features), we introduce a coupling strategy for Kernel Sparse Representation based Classification (KSRC) and construct a new model called Multi-view KSRC (MvKSRC). We implement our method on 4 benchmark data sets of membrane proteins. The comparison results indicate that our method is much superior to all existing methods.

MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins.

DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is a crucial issue. And it is greatly helpful to understand the mechanism of protein-DNA interactions. In traditional experimental methods, it is significant time-consuming and labor-consuming to identify DBPs. In recent years, many researchers have proposed lots of different DBP identification methods based on machine learning algorithm to overcome shortcomings mentioned above. However, most existing methods cannot get satisfactory results. In this paper, we focus on developing a new predictor of DBPs, called Multi-View Hypergraph Restricted Kernel Machines (MV-H-RKM). In this method, we extract five features from the three views of the proteins. To fuse these features, we couple them by means of the shared hidden vector. Besides, we employ the hypergraph regularization to enforce the structure consistency between original features and the hidden vector. Experimental results show that the accuracy of MV-H-RKM is 84.09% and 85.48% on PDB1075 and PDB186 data set respectively, and demonstrate that our proposed method performs better than other state-of-the-art approaches. The code is publicly available at https://github.com/ShixuanGG/MV-H-RKM.

Extractive electrospray ionization mass spectrometry for analytical evaluation and synthetic preparation of pharmaceutical chemicals.

Extraction electrospray ionization mass spectrometry (EESI-MS), due to the unique configuration of its ionization module, enables the effective ionization of trace molecules of interest in samples containing complex matrices with high sensitivity, high selectivity and high responding speed without requiring sample pretreatment, and allows high-energy molecular species to undergo specially designed reactions for advanced functionalization. The typical effects of operating conditions on the analytical performance of extraction electrospray ionization mass spectrometry for various pharmaceutical compounds, pharmaceutical preparations and herbal materials were systematically reviewed. The application prospect of extraction electrospray ionization in molecular functionalization for advanced drug discovery is also briefly introduced.

Identification of DNA-binding protein based multiple kernel model.

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/.

Design, synthesis, and biological evaluation of novel 4,4'-bipyridine derivatives acting as CDK9-Cyclin T1 protein-protein interaction inhibitors against triple-negative breast cancer.

Cyclin-dependent kinase 9 (CDK9) is directly related to tumor development in triple-negative breast cancer (TNBC) patients. Increased CDK9 is significantly associated with poor patient prognosis, while inhibiting CDK9-Cyclin T1 protein-protein interaction has recently been demonstrated as a new approach to TNBC treatment. Herein, we synthesized a novel class of 4,4'-bipyridine derivatives as potential CDK9-Cyclin T1 PPI inhibitors against TNBC. The represented compound B19 was found to be an excellent and selective CDK9-Cyclin T1 PPI inhibitor with good potency against TNBC cell lines while exhibiting lower toxicity in normal human cell lines than the positive compound I-CDK9. Notably, compound B19 showed good pharmacokinetic properties and excellent antitumor activity against TNBC (4T1) allografts in mice with a therapeutic index of more than 42 (TGI4T1(12.5 mg/kg,i.p.) = 63.1% vs. LD50 = 537 mg/kg). Moreover, the administration of B19 in combination with the PARP inhibitor Olaparib results in a significant increase of the antitumor activity in MDA-MB-231 cells relative to that of either single agent. To our knowledge, B19 is the first reported non-metal organic compound that acts as a selective CDK9-Cyclin T1 PPI inhibitor with in vivo antitumor activity, and it may be alone and in combination with PARP inhibitor Olaparib for TNBC therapy.

Discovery of 4-((3,4-dichlorophenyl)amino)-2-methylquinolin-6-ol derivatives as EGFR and HDAC dual inhibitors.

In patients with non-small cell lung cancer (NSCLC), the standard therapy consists of selective tyrosine kinase inhibitors that target epidermal growth factor receptors (EGFR). Nonetheless, their clinical utility is primarily limited by the development of resistance to drugs. HDAC inhibitors have been shown in studies to reduce the level of EGFR that is expressed and downregulate the EGFR-induced phosphorylation of AKT and ERK. Therefore, dual inhibitors of EGFR and HDAC provide a potential approach as combination treatment synergistically inhibited the growth of NSCLC. Herein, we examined the EGFR inhibition effect of twenty compounds which designed and synthesized by us previously. Among them, compounds 12c and 12d exhibited powerful antiproliferative activity against the NCI-H1975 cell line with IC50 values of 0.48 ± 0.07 and 0.35 ± 0.02 µM, correspondingly. In cell-free kinase assays, both 12c and 12d demonstrated target-specific EGFR inhibition against wild type (EGFRwt). Furthermore, the expression of EGFR and phosphorylation of the EGF-induced pathways were significantly suppressed under the treatment of 12c and 12d. Besides, both histones H3 and H4 exhibited increased levels of acetylation following 12c and 12d treatment. The animal experiments shown that 12d could prevent the growth of tumor, inhibited the expression of EGFR and the phosphorylation levels of p70 S6K, AKT and p38 MAPK in vivo, and did not cause organ damage to the mice during the experiment. Overall, the results illustrated that compound 12c and 12d could serve as effective EGFR and HDAC dual inhibitors in NSCLC cells. Our work offers an alternative strategy for NSCLC therapy.

Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.

Membrane proteins are an essential part of the body's ability to maintain normal life activities. Further research into membrane proteins, which are present in all aspects of life science research, will help to advance the development of cells and drugs. The current methods for predicting proteins are usually based on machine learning, but further improvements in prediction effectiveness and accuracy are needed. In this paper, we propose a dynamic deep network architecture based on lifelong learning in order to use computers to classify membrane proteins more effectively. The model extends the application area of lifelong learning and provides new ideas for multiple classification problems in bioinformatics. To demonstrate the performance of our model, we conducted experiments on top of two datasets and compared them with other classification methods. The results show that our model achieves high accuracy (95.3 and 93.5%) on benchmark datasets and is more effective compared to other methods.

Low-molecular-weight glutenin subunit LMW-N13 improves dough quality of transgenic wheat.

In our previous study, a novel LMW-GS designated as LMW-N13 with a unique molecular structure was identified from Aegilops uniaristata. LMW-N13 has been characterized as the largest LMW-GS, so far, and possesses an extra cysteine residue compared with typical LMW-GS. In order to analyze the contribution of LMW-N13 to dough quality, in this work, three transgenic wheat lines overexpressing LMW-N13 were generated. Compared with non-transformation (NT) lines, transgenic (TG) lines demonstrated superior dough properties. These superior dough properties were accompanied by the higher contents of glutenin macropolymer (GMP) and total protein. The microstructure of the dough was further investigated by scanning electron microscopy; starch granules in NT lines were smaller than those in transgenic lines. The protein matrix in NT lines was relatively loose and discontinuous. Conversely, the protein matrix in transgenic lines was more continuous and tight. The application of LMW-N13 in wheat breeding is also discussed.

Cystine dimethyl ester cross-linked PEG-poly(urethane-urea)/nano-hydroxyapatite composited biomimetic scaffold for bone defect repair.

Polyurethane (PU) and polyurea (PUA) materials have shown significant potential for application in tissue repair. Herein, we design a glycerol ethoxylate (PEG)-based poly(urethane-urea) for bone tissue repair. The polymer precursor was prepared from the reaction of PEG and isophorone diisocyanate (IPDI). The cystine dimethyl ester was used as a cross-linker for the preparation of poly(urethane-urea) elastomers. The material was further strengthened by physical blending of nano-hydroxyapatite (nHA). The physical and biological properties of final material were evaluated by mechanical testing, scanning electron microscopy characterization, degradation tests, cell proliferation and cell differentiation assays. The obtained scaffolds showed good mechanical strength, excellent biocompatibility and osteogenic capability. All the evidences demonstrated that this type of materials has good prospects for bone tissue repair application.

Recyclable Self-Healing Polyurethane Cross-Linked by Alkyl Diselenide with Enhanced Mechanical Properties.

Dynamic structures containing polymers can behave as thermosets at room temperature while maintaining good mechanical properties, showing good reprocessability, repairability, and recyclability. In this work, alkyl diselenide is effectively used as a dynamic cross-linker for the design of self-healing poly(urea-urethane) elastomers, which show quantitative healing efficiency at room temperature, without the need for any catalysts or external interventions. Due to the combined action of the urea bond and amide bond, the material has better mechanical properties. We also compared the self-healing effect of alkyl diselenide-based polyurethanes and alkyl disulfide-based polyurethanes. The alkyl diselenide has been incorporated into polyurethane networks using a para-substituted amine diphenyl alkyl diselenide. The resulting materials not only exhibit faster self-healing properties than the corresponding disulfide-based materials, but also show the ability to be processed at temperatures as low as 60 °C.