Spin Mixing Control of Interlayer Excitons in ZrS₂/ZrNCl Heterostructures

Two-dimensional (2D) transition metal dichalcogenide heterostructures provide a fruitful platform for realizing interlayer excitons (IXs) with spatial separation between the charges. When Janus layers stack into the heterostructures, an extra electric field is introduced to manipulate the properties of the IXs. Here, we demonstrate how the intrinsic electric field of the Janus monolayer ZrNCl affects the IXs in the ZrS2/ZrNCl heterostructures via GW-BSE calculations. Our findings show that both stacking orders 1-S/N and 2-S/Cl with different interfaces exhibit tightly bound bright IXs. Additionally, the dominant excitonic absorption peak arising from interlayer excitation of 1-S/N is located in the infrared range, whereas it is in the visible range in 2-S/Cl. The radiative lifetime of IXs can also be tuned from 10–4 to 10–8 s at 300 K by the stacking order. By analyzing the spin properties, we find that the lowest-energy exciton IX0 of 1-S/N mixes much more spin-singlet states than that of 2-S/Cl and thus has a shorter lifetime. However, for bright exciton IXB, the dominant transition process in 1-S/N is spin-forbidden but dipole-allowed, while it is spin- and dipole-allowed in 2-S/Cl; therefore, the lifetime of bright excitons in 2-S/Cl is shorter than that of 1-S/N. Interestingly, the multiband transition characteristic of IXs in ZrS2/ZrNCl arising from the high energy degeneracy is favorable for satisfying dipole transition selection rules. Our study demonstrates the significance of the degree of mixing of spin-singlet and spin-triplet states, combined with the multiband transition feature, to the lifetime of IX by altering the stacking order of the heterostructures with the Janus layer

Table1_EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data.XLSX

Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.</p

Electronic and Optical Properties of Graphene Quantum Dots: The Role of Many-Body Effects

The electronic structure and optical properties of hexagonal armchair and zigzag-edged graphene quantum dots (GQDs) are investigated within the framework of many-body perturbation theory. Many-body effects are significant due to quantum confinement and reduced screening. The quasi-particle corrections and exciton binding energies can be several eV, much larger than those of other carbon allotropes with higher dimensionality. All the GQDs show similar absorption spectra when electron–hole interaction is included, with a prominent peak emerging below the absorption onset of the noninteracting spectrum. This peak is contributed by a pair of double-degenerate excited states originating from the transitions between degenerate frontier orbitals. The spin singlet–triplet splitting is closely related to the electron–hole overlap, which can be approximately measured by the overlap between frontier orbitals involved in the optical transitions. The strong many-body effects in GQDs should be of great importance in optoelectronic applications

Expanding Pore Size of Al-BDC Metal–Organic Frameworks as a Way to Achieve High Adsorption Selectivity for CO₂/CH₄ Separation

The mesostructured Al-BDC metal–organic frameworks (MOFs) with an average pore size of 2.58 nm were prepared via a simplified washing and drying process and applied to the separation of CO₂/CH₄ mixtures. The adsorption equilibrium and thermodynamics of CH₄ and CO₂ were studied in the dynamic processes by the volumetric–chromatographic and inverse gas chromatographic (IGC) methods. The experiments represent that the Al-BDC MOF with large pore size has a much higher CO₂/CH₄ selectivity of ca. 24 at 303 K in the pressure range 0–1.0 MPa and therefore appears to be a good candidate for the separation of CH₄ from CO₂. The initial heats of adsorption of CH₄ and CO₂ on the mesostructured Al-BDC MOFs were determined to be 11.5 and 25.2 kJ mol^–1 by the IGC method, respectively, which are significantly reduced by ca. 25% compared with that on the microporous Al-BDC MOFs. The results indicate that the expanded pore size not only greatly increases the selectivity of CO₂ adsorption over CH₄ but also reduces the adsorption heat, revealing that it should be the desired method to obtain a satisfactory absorbent for CO₂/CH₄ separation

Table2_EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data.XLSX

Accurate identification of Drug Target Interactions (DTIs) is of great significance for understanding the mechanism of drug treatment and discovering new drugs for disease treatment. Currently, computational methods of DTIs prediction that combine drug and target multi-source data can effectively reduce the cost and time of drug development. However, in multi-source data processing, the contribution of different source data to DTIs is often not considered. Therefore, how to make full use of the contribution of different source data to predict DTIs for efficient fusion is the key to improving the prediction accuracy of DTIs. In this paper, considering the contribution of different source data to DTIs prediction, a DTIs prediction approach based on an effective fusion of drug and target multi-source data is proposed, named EFMSDTI. EFMSDTI first builds 15 similarity networks based on multi-source information networks classified as topological and semantic graphs of drugs and targets according to their biological characteristics. Then, the multi-networks are fused by selective and entropy weighting based on similarity network fusion (SNF) according to their contribution to DTIs prediction. The deep neural networks model learns the embedding of low-dimensional vectors of drugs and targets. Finally, the LightGBM algorithm based on Gradient Boosting Decision Tree (GBDT) is used to complete DTIs prediction. Experimental results show that EFMSDTI has better performance (AUROC and AUPR are 0.982) than several state-of-the-art algorithms. Also, it has a good effect on analyzing the top 1000 prediction results, while 990 of the first 1000DTIs were confirmed. Code and data are available at https://github.com/meng-jie/EFMSDTI.</p

Insights into the Importance of DFD-Motif and Insertion I1 in Stabilizing the DFD-Out Conformation of Mnk2 Kinase

Human mitogen-activated protein kinases (MAPK)-interacting kinases 1 and 2 (Mnk1/2) are promising anticancer targets. Mnks possess special insertions and a DFD-motif that are distinct from other kinases. Crystallographic studies of Mnk1/2 have revealed that the DFD-motif adopts the DFG/D-out conformation in which residue F227 flips into the ATP binding pocket. This is rarely observed in other kinases. Although the DFG-out conformation has attracted great interest for designing selective inhibitors, structural requirements for binding and the mechanism governing the DFG-out conformation remain unclear. This work presents for the first time the applicability of 3D models of Mnk2 protein in studying conformational changes by utilizing homology modeling and molecular dynamics simulations. The study reveals that the interactions between residue K234 of insertion I1 and D226 of the DFD motif play a key role in inducing and stabilizing the DFD-out conformation. The structural features will aid in the rational design of Mnk2 inhibitors

Insights into the Importance of DFD-Motif and Insertion I1 in Stabilizing the DFD-Out Conformation of Mnk2 Kinase

Human mitogen-activated protein kinases (MAPK)-interacting kinases 1 and 2 (Mnk1/2) are promising anticancer targets. Mnks possess special insertions and a DFD-motif that are distinct from other kinases. Crystallographic studies of Mnk1/2 have revealed that the DFD-motif adopts the DFG/D-out conformation in which residue F227 flips into the ATP binding pocket. This is rarely observed in other kinases. Although the DFG-out conformation has attracted great interest for designing selective inhibitors, structural requirements for binding and the mechanism governing the DFG-out conformation remain unclear. This work presents for the first time the applicability of 3D models of Mnk2 protein in studying conformational changes by utilizing homology modeling and molecular dynamics simulations. The study reveals that the interactions between residue K234 of insertion I1 and D226 of the DFD motif play a key role in inducing and stabilizing the DFD-out conformation. The structural features will aid in the rational design of Mnk2 inhibitors

Lipophilic Prodrugs of SN38: Synthesis and in Vitro Characterization toward Oral Chemotherapy

SN38 (7-ethyl-10-hydroxy camptothecin) is a potent anticancer agent belonging to the camptothecin family; however, its oral delivery is extensively restricted by poor solubility in pharmaceutically acceptable excipients and low transmucosal permeability. Lipid-based carriers are well-known for their ability to improve oral absorption and bioavailability of lipid soluble and highly permeable compounds. Thus, this study has focused on improving solubility in lipid excipients, controlling stability, and enhancing transmucosal permeability of SN38 by specific chemical modification. To achieve these aims, a series of lipophilic prodrugs were designed and synthesized by esterification at the C₁₀ and/or C₂₀ positon(s) of SN38 with dietary fatty acids of diverse hydrocarbon chain lengths. The solubility of these novel prodrugs in long-chain triglycerides was increased up to 444-fold, and cytotoxicity was significantly reduced in comparison to SN38. The prodrugs were stable in simulated gastric fluids but exhibited different rates of hydrolysis (<i>t</i>_1/2 < 5 min to <i>t</i>_1/2 > 2 h) in simulated intestinal fluids (in the presence of enzymes) depending on the alkyl chain length and the position modified. A predictable reconversion of prodrugs to SN38 in plasma was also confirmed. On the basis of these studies, SN38-undecanoate (C₂₀) was identified as the optimal prodrug. Finally, in vitro permeability and uptake studies in rat intestinal mucosal membrane using an Ussing chamber showed significant improvement in transepithelial drug transport and cellular uptake. Together, these results indicate that well designed lipophilic prodrugs have potential for the efficacious and safe oral delivery of SN38

In Search of Novel CDK8 Inhibitors by Virtual Screening

Aberrant activity of cyclin-dependent kinase (CDK) 8 is implicated in various cancers. While CDK8-targeting anticancer drugs are highly sought-after, no CDK8 inhibitor has yet reached clinical trials. Herein a large library of drug-like molecules was computationally screened using two complementary cascades to identify potential CDK8 inhibitors. Thirty-three hits were identified to inhibit CDK8 and seven of them were active against colorectal cancer cell lines. Finally, the primary target was confirmed using three promising hits

Comparative Structural and Functional Studies of 4‑(Thiazol-5-yl)-2-(phenylamino)pyrimidine-5-carbonitrile CDK9 Inhibitors Suggest the Basis for Isotype Selectivity

Cyclin-dependent kinase 9/cyclin T, the protein kinase heterodimer that constitutes positive transcription elongation factor b, is a well-validated target for treatment of several diseases, including cancer and cardiac hypertrophy. In order to aid inhibitor design and rationalize the basis for CDK9 selectivity, we have studied the CDK-binding properties of six different members of a 4-(thiazol-5-yl)-2-(phenylamino)­pyrimidine-5-carbonitrile series that bind to both CDK9/cyclin T and CDK2/cyclin A. We find that for a given CDK, the melting temperature of a CDK/cyclin/inhibitor complex correlates well with inhibitor potency, suggesting that differential scanning fluorimetry (DSF) is a useful orthogonal measure of inhibitory activity for this series. We have used DSF to demonstrate that the binding of these compounds is independent of the presence or absence of the C-terminal tail region of CDK9, unlike the binding of the CDK9-selective inhibitor 5,6-dichlorobenzimidazone-1-β-d-ribofuranoside (DRB). Finally, on the basis of 11 cocrystal structures bound to CDK9/cyclin T or CDK2/cyclin A, we conclude that selective inhibition of CDK9/cyclin T by members of the 4-(thiazol-5-yl)-2-(phenylamino)­pyrimidine-5-carbonitrile series results from the relative malleability of the CDK9 active site rather than from the formation of specific polar contacts