The emerging roles and therapeutic potential of cyclin-dependent kinase 11 (CDK11) in human cancer.

Overexpression and/or hyperactivation of cyclin-dependent kinases (CDKs) are common features of most cancer types. CDKs have been shown to play important roles in tumor cell proliferation and growth by controlling cell cycle, transcription, and RNA splicing. CDK4/6 inhibitor palbociclib has been recently approved by the FDA for the treatment of breast cancer. CDK11 is a serine/threonine protein kinase in the CDK family and recent studies have shown that CDK11 also plays critical roles in cancer cell growth and proliferation. A variety of genetic and epigenetic events may cause universal overexpression of CDK11 in human cancers. Inhibition of CDK11 has been shown to lead to cancer cell death and apoptosis. Significant evidence has suggested that CDK11 may be a novel and promising therapeutic target for the treatment of cancers. This review will focus on the emerging roles of CDK11 in human cancers, and provide a proof-of-principle for continued efforts toward targeting CDK11 for effective cancer treatment

An Empirical Study of Credit Risk Determinants in UK Banks

This paper explores the factors that affect banks' credit risk and looks at UK banks. The ratio of non-performing loans to gross loans is used to quantify credit risk as the dependent variable. Seven independent variables were selected, of which bank capital, size and profitability were used as bank-level independent variables, and exchange rate, GDP growth, inflation rate and unemployment were used as macroeconomic-level independent variables. In this study, 119 banks in the UK were selected as samples, spanning the period 2016 to 2020. Regression analysis was conducted using panel data and concluded that bank capital, size and exchange rate are positively and statistically significantly related to credit risk. Yield rate, GDP growth and unemployment are significantly negatively related to credit risk. In addition, the coefficient of the inflation rate is not significant

Sequential emitter identification method based on D-S evidence theory

This paper proposes a novel sequential identification method for enhancing the anti-jamming performance and for accurate recognition rate of the emitters’ individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information

Mimicking Intermolecular Interactions of Tight Protein–Protein Complexes for Small-Molecule Antagonists

Tight protein–protein interactions (Kd1000 Å2) are highly challenging to disrupt with small molecules. Historically, the design of small molecules to inhibit protein–protein interactions has focused on mimicking the position of interface protein ligand side chains. Here, we explore mimicry of the pairwise intermolecular interactions of the native protein ligand with residues of the protein receptor to enrich commercial libraries for small-molecule inhibitors of tight protein–protein interactions. We use the high-affinity interaction (Kd=1 nm) between the urokinase receptor (uPAR) and its ligand urokinase (uPA) to test our methods. We introduce three methods for rank-ordering small molecules docked to uPAR: 1) a new fingerprint approach that represents uPA′s pairwise interaction energies with uPAR residues; 2) a pharmacophore approach to identify small molecules that mimic the position of uPA interface residues; and 3) a combined fingerprint and pharmacophore approach. Our work led to small molecules with novel chemotypes that inhibited a tight uPAR⋅uPA protein–protein interaction with single-digit micromolar IC50 values. We also report the extensive work that identified several of the hits as either lacking stability, thiol reactive, or redox active. This work suggests that mimicking the binding profile of the native ligand and the position of interface residues can be an effective strategy to enrich commercial libraries for small-molecule inhibitors of tight protein–protein interactions

Generation of diffuse large B cell lymphoma-associated antigen-specific Vα6/Vβ13+T cells by TCR gene transfer

<p>Abstract</p> <p>Background</p> <p>Our previous study had amplified antigen-specific full-length TCR α and β genes of clonally expanded T cells in the peripheral blood (PB) of patients with diffuse large B-cell lymphoma (DLBCL). The transfer of T cell receptor (TCR) genes endows T cells with new antigen specificity. Therefore, the aim of this study is to generate diffuse large B cell lymphoma (DLBCL)-specific T cells by T cell receptor (TCR) gene transfer.</p> <p>Materials and methods</p> <p>Two different eukaryotic expression plasmids harboring TCR Vα6 and TCR Vβ13 genes specific for DLBCL-associated antigens were constructed and subsequently transferred into human T cells using Nucleofector™ technique. The expression of targeted genes in TCR gene-modified cells was detected by real-time PCR, and western blot using TCR Vβ antibody. The specific cytotoxicity of TCR gene-transferred T cells <it>in vitro </it>was estimated using a lactate dehydrogenase (LDH) release assay.</p> <p>Results</p> <p>Two different eukaryotic expression plasmids harboring TCR Vα6 and TCR Vβ13 genes specific for DLBCL-associated antigens were constructed and subsequently transferred into T cells from healthy donors. Specific anti-DLBCL cytotoxic T lymphocytes (CTL) could be induced by transduction of specific TCR gene to modify healthy T cells. The transgene cassette of TCR Vβ13-IRES-TCR Vα6 was superior to the other in the function of TCR-redirected T cells.</p> <p>Conclusions</p> <p>Specific anti-DLBCL cytotoxic T lymphocyte (CTL) could be inducted by transduction of specific TCR gene to modify healthy T cells.</p

A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein–Protein Interaction Interfaces

The binding affinity of a protein–protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein–protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein–protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein–protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein–protein interactions do not optimally mimic protein–ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials

Small-molecule CaVα1⋅CaVβ antagonist suppresses neuronal voltage-gated calcium-channel trafficking

Extracellular calcium flow through neuronal voltage-gated CaV2.2 calcium channels converts action potential-encoded information to the release of pronociceptive neurotransmitters in the dorsal horn of the spinal cord, culminating in excitation of the postsynaptic central nociceptive neurons. The CaV2.2 channel is composed of a pore-forming α1 subunit (CaVα1) that is engaged in protein-protein interactions with auxiliary α2/δ and β subunits. The high-affinity CaV2.2α1⋅CaVβ3 protein-protein interaction is essential for proper trafficking of CaV2.2 channels to the plasma membrane. Here, structure-based computational screening led to small molecules that disrupt the CaV2.2α1⋅CaVβ3 protein-protein interaction. The binding mode of these compounds reveals that three substituents closely mimic the side chains of hot-spot residues located on the α-helix of CaV2.2α1 Site-directed mutagenesis confirmed the critical nature of a salt-bridge interaction between the compounds and CaVβ3 Arg-307. In cells, compounds decreased trafficking of CaV2.2 channels to the plasma membrane and modulated the functions of the channel. In a rodent neuropathic pain model, the compounds suppressed pain responses. Small-molecule α-helical mimetics targeting ion channel protein-protein interactions may represent a strategy for developing nonopioid analgesia and for treatment of other neurological disorders associated with calcium-channel trafficking

Possible interpretation of the ZbZ_b(10610) and ZbZ_b(10650) in a chiral quark model

Motivated by the two charged bottomonium-like resonances $Z_b$(10610) and $Z_b$(10650) newly observed by the Belle collaboration, the possible molecular states composed of a pair of heavy mesons, $B\bar{B}, B\bar{B}^*, B^*\bar{B}^*, B_s\bar{B}$, etc (in S-wave), are investigated in the framework of chiral quark models by the Gaussian expansion method. The bound states $B\bar{B}^*$ and $B^*\bar{B}^*$ with quantum numbers $I(J^{PC})=1(1^{+-})$, which are good candidates for the $Z_b(10610)$ and $Z_b(10650)$ respectively, are obtained. Other three bound states $B\bar{B}^*$ with $I(J^{PC})=0(1^{++})$, $B^*\bar{B}^*$ with $I(J^{PC})=1(0^{++}), 0(2^{++})$ are predicted. These states may be observed in open-bottom or hidden-bottom decay channel of highly excited $\Upsilon$. When extending directly the quark model to the hidden color channel of the multi-quark system, more deeply bound states are found. Future experimental search of those states will cast doubt on the validity of applying the chiral constituent quark model to the hidden color channel directly.Comment: 13 pages, 1 figure, title and some arguments in the abstract and section 5 are revised, results unchange

A prediction strategy based on decision variable analysis for dynamic multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Many multi-objective optimization problems in reality are dynamic, requiring the optimization algorithm to quickly track the moving optima after the environment changes. Therefore, response strategies are often used in dynamic multi-objective algorithms to find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on the classification of decision variables, which consists of three steps. After detecting the environment change, the first step is to analyze the influence of each decision variable on individual convergence and distribution in the new environment. The second step is to adopt different prediction methods for different decision variables. Finally, adaptive selection is applied to the solution set generated in the first and second steps, and solutions with good convergence and diversity are selected to make the initial population more adaptable to the new environment. The prediction strategy can help the solution set converge while maintaining its diversity. The experimental results and performance show that the proposed algorithm is capable of significantly improving the dynamic optimization performance compared with five state-of-the-art evolutionary algorithms