Computational-Driven Epitope Verification and Affinity Maturation of TLR4-Targeting Antibodies

Toll-like receptor (TLR) signaling plays a critical role in the induction and progression of autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematous, experimental autoimmune encephalitis, type 1 diabetes mellitus and neurodegenerative diseases. Deciphering antigen recognition by antibodies provides insights and defines the mechanism of action into the progression of immune responses. Multiple strategies, including phage display and hybridoma technologies, have been used to enhance the affinity of antibodies for their respective epitopes. Here, we investigate the TLR4 antibody-binding epitope by computational-driven approach. We demonstrate that three important residues, i.e., Y328, N329, and K349 of TLR4 antibody binding epitope identified upon in silico mutagenesis, affect not only the interaction and binding affinity of antibody but also influence the structural integrity of TLR4. Furthermore, we predict a novel epitope at the TLR4-MD2 interface which can be targeted and explored for therapeutic antibodies and small molecules. This technique provides an in-depth insight into antibody–antigen interactions at the resolution and will be beneficial for the development of new monoclonal antibodies. Computational techniques, if coupled with experimental methods, will shorten the duration of rational design and development of antibody therapeutics.     

PRP4 Promotes Skin Cancer by Inhibiting Production of Melanin,Blocking Influx of Extracellular Calcium,and Remodeling Cell Actin Cytoskeleton

Pre-mRNA processing factor 4B (PRP4) has previously been shown to induce epithelial-mesenchymal transition (EMT) and drug resistance in cancer cell lines. As melanin plays an important photoprotective role in the risk of sun-induced skin cancers, we have investigated whether PRP4 can induce drug resistance and regulate melanin biosynthesis in a murine melanoma (B16F10) cell line. Cells were incubated with a crucial melanogenesis stimulator, alpha-melanocyte-stimulating hormone, followed by transfection with PRP4. This resulted in the inhibition of the production of melanin via the downregulation of adenylyl cyclase-cyclic adenosine 3′,5′-monophosphate (AC)–(cAMP)–tyrosinase synthesis signaling pathway. Inhibition of melanin production by PRP4 leads to the promotion of carcinogenesis and induced drug resistance in B16F10 cells. Additionally, PRP4 overexpression upregulated the expression of β-arrestin 1 and desensitized the extracellular calcium-sensing receptor (CaSR), which in turn, inhibited the influx of extracellular Ca²⁺ ions. The decreased influx of Ca²⁺ was confirmed by a decreased expression level of calmodulin. We have demonstrated that transient receptor potential cation channel subfamily C member 1 was involved in the influx of CaSR-induced Ca²⁺ via a decreasing level of its expression. Furthermore, PRP4 overexpression downregulated the expression of AC, decreased the synthesis of cAMP, and modulated the actin cytoskeleton by inhibiting the expression of Ras homolog family member A (RhoA). Our investigation suggests that PRP4 inhibits the production of melanin in B16F10 cells, blocks the influx of Ca²⁺ through desensitization of CaSR, and modulates the actin cytoskeleton through downregulating the AC–cAMP pathway; taken together, these observations collectively lead to the promotion of skin carcinogenesis.     

Differential Regulation of Interferon Signaling Pathways in CD4+ T Cells of the Low Type-2 Obesity-Associated Asthma Phenotype

In the era of personalized medicine, insights into the molecular mechanisms that differentially contribute to disease phenotypes, such as asthma phenotypes including obesity-associated asthma, are urgently needed. Peripheral blood was drawn from 10 obese, non-atopic asthmatic adults with a high body mass index (BMI; 36.67 ± 6.90); 10 non-obese, non-atopic asthmatic adults with normal BMI (23.88 ± 2.73); and 10 healthy controls with normal BMI (23.62 ± 3.74). All asthmatic patients were considered to represent a low type-2 asthma phenotype according to selective clinical parameters. RNA sequencing (RNA-Seq) was conducted on peripheral blood CD4⁺ T cells. Thousands of differentially expressed genes were identified in both asthma groups compared with heathy controls. The expression of interferon (IFN)-stimulated genes associated with IFN-related signaling pathways was specifically affected in obese asthmatics, while the gap junction and G protein-coupled receptor (GPCR) ligand binding pathways were enriched in both asthma groups. Furthermore, obesity gene markers were also upregulated in CD4⁺ T cells from obese asthmatics compared with the two other groups. Additionally, the enriched genes of the three abovementioned pathways showed a unique correlation pattern with various laboratory and clinical parameters. The specific activation of IFN-related signaling and viral infection pathways might provide a novel view of the molecular mechanisms associated with the development of the low type-2 obesity-associated asthma phenotype, which is a step ahead in the development of new stratified therapeutic approaches.     

Optimized structure and electrochemical properties of sulfonated carbon nanotubes/Co–Ni bimetallic layered hydroxide composites for high-performance supercapacitors

Technical development in electronic devices is frequently stifled by their insufficient capacity and cyclic stability of energy-storage devices. The nano-structured materials have sensational importance for providing novel and optimized combination to overcome exiting boundaries and provide efficient energy storage systems. Metal hydroxide materials with high capacity for pseudo-capacitance properties have grabbed special attention. Lately, the blend of nickel and cobalt hydroxides has been considered as a favorable class of metallic hydroxide materials owing to their comparatively high capacitance and exceptional redox reversibility. The sulfonated carbon nanotube fluid (SCNTF) was prepared by the ion exchange method to be utilized as the exceptional templates due to astonishing specific surface area, ensuring the maximum utilization of the active material. The CoNi-layered double hydroxides (LDHs)/SCNTF core-shell nanocomposite was prepared by the simple solvothermal method. Structural analysis showed that the composite material had the high conductance of carbon materials, the pseudo-capacitance characteristics of metal hydroxides, and porous structure, which facilitates the ion shuttle when the electrolyte reacts with the active material. Electrochemical analysis results showed that CoNi-LDHs/SCNTF had excellent rate performance, reversible charge-discharge properties and cycle stability. It exhibited an extreme specific capacity of 1190.5 F g^?1 at a current density of 1 A g^?1; whereas specific capacity remained 953.7 F g^?1 at the current density was 10 A g^?1. In addition, the capacity retention rate after 5000 charge-discharge cycles at a current density of 20 A g^?1 was 81.0%. The results indicated that the CoNi-LDHs/SCNTF core-shell nanocomposite material is cost efficient and an effective substitute in energy storage applications.     

Optimization of Microchannel Heat Sinks Using Genetic Algorithm

In this study, a genetic algorithm is employed to minimize the entropy generation rate in microchannel heat sinks. The entropy generation rate allows the combined effects of thermal performance and pressure drop to be assessed simultaneously as the heat sink interacts with the surrounding flow field. Previously developed models for the heat transfer, pressure drop and entropy generation rate are used in the optimization procedure. The results of optimization are compared with existing results obtained by the Newton–Raphson method. It is observed that the GA gives better overall performance of the microchannel heat sinks.     

Prediction of Evaporation Losses in Wet Cooling Towers

The accurate prediction of all aspects of cooling tower behavior is very important. Accurately predicting evaporation losses is significant because water in cooling towers is cooled primarily through the evaporation of a portion of the circulating water, which causes the concentration of dissolved solids and other impurities to increase. An empirical relation is developed on the basis of ASHRAE's rule of thumb that is simple and accurate with a wide range of applicability. The predicted values are in good agreement with experimental data as well as predictions made by an accurate mathematical model.     

A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems

This paper presents a new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems which is based on the idea of adaptive learning approach and simulated annealing. The proposed approach uses a weight parameter to perturb task priorities of a solution to obtain improved solutions. The weight parameters are then modified using a learning strategy. The maximization of line efficiency (i.e., the minimization of the number of stations) and the equalization of workloads among stations (i.e., the minimization of the smoothness index or the minimization of the variation of workloads) are considered as the performance criteria. In order to clarify the proposed solution methodology, a well known problem taken from literature is solved. A computational study is conducted by solving a large number of benchmark problems available in the literature to compare the performance of the proposed approach to the existing methods such as simulated annealing and genetic algorithms. Some test instances taken from literature are also solved by the proposed approach. The results of the computational study show that the proposed approach performs quite effectively. It also yields optimal solutions for all test problems within a short computational time.     

An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules

In this paper, a genetic algorithm (GA) is proposed as a search strategy for not only positive but also negative quantitative association rule (AR) mining within databases. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed GA performs a database-independent approach that does not rely upon the minimum support and the minimum confidence thresholds that are hard to determine for each database. Instead of randomly generated initial population, uniform population that forces the initial population to be not far away from the solutions and distributes it in the feasible region uniformly is used. An adaptive mutation probability, a new operator called uniform operator that ensures the genetic diversity, and an efficient adjusted fitness function are used for mining all interesting ARs from the last population in only single run of GA. The efficiency of the proposed GA is validated upon synthetic and real databases.