Nox1 and Nox4, homologues of the leukocyte NADPH oxidase subunit Nox2 (gp91phox) mediate superoxide anion formation in various cell types. However, their interactions with other components of the NADPH oxidase are poorly defined. We determined whether a direct interaction of Nox1 and Nox4 with the p22phox subunit of the NADPH oxidase occurs. Using confocal microscopy, co-localization of p22phox with Nox1, Nox2, and Nox4 was observed in transiently transfected vascular smooth muscle cells (VSMC) and HEK293 cells. Plasmids coding for fluorescent fusion proteins of p22phox and the Nox proteins with cyan-and yellowfluorescent protein (cfp and yfp, respectively) were constructed and expressed in VSMC and HEK293 cells. The cfp-tagged p22phox expression level increased upon cotransfection with Nox1 or Nox4. Protein-protein interaction between the fluorescent fusion proteins of p22phox and the Nox partners was observed using the fluorescence resonance energy transfer technique. Immunoprecipitation of native Nox1 from human VSMC revealed co-precipitation of p22phox. Immunoprecipitation from transfected HEK293 cells revealed co-precipitation of native p22phox with yfp-tagged Nox1, Nox2, and Nox4. Following mutation of a histidine (corresponding to the position 115 in human Nox2) to leucine, this interaction was abolished. Transfection of rat p22phox (but not Noxo1 and Noxa1) increased the radical generation in cells expressing Nox4. We provide evidence that p22phox directly interacts with Nox1 and Nox4, to form an superoxide-generating NADPH oxidase and demonstrate that mutation of the potential heme binding site in the Nox proteins disrupts the complex formation of Nox1 and Nox4 with p22phox.

Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

In contrast with the class A heat stress transcription factors (HSFs) of plants, a considerable number of HSFs assigned to classes B and C have no evident function as transcription activators on their own. However, in the following article, we provide evidence that tomato (Lycopersicon peruvianum) HsfB1 represents a novel type of coactivator cooperating with class A HSFs (e.g., with tomato HsfA1). Provided the appropriate promoter architecture, the two HSFs assemble into an enhanceosome-like complex, resulting in strong synergistic activation of reporter gene expression. Moreover, HsfB1 also cooperates in a similar manner with other activators, for example, with the ASF1/2 enhancer binding proteins of the 35S promoter of Cauliflower mosaic virus or with yet unidentified activators controlling housekeeping gene expression. By these effects, HsfB1 may help to maintain and/or restore expression of certain viral or housekeeping genes during ongoing heat stress. The coactivator function of HsfB1 depends on a histone-like motif in its C-terminal domain with an indispensable Lys residue in the center (GRGKMMK). This motif is required for recruitment of the plant CREB binding protein (CBP) ortholog HAC1. HsfA1, HsfB1, and HAC1/CBP form ternary complexes in vitro and in vivo with markedly enhanced efficiency in promoter recognition and transcription activation in plant and mammalian (COS7) cells. Using small interfering RNA–mediated knock down of HAC1 expression in Arabidopsis thaliana mesophyll protoplasts, the crucial role for the coactivator function of HsfB1 was confirmed.

Intraneuronal ␤-amyloid (A␤ i ) accumulates early in Alzheimer's disease (AD) and inclusion body myositis. Several organelles, receptor molecules, homeostatic processes, and signal transduction components have been identified as sensitive to A␤. Although prior studies implicate the insulin-PI3K-Akt signaling cascade, a specific step within this or any essential metabolic or survival pathway has not emerged as a molecular target. We tested the effect of A␤42 on each component of this cascade. In AD brain, the association between PDK and Akt, phospho-Akt levels and its activity were all decreased relative to control. In cell culture, A␤ i expression inhibited both insulin-induced Akt phosphorylation and activity. In vitro experiments identified that ␤-amyloid (A␤), especially oligomer preparations, specifically interrupted the PDK-dependent activation of Akt. A␤ i also blocked the association between PDK and Akt in cell-based and in vitro experiments. Importantly, A␤ did not interrupt Akt or PI3K activities (once stimulated) nor did it affect more proximal signal events. These results offer a novel therapeutic strategy to neutralize A␤-induced energy failure and neuronal death. INTRODUCTIONThere is widening recognition that Alzheimer's disease (AD) is closely linked to a state of relative insulin resistance in the brain, so-called "type III diabetes" (de la Monte et al., 2006). Levels of insulin-like growth factor I (IGF-I), insulin, and/or cognate receptors are dysregulated in AD brain (Messier and Teutenberg, 2005;Moloney et al., 2008). In normal brain, IGF-I and insulin promote glucose utilization, energy metabolism, and neuronal survival (Hoyer, 2004), largely through PI3K/Akt/GSK-3␤ signaling (Bondy and Cheng, 2004). Insulin receptors (IRs) populate neuronal synapses and astrocytes in memory-processing brain regions (Lee et al., 2005). Acute insulin treatment increased memory function in rats on a passive-avoidance task (Park et al., 2000) and in small studies involving normal adults and AD patients (Craft and Watson, 2004), consistent with positive effects on synaptic plasticity (Horwood et al., 2006). How ␤-amyloid, a major culprit neurotoxin in AD, could cause central insulin resistance is unknown.Intraneuronal ␤-amyloid (A␤i) in particular, is a significant factor in the early pathogenesis of AD LaFerla et al., 2007). Inclusion body myositis (IBM), another disorder associated with intracellular ␤-amyloid (A␤) deposits, is a major cause of skeletal muscle inflammation and degeneration in the elderly. Cytoplasmic A␤ induces programmed cell death (apoptosis) in a number of experimental and transgenic models (Magrane et al., 2005;Oakley et al., 2006).Interference with or alteration of the Akt signaling pathway has emerged as an important feature in several neurodegenerative diseases characterized by neuronal attrition including AD and schizophrenia (Emamian et al., 2004;Griffin et al., 2005). In previous studies of the effects of intracellular A␤ on this pathway using primary cortical neurons as well as Tg257...