The direct fusion of viral and target cell membranes required for human immunodeficiency virus type 1 (HIV-1) entry is initiated by the primary receptor, CD4, and a chemokine receptor, usually CXCR4 or CCR5. Chemokine receptors are members of the G-protein-coupled receptor (GPCR) superfamily that possess seven transmembrane (TM) domains. Because of its importance in the development of AIDS, CXCR4 has been explored as a new target for drug discovery to combat the AIDS epidemic (3,8,10). As the natural ligands of chemokine receptors, chemokines are small soluble proteins of about 70 amino acid residues that play prominent roles in leukocyte activation and inflammation (5, 11). Most of the known human chemokines are broadly categorized into the CXC and CC chemokines based on the position of two conserved cysteine residues in their amino (N)-terminal domains (3, 11). The natural chemokines of CXCR4 or CCR5 can inhibit HIV-1 infection (4, 13) by blocking HIV-1 gp120 binding sites (2, 14) and/or inducing receptor internalization (1, 9).Despite their important roles in the pathogenesis of AIDS and other human diseases, the lack of receptor selectivity of natural chemokines has made their direct clinical applications problematic. It is common knowledge that a chemokine receptor can often be recognized by multiple ligands, while a chemokine ligand binds to several different receptors (15), illustrating the apparent redundancy and the lack of selectivity in the chemokine ligand-receptor interaction network. As such, we have been working toward the development of a systematic chemical biology approach based on chemokine protein structures and chemistry to generate synthetically and modularly modified (SMM) chemokines that have higher receptor binding selectivity and improved pharmacological profiles compared with natural chemokines. This SMM chemokine approach was recently applied to generate novel ligands selective for CXCR4 or CCR5 by modifying the N-terminal (1-10) sequence module of viral macrophage inflammatory protein II (vMIP-II) or stromal cell-derived factor 1␣ (SDF-1␣) (unpublished data). Importantly, some of these SMM chemokines,
The stress-activated p38 mitogen-activated protein kinase (p38 MAPK), a member of the subgroup of mammalian kinases, appears to play an important role in regulating inflammatory responses, including cytokine secretion and apoptosis. The upstream mediators that link extracellular signals with the p38 MAPK signaling pathway are currently unknown. Here we demonstrate that pp125 focal adhesion kinase-related tyrosine kinase RAFTK (also known as PYK2, CADTK) is activated specifically by methylmethane sulfonate (MMS) and hyperosmolarity but not by ultraviolet radiation, ionizing radiation, or cis-platinum. Overexpression of RAFTK leads to the activation of p38 MAPK. Furthermore, overexpression of a dominant-negative mutant of RAFTK (RAFTK K-M) inhibits MMS-induced p38 MAPK activation. MKK3 and MKK6 are known potential constituents of p38 MAPK signaling pathway, whereas SEK1 and MEK1 are upstream activators of SAPK/JNK and ERK pathways, respectively. We observe that the dominantnegative mutant of MKK3 but not of MKK6, SEK1, or MEK1 inhibits RAFTK-induced p38 MAPK activity. Furthermore, the results demonstrate that treatment of cells with 1,2-bis(2-aminophenoxy)ethane-N,N,N,N-tetraacetic acid, tetra(acetoxymethyl)-ester, a membranepermeable calcium chelator, inhibits MMS-induced activation of RAFTK and p38 MAPK. Taken together, these findings indicate that RAFTK represents a stress-sensitive mediator of the p38 MAPK signaling pathway in response to certain cytotoxic agents.The mitogen-activated protein kinases (MAPKs) 1 are induced in response to diverse classes of inducers in the transduction of signals from the cell membrane to the nucleus. MAPKs are proline-directed Ser/Thr protein kinases that are regulated by extracellular signals including growth factors and cellular stress (1-3). The well characterized MAPK subfamily includes ERK1 and ERK2, which are activated by growth factors via the conserved Ras/Raf/MEK pathway (4 -7).c-Jun N-terminal protein kinases (JNKs) or stress-activated protein kinases (SAPKs) represent a second class of the mammalian MAPKs, which are primarily activated in response to tumor necrosis factor, interleukin-1, UV-, and DNA-damaging agents (5, 8 -11). A recently identified novel protein tyrosine kinase, related adhesion focal tyrosine kinase (RAFTK) (12) (also known as Pyk2, Refs. 13 and 14); CADTK, Ref. 15) has been shown to be involved upstream to ERKs and JNK signaling pathways (14,16). RAFTK is also a close relative to pp125 FAK tyrosine kinase and is activated by various extracellular signals that increase intracellular calcium concentrations (13). Moreover, RAFTK can tyrosine phosphorylate and modulate the action of ion channels and appears to function as an intermediate that links various calcium signals with both short-and long-term responses in neuronal cells (13).An additional class, which presents substantial similarity to the Saccharomyces cerevisiae HOG1 kinase involved in response to increased extracellular osmolarity (17), is p38 MAPK. p38 MAPK can also be activated by chan...
This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.
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