The expression of messenger RNAs coding for growth factors, their receptors, and eph-class receptor tyrosine kinases in normal and ototoxically damaged chick cochleae

Messenger RNAs coding for growth factors and receptor tyrosine kinases were measured by quantitative competitive and by semi-quantitative reverse-transcription polymerase chain reaction in whole and dissected chick inner ears. The fibroblast growth factor (FGF) receptor 1 chick embryonic kinase (CEK) 1 was expressed in all structures examined (otocyst, hatchling whole cochlea, cochlear nerve ganglion, and cochlear and vestibular sensory epithelia), although slightly more heavily in the otocyst. The related fibroblast growth factor receptors CEK 2 and 3 were preferentially expressed in the nerve ganglion and in the vestibular sensory epithelium, respectively. FGF 1 mRNA was low in early development, increasing to mature levels at around embryonic age 11 days, while FGF2, mRNA was expressed at constant levels at all ages. In response to ototoxic damage, FGF1 mRNA levels were increased in the early damaged cochlear sensory epithelium. Immunohistochemistry for CEK1 showed that normal hair cells expressed the receptor heavily on the hair cell stereocilia, while with early damage, CEK1 came to be expressed heavily on the apical surfaces of the supporting cells. In normal chicks, the CEK4 and CEK8 eph-class receptor tyrosine kinases were expressed relatively heavily by the cochlear nerve ganglion, and CEK10 was expressed relatively heavily by the cochlear hair cell sensory epithelium. The results suggest that the FGF system may be involved in the response of the cochlear epithelium to ototoxic damage. The eph-class receptor tyrosine kinase CEK10 may be involved in cell interactions in the cochlear sensory epithelium, while CEK4 and CEK8 may play a role in the cochlear innervation.

FHA domain boundaries of the Dun1p and Rad53p cell cycle checkpoint kinases

Dun1p and Rad53p of the budding yeast Saccharomyces cerevisiae are members of a conserved family of cell cycle checkpoint protein kinases that contain forkhead-associated (FHA) domains. Here, we demonstrate that these FHA domains contain 130-140 residues, and are thus considerably larger than previously predicted by sequence comparisons (55-75 residues), In vivo, expression of the proteolytically defined Dun1p FHA domain, but not a fragment containing only the predicted domain boundaries, inhibited the transcriptional induction of repair genes following replication blocks, This indicates that the non-catalytic FI-IA domain plays an important role in the transcriptional function of the Dun1p protein kinase. (C) 2000 Federation of European Biochemical Societies.

Giant protein kinases: Domain interactions and structural basis of autoregulation

The myosin-associated giant protein kinases twitchin and titin are composed predominantly of fibronectin- and immunoglobulin-like modules, We report the crystal structures of two autoinhibited twitchin kinase fragments, one from Aplysia and a larger fragment from Caenorhabditis elegans containing an additional C-terminal immunoglobulin-like domain, The structure of the longer fragment shoes that the immunoglobulin domain contacts the protein kinase domain on the opposite side from the catalytic cleft, laterally exposing potential myosin binding residues, Together, the structures reveal the cooperative interactions between the autoregulatory region and the residues from the catalytic domain involved in protein substrate binding, ATP binding, catalysis and the activation loop, and explain the differences between the observed autoinhibitory mechanism and the one found in the structure of calmodulin-dependent kinase I.

Crystallization and preliminary X-ray diffraction studies of FHA domains of Dun1 and Rad53 protein kinases

Forkhead-associated (FHA) domains are modular protein–protein interaction domains of ~130 amino acids present in numerous signalling proteins. FHA-domain-dependent protein interactions are regulated by phosphorylation of target proteins and FHA domains may be multifunctional phosphopeptide-recognition modules. FHA domains of the budding yeast cell-cycle checkpoint protein kinases Dun1p and Rad53p have been crystallized. Crystals of the Dun1-FHA domain exhibit the symmetry of the space group P6122 or P6522, with unit-cell parameters a = b = 127.3, c = 386.3 Å; diffraction data have been collected to 3.1 Å resolution on a synchrotron source. Crystals of the N-terminal FHA domain (FHA1) of Rad53p diffract to 4.0 Å resolution on a laboratory X-ray source and have Laue-group symmetry 4/mmm, with unit-cell parameters a = b = 61.7, c = 104.3 Å.

Structural basis and prediction of substrate specificity in protein serine/threonine kinases

The large number of protein kinases makes it impractical to determine their specificities and substrates experimentally. Using the available crystal structures, molecular modeling, and sequence analyses of kinases and substrates, we developed a set of rules governing the binding of a heptapeptide substrate motif (surrounding the phosphorylation site) to the kinase and implemented these rules in a web-interfaced program for automated prediction of optimal substrate peptides, taking only the amino acid sequence of a protein kinase as input. We show the utility of the method by analyzing yeast cell cycle control and DNA damage checkpoint pathways. Our method is the only available predictive method generally applicable for identifying possible substrate proteins for protein serine/threonine kinases and helps in silico construction of signaling pathways. The accuracy of prediction is comparable to the accuracy of data from systematic large-scale experimental approaches.

Phosphoregulators: Protein kinases and protein phosphatases of mouse

With the completion of the human and mouse genome sequences, the task now turns to identifying their encoded transcripts and assigning gene function. In this study, we have undertaken a computational approach to identify and classify all of the protein kinases and phosphatases present in the mouse gene complement. A nonredundant set of these sequences was produced by mining Ensembl gene predictions and publicly available cDNA sequences with a panel of InterPro domains. This approach identified 561 candidate protein kinases and 162 candidate protein phosphatases. This cohort was then analyzed using TribeMCL protein sequence similarity clustering followed by CLUSTALV alignment and hierarchical tree generation. This approach allowed us to (1) distinguish between true members of the protein kinase and phosphatase families and enzymes of related biochemistry, (2) determine the structure of the families, and (3) suggest functions for previously uncharacterized members. The classifications obtained by this approach were in good agreement with previous schemes and allowed us to demonstrate domain associations with a number of clusters. Finally, we comment on the complementary nature of cDNA and genome-based gene detection and the impact of the FANTOM2 transcriptome project.

Substrate specifity of protein kinases and computational prediction of substrates

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of peptide specificity of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information. (c) 2005 Elsevier B.V. All rights reserved.

Protein kinases associated with the yeast phosphoproteome

Background: Protein phosphorylation is an extremely important mechanism of cellular regulation. A large-scale study of phosphoproteins in a whole-cell lysate of Saccharomyces cerevisiae has previously identified 383 phosphorylation sites in 216 peptide sequences. However, the protein kinases responsible for the phosphorylation of the identified proteins have not previously been assigned. Results: We used Predikin in combination with other bioinformatic tools, to predict which of 116 unique protein kinases in yeast phosphorylates each experimentally determined site in the phosphoproteome. The prediction was based on the match between the phosphorylated 7-residue sequence and the predicted substrate specificity of each kinase, with the highest weight applied to the residues or positions that contribute most to the substrate specificity. We estimated the reliability of the predictions by performing a parallel prediction on phosphopeptides for which the kinase has been experimentally determined. Conclusion: The results reveal that the functions of the protein kinases and their predicted phosphoprotein substrates are often correlated, for example in endocytosis, cytokinesis, transcription, replication, carbohydrate metabolism and stress response. The predictions link phosphoproteins of unknown function with protein kinases with known functions and vice versa, suggesting functions for the uncharacterized proteins. The study indicates that the phosphoproteins and the associated protein kinases represented in our dataset have housekeeping cellular roles; certain kinases are not represented because they may only be activated during specific cellular responses. Our results demonstrate the utility of our previously reported protein kinase substrate prediction approach (Predikin) as a tool for establishing links between kinases and phosphoproteins that can subsequently be tested experimentally.

Uses for JNK: the many and varied substrates of the c-Jun N-terminal kinases

The c-Jun N-terminal kinases (JNKs) are members of a larger group of serine/ threonine (Ser/Thr) protein kinases from the mitogen-activated protein kinase family. JNKs were originally identified as stress-activated protein kinases in the livers of cycloheximide-challenged rats. Their subsequent purification, cloning, and naming as JNKs have emphasized their ability to phosphorylate and activate the transcription factor c-Jun. Studies of c-Jun and related transcription factor substrates have provided clues about both the preferred substrate phosphorylation sequences and additional docking domains recognized by JNK There are now more than 50 proteins shown to be substrates for JNK These include a range of nuclear substrates, including transcription factors and nuclear hormone receptors, heterogeneous nuclear ribonucleoprotein K and the Pol I-specific transcription factor TIF-IA, which regulates ribosome synthesis. Many nonnuclear substrates have also been characterized, and these are involved in protein degradation (e.g., the E3 ligase Itch), signal transduction (e.g., adaptor and scaffold proteins and protein kinases), apoptotic cell death (e.g., mitochondrial Bcl2 family members), and cell movement (e.g., paxillin, DCX, microtubule-associated proteins, the stathmin family member SCG10, and the intermediate filament protein keratin 8). The range of JNK actions in the cell is therefore likely to be complex. Further characterization of the substrates of JNK should provide clearer explanations of the intracellular actions of the JNKs and may allow new avenues for targeting the JNK pathways with therapeutic agents downstream of JNK itself.