Search

The importance of 3D protein structure in proteolytic processing is well known. However, despite the plethora of existing methods for predicting proteolytic sites, only a few of them utilize the structural features of potential substrates as predictors. Moreover, to our knowledge, there is currently no method available for predicting the structural susceptibility of protein regions to proteolysis. We developed such a method using data from CutDB, a database that contains experimentally verified proteolytic events. For prediction, we utilized structural features that have been shown to influence proteolysis in earlier studies, such as solvent accessibility, secondary structure, and temperature factor. Additionally, we introduced new structural features, including length of protruded loops and flexibility of protein termini. To maximize the prediction quality of the method, we carefully curated the training set, selected an appropriate machine learning method, and sampled negative examples to determine the optimal positive-to-negative class size ratio. We demonstrated that combining our method with models of protease primary specificity can outperform existing bioinformatics methods for the prediction of proteolytic sites. We also discussed the possibility of utilizing this method for bioinformatics prediction of other post-translational modifications. [ABSTRACT FROM AUTHOR]

Amino acid decarboxylases convert amino acids into different biogenic amines which regulate diverse biological processes. Therefore, identifying the substrates of amino acid decarboxylases is critical for investigating the function of the decarboxylases, especially for the new genes predicted to be amino acid decarboxylases. In the present work, we have established a simple and efficient method to identify the substrates and enzymatic activity of amino acid decarboxylases based on LC-MS methods. We chose GAD65 and AADC as models to validate our method. GAD65 and AADC were expressed in HEK 293T cells and purified through immunoprecipitation. The purified amino acid decarboxylases were subjected to enzymatic reaction with different substrate mixtures in vitro. LC-MS analysis of the reaction mixture identified depleted or accumulated metabolites, which corresponded to candidate enzyme substrates and products, respectively. Our method successfully identified the substrates and products of known amino acid decarboxylases. In summary, our method can efficiently identify the substrates and products of amino acid decarboxylases, which will facilitate future amino acid decarboxylase studies. [ABSTRACT FROM AUTHOR]

Inherited retinal degenerative diseases (IRDs), which ultimately lead to photoreceptor cell death, are characterized by high genetic heterogeneity. Many IRD-associated genetic defects affect 3′,5′-cyclic guanosine monophosphate (cGMP) levels. cGMP-dependent protein kinases (PKGI and PKGII) have emerged as novel targets, and their inhibition has shown functional protection in IRDs. The development of such novel neuroprotective compounds warrants a better understanding of the pathways downstream of PKGs that lead to photoreceptor degeneration. Here, we used human recombinant PKGs in combination with PKG activity modulators (cGMP, 3′,5′-cyclic adenosine monophosphate (cAMP), PKG activator, and PKG inhibitors) on a multiplex peptide microarray to identify substrates for PKGI and PKGII. In addition, we applied this technology in combination with PKG modulators to monitor kinase activity in a complex cell system, i.e. the retinal cell line 661W, which is used as a model system for IRDs. The high-throughput method allowed quick identification of bona fide substrates for PKGI and PKGII. The response to PKG modulators helped us to identify, in addition to ten known substrates, about 50 novel substrates for PKGI and/or PKGII which are either specific for one enzyme or common to both. Interestingly, both PKGs are able to phosphorylate the regulatory subunit of PKA, whereas only PKGII can phosphorylate the catalytic subunit of PKA. In 661W cells, the results suggest that PKG activators cause minor activation of PKG, but a prominent increase in the activity of cAMP-dependent protein kinase (PKA). However, the literature suggests an important role for PKG in IRDs. This conflicting information could be reconciled by cross-talk between PKG and PKA in the retinal cells. This must be explored further to elucidate the role of PKGs in IRDs. [ABSTRACT FROM AUTHOR]