Lysine is catabolized via the saccharopine pathway in plants and mammals. In this pathway, lysine is converted to a-aminoadipic-d-semialdehyde (AASA) by lysine-ketoglutarate reductase/ saccharopine dehydrogenase (LKR/SDH); thereafter, AASA is converted to aminoadipic acid (AAA) by a-aminoadipic-d-semialdehyde dehydrogenase (AASADH). Here, we investigate the occurrence, genomic organization and functional role of lysine catabolic pathways among prokaryotes. Surprisingly, only 27 species of the 1478 analyzed contain the lkr and sdh genes, whereas 323 species contain aasadh orthologs. A sdh-related gene, identified in 159 organisms, was frequently found contiguously to an aasadh gene. This gene, annotated as lysine dehydrogenase (lysdh), encodes LYSDH an enzyme that directly converts lysine to AASA. Pipecolate oxidase (PIPOX) and lysine-6-aminotransferase (LAT), that converts lysine to AASA, were also found associated with aasadh. Interestingly, many lysdh-aasadh-containing organisms live under hyperosmotic stress. To test the role of the lysine-to-AASA pathways in the bacterial stress response, we subjected Silicibacter pomeroyi to salt stress. All but lkr, sdh, lysdh and aasadh were upregulated under salt stress conditions. In addition, lysine-supplemented culture medium increased the growth rate of S. pomeroyi under high-salt conditions and induced high-level expression of the lysdh-aasadh operon. Finally, transformation of Escherichia coli with the S. pomeroyi lysdh-aasadh operon resulted in increased salt tolerance. The transformed E. coli accumulated high levels of the compatible solute pipecolate, which may account for the salt resistance. These findings suggest that the lysine-to-AASA pathways identified in this work may have a broad evolutionary importance in osmotic stress resistance.

Sphingomyelinases D (SMases D) or dermonecrotic toxins are well characterized in Loxosceles spider venoms and have been described in some strains of pathogenic microorganisms, such as Corynebacterium sp. After spider bites, the SMase D molecules cause skin necrosis and occasional severe systemic manifestations, such as acute renal failure. In this paper, we identified new SMase D amino acid sequences from various organisms belonging to 24 distinct genera, of which, 19 are new. These SMases D share a conserved active site and a C-terminal motif. We suggest that the C-terminal tail is responsible for stabilizing the entire internal structure of the SMase D Tim barrel and that it can be considered an SMase D hallmark in combination with the amino acid residues from the active site. Most of these enzyme sequences were discovered from fungi and the SMase D activity was experimentally confirmed in the fungus Aspergillus flavus. Because most of these novel SMases D are from organisms that are endowed with pathogenic properties similar to those evoked by these enzymes alone, they might be associated with their pathogenic mechanisms.

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

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