In the presented research the extracellular chitinase of Stenotrophomonas rhizophila G22 was biochemically and molecularly characterized. The studied enzyme was purified from a 72-h bacterial culture about 14 times, with a recovery of 63%. The molecular weight of the purified protein was estimated at 50 kDa by SDS-PAGE. The enzyme showed high activity against colloidal chitin. Significantly lower activities were observed with native chitin powder and chitosan. Adsorption of the enzyme to colloidal chitin and to powdered chitin at the level of 75% and 37%, respectively, was observed after 30 min of reaction. Optimum temperature and pH were 37 °C and 5.9, respectively. The enzyme demonstrated higher activity against nitrophenyl-β d N, N′, N″-triacetylchitotriose and approx. 5 times lower activity for 4-nitrophenyl-N, N′-diacetylβ-d-chitobiose. The enzyme is an endochitinase, which is confirmed by the K m and V max values determined in the studies. S. rhizophila G22 endochitinase was inhibited in the presence of cysteine-specific inhibitors, which indicates the role of cysteine moieties in the mechanism of catalysis or in stabilisation of the enzyme molecule. Also Ca 2+ and Mn 2+ ions may stabilise the protein's spatial structure. SDS and ions: Fe 2+ , Cu 2+ , Co 2+ , Zn 2+ inhibited the activity of enzyme. A full-length (2109 bp) gene coding chitinase from S. rhizophila G22 was obtained. Four domains typical for glycoside hydrolase family 18 (GH 18) chitinases were identified: catalytic Gly_18, chitin-binding-ChtBD3, type-III fibronectin-FN3 and polycystic kidney disease domain-PKD domain.

The protein kinase-like clan/superfamily is a large group of regulatory, signaling and biosynthetic enzymes that were historically regarded as typically eukaryotic proteins, although bacterial members have also been known for a long time. In this review, we explore the diversity of bacterial protein kinase like families, and discuss functional versatility of these enzymes, both the ones acting within the bacterial cell, and those acting within eukaryotic cells as effectors during infection. We focus on novel bacterial kinase-like families discovered in the last five years. A bioinformatics perspective is held here, hence sequence and structure comparison overview is presented, and also a comparison of genomic neighbourhoods of the families. We perform a phylum-level census of the families. Also, we discuss apparent pseudokinases that turned out to perform alternative catalytic functions by repurposing their atypical kinase-like active sites. We also highlight some ‘unpopular' kinase-like families that await characterisation.

The pathogenic Legionella bacteria are notorious for delivering numerous effector proteins into the host cell with the aim of disturbing and hijacking cellular processes for their benefit. Despite intensive studies, many effectors remain uncharacterized. Motivated by the richness of Legionella effector repertoires and their oftentimes atypical biochemistry, also by several known atypical Legionella effector kinases and pseudokinases discovered recently, we undertook an in silico survey and exploration of the pan-kinome of the Legionella genus, i.e., the union of the kinomes of individual species. In this study, we discovered 13 novel (pseudo)kinase families (all are potential effectors) with the use of non-standard bioinformatic approaches. Together with 16 known families, we present a catalog of effector and non-effector protein kinase-like families within Legionella, available at http://bioinfo.sggw.edu.pl/kintaro/. We analyze and discuss the likely functional roles of the novel predicted kinases. Notably, some of the kinase families are also present in other bacterial taxa, including other pathogens, often phylogenetically very distant from Legionella. This work highlights Nature’s ingeniousness in the pathogen–host arms race and offers a useful resource for the study of infection mechanisms.

The research was intended to solve the travelling salesman problem by means of genetic algorithms. The implementation of the algorithm was by virtue of CUDA technology. The research was focused on checking how much the system can improve if instead of classical CPU processors one uses GPU graphical processors enabled to perform the operations parallel. The algorithm was implemented in the high level CUDA C language. Thus, measuring the pure time of performance of the algorithm could be the single but reliable point of comparison between two above mentioned types of processors. Making some operations mutually independent and using CUDA technology makes the task much faster to execute. Due to it complex issues can be solved in a shorter time.

The pathogenic Legionella bacteria are notorious for delivering numerous effector proteins into the host cell with the aim of disturbing and hijacking cellular processes for their benefit. Despite intensive studies, many effectors remain uncharacterized. Motivated by the richness of Legionella effector repertoires and their oftentimes atypical biochemistry, also by several known atypical Legionella effector kinases and pseudokinases, we undertook an in silico survey and exploration of the pan-kinome of the Legionella genus, i.e., the union of the kinomes of individual species. In this study, we discovered 13 novel (pseudo)kinase families (all are potential effectors) with the use of non-standard bioinformatic approaches. Together with 16 known families, we present a catalog of effector and non-effector protein kinase-like families within Legionella. We analyze and discuss the likely functional roles of the novel predicted kinases. Notably, some of the kinase families are also present in other bacterial taxa, including other pathogens, often phylogenetically very distant from Legionella. This work highlights Nature's ingeniousness in the pathogen–host arms race and offers a useful resource for the study of infection mechanisms.