Phylogenetic Studies on the Prodigiosin Biosynthetic Operon.

Prodigiosin and undecylprodigiosin are tripyrrolic red pigmented antibiotics produced by certain bacteria. Many strains of Serratia and certain other Gammaproteobacteria produce prodigiosin and undecylprodigiosin is produced by certain strains of Streptomyces. This is a multistage process which involves the synthesis of a bipyrrolic compound from L-proline and its subsequent condensation with a mono pyrrole synthesized from 2-octenal in the case of prodigiosin and malonyl-CoA in the case of undecylprodigiosin respectively. We have carried out sequence analysis of the genes involved in the pathway and identified the distribution of the prodigiosin producing genes amongst the various bacteria which have been fully sequenced. The presence of the operon was clearly seen in certain clustered branches suggesting inheritance from a common ancestor. This was further confirmed by the absence of traits observed in horizontally acquired genes like, GC content variation, codon bias or the presence of mobile elements. Multiple sequence alignment of the promoter of the prodigiosin operon in seven fully sequenced Serratia marcescens strains showed excellent homology. Putative regulatory elements in this region were identified by sequence analysis studies and many of them have been found to influence pigment production. The undecylprodigiosin gene cluster on the other hand, shows homology to other gene clusters involved in the production of other pyrrole-containing antibiotics of the genus Streptomyces. This coupled with the presence of ORFs with three different promoters could indicate lateral gene transfer. Hence the evolution of undecylprodigiosin operon could be an example of convergent evolution.

Improved stability and catalytic activity of graphene oxide/chitosan hybrid beads loaded with porcine liver esterase.

Graphene oxide/chitosan and reduced graphene oxide/chitosan (GO/CS and RGO/CS) beads were prepared by precipitation with NaOH. Porcine liver esterase was immobilized on these beads to give GO/CS/E and RGO/CS/E beads. The optimum conditions for the maximum activity of RGO/CS/E beads were pH 8 and 50°C. The stability of the enzyme immobilized on GO/CS/E and RGO/CS/E was high in the pH range of 5-8. The GO/CS/E beads showed superior stability compared to that of the free enzyme and CS/E beads between 20 and 50°C. Kinetic analysis showed that GO/CS/E was a better catalyst than the RGO/CS/E beads with a lower Km value of 0.9 mM. The hybrid beads also retained more than 95% activity after 10 consecutive cycles. The GO/CS/E and RGO/CS/E beads retained 84% and 87% activity after 40 days at 4°C. The GO/CS/E beads were used for the successful hydrolysis of methyl 4-hydroxy benzoate.

Automatic classification of protein structures using physicochemical parameters.

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.