BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Identification and characterization of rhizospheric microbial diversity by 16S ribosomal RNA gene sequencing

In the present study, samples of rhizosphere and root nodules were collected from different areas of Pakistan to isolate plant growth promoting rhizobacteria. Identification of bacterial isolates was made by 16S rRNA gene sequence analysis and taxonomical confirmation on EzTaxon Server. The identified bacterial strains were belonged to 5 genera i.e. Ensifer, Bacillus, Pseudomona, Leclercia and Rhizobium. Phylogenetic analysis inferred from 16S rRNA gene sequences showed the evolutionary relationship of bacterial strains with the respective genera. Based on phylogenetic analysis, some candidate novel species were also identified. The bacterial strains were also characterized for morphological, physiological, biochemical tests and glucose dehydrogenase (gdh) gene that involved in the phosphate solublization using cofactor pyrroloquinolone quinone (PQQ). Seven rhizoshperic and 3 root nodulating stains are positive for gdh gene. Furthermore, this study confirms a novel association between microbes and their hosts like field grown crops, leguminous and non-leguminous plants. It was concluded that a diverse group of bacterial population exist in the rhizosphere and root nodules that might be useful in evaluating the mechanisms behind plant microbial interactions and strains QAU-63 and QAU-68 have sequence similarity of 97 and 95% which might be declared as novel after further taxonomic characterization.

Bioinformatics based structural characterization of glucose dehydrogenase (gdh) gene and growth promoting activity of Leclercia sp. QAU-66

Glucose dehydrogenase (GDH; EC 1.1. 5.2) is the member of quinoproteins group that use the redox cofactor pyrroloquinoline quinoine, calcium ions and glucose as substrate for its activity. In present study, Leclercia sp. QAU-66, isolated from rhizosphere of Vigna mungo, was characterized for phosphate solubilization and the role of GDH in plant growth promotion of Phaseolus vulgaris. The strain QAU-66 had ability to solubilize phosphorus and significantly (p ≤ 0.05) promoted the shoot and root lengths of Phaseolus vulgaris. The structural determination of GDH protein was carried out using bioinformatics tools like Pfam, InterProScan, I-TASSER and COFACTOR. These tools predicted the structural based functional homology of pyrroloquinoline quinone domains in GDH. GDH of Leclercia sp. QAU-66 is one of the main factor that involved in plant growth promotion and provides a solid background for further research in plant growth promoting activities.