Homology modelling and in silico substrate-binding analysis of a Rhizobium sp. RC1 haloalkanoic acid permease

Revealing potential functions of hypothetical proteins induced by genistein in the symbiosis island of Bradyrhizobium japonicum commercial strain SEMIA 5079 (= CPAC 15)

BACKGROUND

Bradyrhizobium japonicum strain SEMIA 5079 (= CPAC 15) is a nitrogen-fixing symbiont of soybean broadly used in commercial inoculants in Brazil. Its genome has about 50% of hypothetical (HP) protein-coding genes, many in the symbiosis island, raising questions about their putative role on the biological nitrogen fixation (BNF) process. This study aimed to infer functional roles to 15 HP genes localized in the symbiosis island of SEMIA 5079, and to analyze their expression in the presence of a nod-gene inducer.

RESULTS

A workflow of bioinformatics tools/databases was established and allowed the functional annotation of the HP genes. Most were enzymes, including transferases in the biosynthetic pathways of cobalamin, amino acids and secondary metabolites that may help in saprophytic ability and stress tolerance, and hydrolases, that may be important for competitiveness, plant infection, and stress tolerance. Putative roles for other enzymes and transporters identified are discussed. Some HP proteins were specific to the genus Bradyrhizobium, others to specific host legumes, and the analysis of orthologues helped to predict roles in BNF.

CONCLUSIONS

All 15 HP genes were induced by genistein and high induction was confirmed in five of them, suggesting major roles in the BNF process.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.