Homology modeling and functional sites prediction of azoreductase enzyme from the cyanobacterium Nostoc sp. PCC7120

An Integrative Approach to Study Bacterial Enzymatic Degradation of Toxic Dyes

Synthetic dyes pose a large threat to the environment and consequently to human health. Various dyes are used in textile, cosmetics, and pharmaceutical industries, and are released into the environment without any treatment, thus adversely affecting both the environment and neighboring human populations. Several existing physical and chemical methods for dye degradation are effective but have many drawbacks. Biological methods over the years have gained importance in the decolorization and degradation of dye and have also overcome the disadvantages of physiochemical methods. Furthermore, biological methods are eco-friendly and lead to complete decolorization. The mechanism of decolorization and degradation by several bacterial enzymes are discussed in detail. For the identification of ecologically sustainable strains and their application at the field level, we have focused on bioaugmentation aspects. Furthermore, in silico studies such as molecular docking of bacterial enzymes with dyes can give a new insight into biological studies and provide an easy way to understand the interaction at the molecular level. This review mainly focuses on an integrative approach and its importance for the effective treatment and decolorization of dyes.

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.