Environmental Microbiology Laboratory, Environmental Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
School of Life Science and Food Engineering, Huaiyin Institute of Technology, Huaian 223003, China.
Sci Total Environ. 2021 May 20;770:144561. doi: 10.1016/j.scitotenv.2020.144561. Epub 2021 Jan 17.
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.
计算机技术与计算框架的可行性已被应用于预测性生物修复，旨在清除污染物、评估毒性以及降解复杂难处理化合物的可能性。来自不同行业的新兴污染物对环境和公众健康构成了重大威胁。鉴于当前的生物修复策略，对于危险污染物的可持续缓解往往是失败的或不足的。然而，从传统修复技术的角度来看，关于生物降解的明确关键信息是相当不完整的。由于缺乏关于生物转化化合物的完整信息，因此需要寻找替代方法。关于转化产物和毒性特征的信息在已发表的文献中非常有限。为了弥补这一文献空白，各种计算或计算机技术已经作为替代技术出现，这些技术被认为是生物修复的计算机模拟方法。分子对接、分子动力学模拟和生物降解途径预测是预测生物降解的重要组成部分，包括定量构效关系（QSAR）和定量构效生物降解关系（QSBR）模型系统。此外，机器学习（ML）、人工神经网络（ANN）和遗传算法（GA）等程序为同时进行生物降解预测以及毒性和环境归宿预测提供了支持。在此，我们重点介绍了在传统生物修复无法减轻这些污染物的情况下，计算机修复方法对各种持久性、难处理污染物的可行性。可以通过利用描述的模型系统和基于算法的程序来解决这些问题。此外，还总结了 QSAR 建模、算法和专用生物降解预测系统的最新进展，以及它们独特的属性。
