PubMed Health⌕ Search

PubMed · 11406306

A review of structure-based biodegradation estimation methods.

Abstract

Biodegradation, being the principal abatement process in the environment, is the most important parameter influencing the toxicity, persistence, and ultimate fate in aquatic and terrestrial ecosystems. Biodegradation of an organic chemical in natural systems may be classified as primary (alteration of molecular integrity), ultimate (complete mineralization; i.e. conversion to inorganic compounds and/or normal metabolic processes), or acceptable (toxicity ameliorated). Most of the biodegradation correlations presented in the literature focus on the characterization of primary or ultimate, aerobic degradation. The US Environmental Protection Agency (USEPA) is charged with determining the risks associated with the thousands of chemicals employed in commerce, an effort that is being facilitated through much research aimed at reliable structure-activity relationships (SAR) to predict biodegradation of chemicals in natural systems. To this end, models are needed to understand the mechanisms of biodegradation, to classify chemicals according to relative biodegradability, and to develop reliable biodegradation estimation methods for new chemicals. Frequently, published correlations associating molecular structure to biodegradation will attempt to quantify the degradability of a limited set of homologous chemicals. These correlations have been dubbed quantitative structure biodegradability relationships (QSBRs). More scarce and valuable to researchers are those models that predict the biodegradability of compounds possessing a wide variety of chemical structures. The latter may use any of several techniques and molecular descriptors to correlate biodegradability: QSBRs, pattern recognition, discriminant analysis, and principle component analysis (PCA), to name several. Generally, models either predict the propensity of a chemical to biodegrade using Boolean-type logic (i.e. whether a chemical will "readily biodegrade" or not), or else they quantify the degree of biodegradation by providing information such as rate constants. Such quantitative predictions of biodegradability come in a diversity of parameters, including half-lives, various biodegradation rates and rates constants, theoretical oxygen demand (ThOD), biological oxygen demand (BOD), and others. In this paper, after describing the advantages and disadvantages of the various biodegradation estimation methods found in the literature, the best models are compared to conclude which provide the greatest utility for determining the biodegradability of chemicals with widely varying structures. The group contribution technique presented by Boethling et al. [Environmen. Sci. Technol. 28 (1994) 459] appears to be the most advantageous for use in broad screening for tendency to biodegrade. The model is simple to use, calculating a probability of biodegrading ranging from 0 (none) to 1 (certain), and has proven to be accurate for a wide range of chemical structures, as established by the large, high-quality data set (BIODEG evaluated biodegradation database, Syracuse Research Corporation, Merrill Lane, Syracuse, NY 13210) used to develop this correlation. The authors, therefore, recommend the method of Boethling et al. [Environ. Sci. Technol. 28 (1994) 459] for the initial screening of chemicals to aid in determining whether additional information is necessary to establish relative biodegradability. For readers with applications requiring more quantitative results, such as biodegradation rate constants, enough model details are presented in this paper to allow the reader to pick a suitable correlation, although the reader is cautioned to consult the original, primary reference for the complete method description, equations, and limitations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J W Raymond, T N Rogers, D R Shonnard, A A Kline. 2001-06-29. A review of structure-based biodegradation estimation methods.. https://doi.org/10.1016/s0304-3894(01)00207-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Fungi to the rescue: recent advances, mechanistic insights and omics-based perspectives in heavy metal mycoremediation.

Heavy metal (HM) contamination arising from rapid industrialization poses critical threats to global ecosystem integrity and public health. Conventional physicochemical approaches are limited by high costs, incomplete removal, and toxic waste generation, necessitating sustainable alternatives. Mycoremediation, which harnesses the remarkable, diverse capacities of fungi to tolerate and mitigate HM stress through sophisticated biological mechanisms, has emerged as a promising and sustainable approach to address HM pollution. This review examines the sources and ecotoxicological impacts of HM pollution, alongside the intracellular and extracellular mechanisms underlying fungal tolerance and removal, including biosorption, precipitation, membrane transport, antioxidant defense, chelation, bioaccumulation, and biotransformation. It further synthesizes fungal-based bioremediation strategies, while examining how metagenomic, metatranscriptomic, transcriptomic, proteomic, and metabolomic approaches are advancing understanding of fungal community structure and active detoxification pathways. This work uniquely integrates community- and isolate-level multi-omics data, explicitly bridges mechanistic understanding with omics-driven insights, and extends this into translational roadmap for applied bioremediation.

Biodegradation, Environmental↗

Cooperative anaerobic catabolism of chlorinated organic compounds: implications for sustainable bioremediation.

Biodegradation research historically followed a reductionist approach focused on axenic (pure) cultures capable of catabolizing the specific contaminant(s) of interest. While this approach has substantially advanced our understanding of the microbiology, physiology, biochemistry, and genetics of contaminant degradation under laboratory conditions, it does not capture the complexity of natural and engineered environments. During in situ bioremediation, microbiomes are exposed to mixtures of contaminants, and microbial interactions profoundly influence contaminant transformation and fate. In anoxic environments, degradation of chlorinated compounds is often sustained by metabolic cooperation among taxonomically and physiologically distinct microorganisms. Through the exchange of metabolites such as hydrogen, formate, acetate, and other nutrients, microbial populations establish interdependent networks that overcome thermodynamic and physiological constraints, enabling self-sustaining systems of contaminant transformations that would be inefficient or impossible with individual organisms. We highlight examples of microbial interactions that underpin anaerobic catabolism of chlorinated contaminants, including systems resulting in self-sustained anaerobic bioremediation.

Biodegradation, Environmental↗

Discovering hidden candidate plastic-degrading enzymes: Combined multi-omics and machine learning strategy.

Plastic pollution poses a major threat to the stability of natural ecosystems as well as human health. Microbial enzymes have long been considered a potential resource for targeted biodegradation but, except for a few successful cases, the discovery of efficient enzymes has proved challenging. Aiming to accelerate the process, we propose an approach combining metagenomics, metatranscriptomics and semi-supervised learning that selects promising plastic-degrading candidate enzymes from the proteome of relevant microorganisms. Tested on a dataset of over 10,000 microbial proteins, ranking models consistently prioritize known plastic-degrading enzymes, achieving an area under the cumulative distribution function curve above 0.96, with leave-one-family-out cross-validation indicating that performance is largely retained across protein families. As a case study, this work focuses on mixed microbial cultures exposed for extended periods to polyethylene, polyethylene terephthalate, and polyurethane substrates. The prevalent species after selective enrichment were functionally characterized, finding Rhodococcus aetherivorans as the most relevant species in two of the five cultures under investigation. Among the top-ranked proteins, several have high structural similarity with known enzymes despite not being identified by sequence similarity search. Moreover, according to metatranscriptomics results, several of these enzymes were found to be expressed at the same level or above that of annotated enzymes, suggesting that they may have functional relevance. Overall, this work highlights the potential of integrating multi-omics with data-driven methods for enzyme discovery and for accelerating the development of biotechnological solutions to plastic pollution.

Biodegradation, Environmental↗