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15.08.2018 Солнце в сеть




Производство оборудования и технологии
Рубрики

Future Directions in Enzymatic Cellulose Hydrolysis Research

The cost of currently available cellulase preparations is a major hurdle in the enzymatic biomass hydrolysis route to cellulosic ethanol. Many researchers have realized that advancements are possible in multiple directions like optimizing the hydrolysis pro­cess, enhancing the cellulase activities, optimizing the reaction conditions, enzyme and substrate cocktail composition, enzyme recycling and recovery strategies. One interesting approach that has been recently developed is the optimization of enzymatic hydrolysis of lignocellulosic biomass using enzymes from dif­ferent sources and mixing in an appropriate proportion using the statistical approach of factorial design [91]. In these studies, they have seen a twofold reduction in the total protein required to reach glucan to glucose and xylan to xylose hydrolysis tar­gets (99% and 88% conversion, respectively). Therefore, mixing enzymes from different sources is a brilliant approach towards enzyme improvement and process cost reduction for lignocellu — lose hydrolysis [92].

Another recent advancement is research focused on enzymes that can tolerate both acid and heat which may contribute towards the improvement of lignocellulosic biomass processing. These enzymes are produced naturally by extremely thermophilic microbes or so called extremophiles [93]. Simultaneous saccharification and fer­mentation (SSF) is a smart combination of steps, however, incom­patibility of operation conditions of different enzymes is a major issue in optimization of the overall conditions. These attempts may help to find reasonable solutions to SSF conditions.

The cost can be drastically reduced with a recyclable enzyme, however, all currently available immobilization approaches gener­ally lead to reduction in the activities of these complex enzymes. Therefore, innovative immobilization approaches like pH — and temperature-responsive enzyme carriers are another interesting approach.

Another possibility is to take a genetic engineering approach, and the recent explosion of genomic data offers a unique oppor­tunity to search for novel cellulolytic activities. However, Zhulin et al. have suggested that the absence of a clear understanding of structural and functional features that are important for reliable computational identification of cellulases precludes their explora­tion in the genomic datasets [94]. In an opinion article in Trends in Biotechnology they have explored the diversity of cellulases and pro­pose a genomic approach to overcome this bottleneck. Furthermore, Zhulin et al. have identified some of the current problems and have proposed solutions as shown in Table 6.4.

In spite of intensive research efforts over the past decade, the enzyme hydrolysis step remains a major techno-economic bottle­neck in the lignocellulosic biomass to ethanol conversion process. As a result, further efforts and new directions are desirable in the enzymatic biomass hydrolysis step.

Table 6.4 Current experimental and computational problems in cellulase studies and proposed solutions to overcome these problems [94].

Problem

Proposed solution

Experimental

Lack of standardization in the use of certain assays and substrates for experimental cellulase determination

Devise a standard assay or a set of assays for unambiguous and reliable identification of cellulases

Poor taxonomic representation among experimentally studied organisms

Obtain genome sequences and biochemically characterize potential cellulases from taxo — nomically diverse organisms

Computational

Cellulases are found in 12 unre­lated protein families

Develop a natural classification system for each cellulase- containing protein family

There are multiple substrate spec­ificities other than cellulose in each of the cellulase-containing families. There are no known genomic markers for cellulases. Current models for genomic identification of cellulases are not specific

Identify class specific genomic markers for cellulases.

Develop sensitive, cellulase — specific models.

Validate models via iterative experiment-computation approach.

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