Characterization of a mutant glucose isomerase from<i>Thermoanaerobacterium saccharolyticum</i>

Xu, Heng; Dong, Shikui; Wu, Xue-Qiang; Liu, Zhiwei; Yang, Quanjun

doi:10.1007/s10295-014-1478-4

Cited by 20 publications

(4 citation statements)

References 20 publications

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“…The optimum pH for CbGI activity was determined to be pH 7.0 presented in Figure 2(a), such as GI from T. saccharolyticum [9], Actinoplanes missouriensis CICIM B0118(A) [10], and Streptomyces griseus [11]. The optimum pH of these strains is between 7.0 and 7.5.…”

Section: Enzymatic Properties Of Purified Recombinant Cbgimentioning

confidence: 99%

A Novel Glucose Isomerase from Caldicellulosiruptor bescii with Great Potentials in the Production of High‐Fructose Corn Syrup

Dai

Miao

Jiang

et al. 2020

BioMed Research International

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Glucose isomerase (GI) that catalyzes the conversion of D-glucose to D-fructose is one of the most important industrial enzymes for the production of high-fructose corn syrup (HFCS). In this study, a novel GI (CbGI) was cloned from Caldicellulosiruptor bescii and expressed in Escherichia coli. The purified recombinant CbGI (rCbGI) showed neutral and thermophilic properties. It had optimal activities at pH 7.0 and 80°C and retained stability at 85°C. In comparison with other reported GIs, rCbGI exhibited higher substrate affinity (Km=42.61 mM) and greater conversion efficiency (up to 57.3% with 3M D-glucose as the substrate). The high catalytic efficiency and affinity of this CbGI is much valuable for the cost-effective production of HFCS.

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Section: Enzymatic Properties Of Purified Recombinant Cbgimentioning

confidence: 99%

A Novel Glucose Isomerase from Caldicellulosiruptor bescii with Great Potentials in the Production of High‐Fructose Corn Syrup

Dai

Miao

Jiang

et al. 2020

BioMed Research International

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“…Several advantages of TPPs include reduced contamination, mixes easily with low viscous agents while maintaining a high mass transfer rate as well as achieving greater solubility of products and substrates (Vieille and Zeikus, 2001[ 55 ]). Furthermore, TPPs are advantageous in high-temperature pelleting processes (Rodriguez et al, 2000[ 48 ]) as well as in the isomerization of glucose through endothermic reactions to generate high fructose syrups (Xu et al, 2014[ 60 ]). Although experimental methods are the gold standard in verifying thermostability of proteins, these methods are usually labor-intensive, time-consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

Charoenkwan,

Schaduangrat,

Moni

et al. 2022

EXCLI Journal; 21:Doc554; ISSN 1611-2156

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Thermophilic proteins (TPPs) are critical for basic research and in the food industry due to their ability to maintain a thermodynamically stable fold at extremely high temperatures. Thus, the expeditious identification of novel TPPs through computational models from protein sequences is very desirable. Over the last few decades, a number of computational methods, especially machine learning (ML)-based methods, for in silico prediction of TPPs have been developed. Therefore, it is desirable to revisit these methods and summarize their advantages and disadvantages in order to further develop new computational approaches to achieve more accurate and improved prediction of TPPs. With this goal in mind, we comprehensively investigate a large collection of fourteen state-of-the-art TPP predictors in terms of their dataset size, feature encoding schemes, feature selection strategies, ML algorithms, evaluation strategies and web server/software usability. To the best of our knowledge, this article represents the first comprehensive review on the development of ML-based methods for in silico prediction of TPPs. Among these TPP predictors, they can be classified into two groups according to the interpretability of ML algorithms employed (i.e., computational black-box methods and computational white-box methods). In order to perform the comparative analysis, we conducted a comparative study on several currently available TPP predictors based on two benchmark datasets. Finally, we provide future perspectives for the design and development of new computational models for TPP prediction. We hope that this comprehensive review will facilitate researchers in selecting an appropriate TPP predictor that is the most suitable one to deal with their purposes and provide useful perspectives for the development of more effective and accurate TPP predictors.

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“…As for their advantages in high-temperature industrial catalysis, TPPs have reduced contamination, easy mixing with low viscosity and high mass transfer rate, higher solubility of substrates and products 13 . Furthermore, the advantage of TPPs are their use in high-temperature pelleting process 14 and in endothermic processes such as the isomerization of glucose to generate high fructose syrups 15 . Although experimental methods are the way to certify thermostability of proteins, these methods are usually labor-intensive, time-consuming and expensive.…”

Section: Introductionmentioning

confidence: 99%

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Charoenkwan

Chotpatiwetchkul

Lee

et al. 2021

Sci Rep

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Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906–0.910) and 2–17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

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Characterization of a mutant glucose isomerase fromThermoanaerobacterium saccharolyticum

Cited by 20 publications

References 20 publications

A Novel Glucose Isomerase from Caldicellulosiruptor bescii with Great Potentials in the Production of High‐Fructose Corn Syrup

A Novel Glucose Isomerase from Caldicellulosiruptor bescii with Great Potentials in the Production of High‐Fructose Corn Syrup

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

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