Carbon Nanotube Covalently Attached Laccase Biocathode for Biofuel Cell

Kurniawan, Rizmahardian Ashari; Aulanni’am, Aulanni’am; Shieh, Fa Kuen; Chu, Peter Po-Jhen

doi:10.21776/ub.jpacr.2013.002.02.143

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Lysine can also be found widely in catalytic domains, for example C-terminal lysines have been implicated in the inactivation of heterologously produced Laccases [ 46 ]. Aside from function, lysines are also widely used as a cross linking target to bind Laccases to various materials [ 47 - 49 ]. Glutamic acid had the next most significant differences between classes.…”

Section: Resultsmentioning

confidence: 99%

LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches

et al. 2014

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BackgroundLaccases (E.C. 1.10.3.2) are multi-copper oxidases that have gained importance in many industries such as biofuels, pulp production, textile dye bleaching, bioremediation, and food production. Their usefulness stems from the ability to act on a diverse range of phenolic compounds such as o-/p-quinols, aminophenols, polyphenols, polyamines, aryl diamines, and aromatic thiols. Despite acting on a wide range of compounds as a family, individual Laccases often exhibit distinctive and varied substrate ranges. This is likely due to Laccases involvement in many metabolic roles across diverse taxa. Classification systems for multi-copper oxidases have been developed using multiple sequence alignments, however, these systems seem to largely follow species taxonomy rather than substrate ranges, enzyme properties, or specific function. It has been suggested that the roles and substrates of various Laccases are related to their optimal pH. This is consistent with the observation that fungal Laccases usually prefer acidic conditions, whereas plant and bacterial Laccases prefer basic conditions. Based on these observations, we hypothesize that a descriptor-based unsupervised learning system could generate homology independent classification system for better describing the functional properties of Laccases.ResultsIn this study, we first utilized unsupervised learning approach to develop a novel homology independent Laccase classification system. From the descriptors considered, physicochemical properties showed the best performance. Physicochemical properties divided the Laccases into twelve subtypes. Analysis of the clusters using a t-test revealed that the majority of the physicochemical descriptors had statistically significant differences between the classes. Feature selection identified the most important features as negatively charges residues, the peptide isoelectric point, and acidic or amidic residues. Secondly, to allow for classification of new Laccases, a supervised learning system was developed from the clusters. The models showed high performance with an overall accuracy of 99.03%, error of 0.49%, MCC of 0.9367, precision of 94.20%, sensitivity of 94.20%, and specificity of 99.47% in a 5-fold cross-validation test. In an independent test, our models still provide a high accuracy of 97.98%, error rate of 1.02%, MCC of 0.8678, precision of 87.88%, sensitivity of 87.88% and specificity of 98.90%.ConclusionThis study provides a useful classification system for better understanding of Laccases from their physicochemical properties perspective. We also developed a publically available web tool for the characterization of Laccase protein sequences (http://lacsubpred.bioinfo.ucr.edu/). Finally, the programs used in the study are made available for researchers interested in applying the system to other enzyme classes (https://github.com/tweirick/SubClPred).

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Section: Resultsmentioning

confidence: 99%

LacSubPred: predicting subtypes of Laccases, an important lignin metabolism-related enzyme class, using in silico approaches

et al. 2014

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“…For instance, a substantial extension of the 3440 cm -1 band and a marked increase in the peak intensity were identified, which is generally interpreted as a characteristic stretching vibration due to OH groups. Moreover, a characteristic peak of 1639 cm -1 , which was assigned to the CO stretch, was detected [32][33][34][35]. It is important to note that, in the literature, recorded values might show some discrepancies due to the origin of the laccase enzyme and its production from different sources.…”

Section: Resultsmentioning

confidence: 99%

Enzymatic behavior of laccase following interaction with γ-CD and immobilization into PCL nanofibers

Canbolat

Savaş

Gültekin

2017

Analytical Biochemistry

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This study examines the effects of CD use on enzymatic activity, following enzyme immobilization into nanofibers. There is almost no research available on the change in enzyme activity following interaction with cyclodextrin and electrospun nanofiber mats together. Laccase enzyme was immobilized into nanofibrous structures by various techniques, with and without γ-CD addition, and the enzymatic activity of the laccase was analyzed. SEM, XRD, and FTIR analyses were used for the characterization of the resulting structures. Our results showed that cyclodextrin use has a positive effect on the enzyme's activity, and increases its stability. The enzymes treated by cyclodextrin showed activation after complex formation trials, and no activation loss or enzyme denaturation was detected. Our conclusions were supported by the enzyme activity test results, which also showed that immobilization by encapsulation methods gave better activity results than layering methods. Another important finding concerned the laccase's stable characteristics that helped to maintain its enzyme activation after the freeze drying process. Among all test groups, the best activity result was recorded by laccase-γ-CD complex encapsulated PCL nanofibers with 96.48 U/mg.

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