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Predicting enzyme adsorption to lignin films by calculating enzyme surface hydrophobicity.

ABSTRACT: The inhibitory action of lignin on cellulase cocktails is a major challenge to the biological saccharification of plant cell wall polysaccharides. Although the mechanism remains unclear, hydrophobic interactions between enzymes and lignin are hypothesized to drive adsorption. Here we evaluate the role of hydrophobic interactions in enzyme-lignin binding. The hydrophobicity of the enzyme surface was quantified using an estimation of the clustering of nonpolar atoms, identifying potential interaction sites. The adsorption of enzymes to lignin surfaces, measured using the quartz crystal microbalance, correlates to the hydrophobic cluster scores. Further, these results suggest a minimum hydrophobic cluster size for a protein to preferentially adsorb to lignin. The impact of electrostatic contribution was ruled out by comparing the isoelectric point (pI) values to the adsorption of proteins to lignin surfaces. These results demonstrate the ability to predict enzyme-lignin adsorption and could potentially be used to design improved cellulase cocktails, thus lowering the overall cost of biofuel production.

SUBMITTER: Sammond DW

PROVIDER: S-EPMC4110302 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Predicting enzyme adsorption to lignin films by calculating enzyme surface hydrophobicity.

Sammond Deanne W DW Yarbrough John M JM Mansfield Elisabeth E Bomble Yannick J YJ Hobdey Sarah E SE Decker Stephen R SR Taylor Larry E LE Resch Michael G MG Bozell Joseph J JJ Himmel Michael E ME Vinzant Todd B TB Crowley Michael F MF

The Journal of biological chemistry 20140529 30

The inhibitory action of lignin on cellulase cocktails is a major challenge to the biological saccharification of plant cell wall polysaccharides. Although the mechanism remains unclear, hydrophobic interactions between enzymes and lignin are hypothesized to drive adsorption. Here we evaluate the role of hydrophobic interactions in enzyme-lignin binding. The hydrophobicity of the enzyme surface was quantified using an estimation of the clustering of nonpolar atoms, identifying potential interact ...[more]

PMID: 24876380

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Project description:BACKGROUND: Laccases (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. RESULTS: In 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%. CONCLUSION: This 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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Predicting enzyme adsorption to lignin films by calculating enzyme surface hydrophobicity.

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Predicting enzyme adsorption to lignin films by calculating enzyme surface hydrophobicity.

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