Concommitant adaptation of a GH11 xylanase by directed evolution to create an alkali-tolerant/thermophilic enzyme

Ruller, Roberto; Alponti, Juliana Sanchez; Deliberto, Laila Aparecida; Zanphorlin, L.M.; Machado, Carla Botelho; Ward, Richard J.

doi:10.1093/protein/gzu027

Cited by 28 publications

(25 citation statements)

References 43 publications

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“…We take the enzyme xylanase A from the GH11 family as an example whose optimal pH is 6.0. Ruller et al generated 5 mutants on xylanase A which can survive in alkaline environment by multiple mutation experiments and determine their activity at pH 8.0 environment by wet experiments [17]. We use Eq.…”

Section: Bioinformatics Verificationmentioning

confidence: 99%

A sequence embedding method for enzyme optimal condition analysis

Dou

Sun

et al. 2020

BMC Bioinformatics

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Background An enzyme activity is influenced by the external environment. It is important to have an enzyme remain high activity in a specific condition. A usual way is to first determine the optimal condition of an enzyme by either the gradient test or by tertiary structure, and then to use protein engineering to mutate a wild type enzyme for a higher activity in an expected condition. Results In this paper, we investigate the optimal condition of an enzyme by directly analyzing the sequence. We propose an embedding method to represent the amino acids and the structural information as vectors in the latent space. These vectors contain information about the correlations between amino acids and sites in the aligned amino acid sequences, as well as the correlation with the optimal condition. We crawled and processed the amino acid sequences in the glycoside hydrolase GH11 family, and got 125 amino acid sequences with optimal pH condition. We used probabilistic approximation method to implement the embedding learning method on these samples. Based on these embedding vectors, we design a computational score to determine which one has a better optimal condition for two given amino acid sequences and achieves the accuracy 80% on the test proteins in the same family. We also give the mutation suggestion such that it has a higher activity in an expected environment, which is consistent with the previously professional wet experiments and analysis. Conclusion A new computational method is proposed for the sequence based on the enzyme optimal condition analysis. Compared with the traditional process that involves a lot of wet experiments and requires multiple mutations, this method can give recommendations on the direction and location of amino acid substitution with reference significance for an expected condition in an efficient and effective way.

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Section: Bioinformatics Verificationmentioning

confidence: 99%

A sequence embedding method for enzyme optimal condition analysis

Dou

Sun

et al. 2020

BMC Bioinformatics

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“…We take the enzyme Xylanase A from the GH11 family as an example, whose optimal pH is 6.0. Ruller, Alponti and Deliberto et al generate 5 mutants on Xylanase A which can survive in alkaline environment by multiple mutation experiments and determine their activity at 5.5 pH environment by wet experiments [19]. We use Eq.15 to calculate the activity scores of Xylanase A and the 5 mutants.…”

Section: Bioinformatics Verificationmentioning

confidence: 99%

“…The higher the score, the higher the activity of the enzyme at the same pH environment. Both the activity at 5.5 pH [19] and the scores of these enzymes calculated by our method are together listed in Table 2. By comparing the changes in activity and scores between mutants and wild-type Xylanase A, we can see that the trend of activity are consistent with the scores.…”

Section: Bioinformatics Verificationmentioning

confidence: 99%

A Sequence Embedding Method For Enzyme Optimal Condition Analysis

Dou

Sun³

et al. 2019

Preprint

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Background: An enzyme activity is influenced by the external environment condition. It is important to have an enzyme remain high activity in a specific condition. A usual way is to first determine the optimal condition of an enzyme by either the gradient test or by tertiary structure, and then to use protein engineering to mutate a wild type enzyme for a higher activity in an expected condition. Results: In this paper, we investigate the optimal condition of an enzyme by directly analyzing the sequence. We propose an embedding method to represent the amino acids and the construct information as vectors in the latent space. These vectors contain information about the correlations between amino acids and sites in the aligned amino acid sequences, as well as the correlations with the optimal conditions. We crawled and processed the amino acid sequence in glycoside hydrolase GH11 family, and got 125 amino acid sequences with optimal pH condition. We used probabilistic approximation method to implement the embedding learning method on these samples. Based on these embedding vectors, we design a computational score to determine the optimal condition for an enzyme and achieves the accuracy 80% on the test proteins in the same family. We also give the mutation suggestion such that it has a higher activity in the expected environment, which is consistent with the professional wet experiments and analysis. Conclusion: A new computational method is proposed for the sequence based enzyme optimal condition analysis. Compared with the traditional process that involves a lot of wet experiments and requires multiple mutations, this method can get the desired protein for an expected condition in an efficient and effective way. Keywords: Protein sequence analysis; Embedding; Bioinformatics

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“…Despite extensive studies, the identification of the molecular interactions that are responsible for these properties remained elusive. Most studies aimed to increase the thermal stability of mesozymes through rational design and enzyme engineering or random mutagenesis and appropriate selection methods to obtain new catalysts for biotechnological applications [6][7][8][9]. Even fewer examples exist of the laboratory evolution of thermozymes adapted to operational temperatures lower than their normal range of physiological conditions [10,11].…”

Section: Introductionmentioning

confidence: 99%

Effects of Random Mutagenesis and In Vivo Selection on the Specificity and Stability of a Thermozyme

et al. 2019

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Factors that give enzymes stability, activity, and substrate recognition result from the combination of few weak molecular interactions, which can be difficult to study through rational protein engineering approaches. We used irrational random mutagenesis and in vivo selection to test if a β-glycosidase from the thermoacidophile Saccharolobus solfataricus (Ssβ-gly) could complement an Escherichia coli strain unable to grow on lactose. The triple mutant of Ssβ-gly (S26L, P171L, and A235V) was more active than the wild type at 85 °C, inactivated at this temperature almost 300-fold quicker, and showed a 2-fold higher kcat on galactosides. The three mutations, which were far from the active site, were analyzed to test their effect at the structural level. Improved activity on galactosides was induced by the mutations. The S26L and P171L mutations destabilized the enzyme through the removal of a hydrogen bond and increased flexibility of the peptide backbone, respectively. However, the flexibility added by S26L mutation improved the activity at T > 60 °C. This study shows that random mutagenesis and biological selection allowed the identification of residues that are critical in determining thermal activity, stability, and substrate recognition.

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Concommitant adaptation of a GH11 xylanase by directed evolution to create an alkali-tolerant/thermophilic enzyme

Cited by 28 publications

References 43 publications

A sequence embedding method for enzyme optimal condition analysis

A sequence embedding method for enzyme optimal condition analysis

A Sequence Embedding Method For Enzyme Optimal Condition Analysis

Effects of Random Mutagenesis and In Vivo Selection on the Specificity and Stability of a Thermozyme

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