Searching of Predictors to Predict pH Optimum of Cellulases

Yan, Shaomin; Wu, Guang

doi:10.1007/s12010-011-9303-2

Cited by 9 publications

(12 citation statements)

References 33 publications

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“…In conclusion, many studies have been focused on revealing the structure-function relationship of enzymes [44][45][46]. This study is consistent with our previous studies [9][10][11][12][13][14][15][16][17][18][19], demonstrating that some predictors Figure 2. Convergence of mean squared error performance function with 100 different initial weights and biases generated by random initialization function.…”

Section: Resultssupporting

confidence: 91%

“…In this situation, it is easily found that many enzymes have their sequence information but lack their optimal working conditions. Thus it is intriguing to develop methods to predict the optimal working conditions of enzymes based on their primary structure, and recently we have conducted several studies on predicting functional parameters of enzymes using amino acid properties, including pH optimum [9][10][11][12], temperature optimum [11][12][13][14][15], Michaelis-Menten constant [16][17][18] and turnover number [19]. However, more studies are needed in order to get solid conclusions.…”

Section: Introductionmentioning

confidence: 99%

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Predictors for Predicting Temperature Optimum in Beta-Glucosidases

Yan¹,

Wu²

2019

JBiSE

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This is the continuation of our studies on beta-glucosidase, which plays an important role in biological processes and recently strong interests focus on their potential role in biofeul production. In order to develop simple methods to predict the optimal working condition for beta-glucosidase, we used a 20-1 feedforward backpropagation neural network to screen possible predictors to predict the temperature optimum of beta-glucosidase from 25 amino-acid properties related to the primary structure of beta-glucosidases. The results show that the normalized polarizability index and amino-acid distribution probability can predict the temperature optimum of beta-glucosidase, which highlights a cost-effective way to predict various enzymatic parameters of beta-glucosidase.

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Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Predictors for Predicting Temperature Optimum in Beta-Glucosidases

Yan¹,

Wu²

2019

JBiSE

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“…This was the continuation of our previous study [1] along the same line with more focus on technical details because the development of predictive models was an objective of many studies, no matter what models were developed; the validation was an important part of studies. In plain words, the analyzed data are needed to divide into at least two sets: one is used for development of predictive models and another for validation of the models.…”

Section: Introductionmentioning

confidence: 75%

Exhausted Jackknife Validation Exemplified by Prediction of Temperature Optimum in Enzymatic Reaction of Cellulases

Yan

2011

Appl Biochem Biotechnol

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This was the continuation of our previous study along the same line with more focus on technical details because the data are usually divided into two datasets, one for model development and the other for model validation during the development of predictive model. The widely used validation method is the delete-1 jackknife validation. However, no systematical studies were conducted to determine whether the jackknife validation with different deletions works better because the number of validations with different deletions increases in a factorial fashion. Therefore it is only small dataset that can be used for such an exhausted study. Cellulase is an enzyme playing an important role in modern industry, and many parameters related to cellulase in enzymatic reactions were poorly documented. With increased interests in cellulases in bio-fuel industry, the prediction of parameters in enzymatic reactions is listed on agenda. In this study, two aims were defined (a) which amino acid property works better to predict the temperature optimum and (b) with which deletion the jackknife validation works. The results showed that the amino acid distribution probability works better in predicting the optimum temperature of catalytic reaction by cellulase, and the delete-4, more precisely one-fifth deletion, jackknife validation works better.

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“…When discussing optimum pH we will follow existing literature and term the pH of optimum of reaction, stability or binding, as the pH -optimum. 135,92,157,161 In terms of pH -optimum of activity, the pH -optimum varies for different enzymes and biological reactions. 28,43,25 Several examples of various pH -optima of the corresponding reaction are shown in Table 5 to indicate the broad range of pH conditions in biology.…”

Section: Interplay Between Sub-cellular Ph Ph-optimum Of Ligand Bimentioning

confidence: 99%

Protonation and pK changes in protein–ligand binding

Onufriev

Alexov

2013

Quart. Rev. Biophys.

171

161

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Formation of protein-ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein-ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor-ligand binding affinities. Statistical prevalence, magnitude, and spatial distribution of the pK and protonation state changes in protein-ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface, but sometimes far away. When receptor-ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor-ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor-ligand interactions evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes – typically computed at the same pH for all complexes – may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

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Searching of Predictors to Predict pH Optimum of Cellulases

Cited by 9 publications

References 33 publications

Predictors for Predicting Temperature Optimum in Beta-Glucosidases

Predictors for Predicting Temperature Optimum in Beta-Glucosidases

Exhausted Jackknife Validation Exemplified by Prediction of Temperature Optimum in Enzymatic Reaction of Cellulases

Protonation and pK changes in protein–ligand binding

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