Dataset Information

Molecular Dynamics-Based Allosteric Prediction Method to Design Key Residues in Threonine Dehydrogenase for Amino-Acid Production.

ABSTRACT: Allosteric proteins are considered as one of the most critical targets to design cell factories via synthetic biology approaches. Here, we proposed a molecular dynamics-based allosteric prediction method (MBAP) to screen indirect-binding sites and potential mutations for protein re-engineering. Using this MBAP method, we have predicted new sites to relieve the allosteric regulation of threonine dehydrogenase (TD) by isoleucine. An obtained mutation P441L has been verified with the ability to significantly reduce the allosteric regulation of TD in vitro assays and with the fermentation application in vivo for amino-acid production. These findings have proved the MBAP method as an effective and efficient predicting tool to find new positions of the allosteric enzymes, thus opening a new path to constructing cell factories in synthetic biology.

SUBMITTER: Wu M

PROVIDER: S-EPMC8153896 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Molecular Dynamics-Based Allosteric Prediction Method to Design Key Residues in Threonine Dehydrogenase for Amino-Acid Production.

Wu Mingyu M Sun Yu Y Zhu Meiru M Zhu Laiyu L Lü Junhong J Geng Feng F

ACS omega 20210415 16

Allosteric proteins are considered as one of the most critical targets to design cell factories via synthetic biology approaches. Here, we proposed a molecular dynamics-based allosteric prediction method (MBAP) to screen indirect-binding sites and potential mutations for protein re-engineering. Using this MBAP method, we have predicted new sites to relieve the allosteric regulation of threonine dehydrogenase (TD) by isoleucine. An obtained mutation P441L has been verified with the ability to sig ...[more]

PMID: 34056250

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Project description:BackgroundKnowledge of catalytic residues can play an essential role in elucidating mechanistic details of an enzyme. However, experimental identification of catalytic residues is a tedious and time-consuming task, which can be expedited by computational predictions. Despite significant development in active-site prediction methods, one of the remaining issues is ranked positions of putative catalytic residues among all ranked residues. In order to improve ranking of catalytic residues and their prediction accuracy, we have developed a meta-approach based method CSmetaPred. In this approach, residues are ranked based on the mean of normalized residue scores derived from four well-known catalytic residue predictors. The mean residue score of CSmetaPred is combined with predicted pocket information to improve prediction performance in meta-predictor, CSmetaPred_poc.ResultsBoth meta-predictors are evaluated on two comprehensive benchmark datasets and three legacy datasets using Receiver Operating Characteristic (ROC) and Precision Recall (PR) curves. The visual and quantitative analysis of ROC and PR curves shows that meta-predictors outperform their constituent methods and CSmetaPred_poc is the best of evaluated methods. For instance, on CSAMAC dataset CSmetaPred_poc (CSmetaPred) achieves highest Mean Average Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96). Importantly, median predicted rank of catalytic residues is the lowest (best) for CSmetaPred_poc. Considering residues ranked ≤20 classified as true positive in binary classification, CSmetaPred_poc achieves prediction accuracy of 0.94 on CSAMAC dataset. Moreover, on the same dataset CSmetaPred_poc predicts all catalytic residues within top 20 ranks for ~73% of enzymes. Furthermore, benchmarking of prediction on comparative modelled structures showed that models result in better prediction than only sequence based predictions. These analyses suggest that CSmetaPred_poc is able to rank putative catalytic residues at lower (better) ranked positions, which can facilitate and expedite their experimental characterization.ConclusionsThe benchmarking studies showed that employing meta-approach in combining residue-level scores derived from well-known catalytic residue predictors can improve prediction accuracy as well as provide improved ranked positions of known catalytic residues. Hence, such predictions can assist experimentalist to prioritize residues for mutational studies in their efforts to characterize catalytic residues. Both meta-predictors are available as webserver at: http://14.139.227.206/csmetapred/ .

Dataset Information

Molecular Dynamics-Based Allosteric Prediction Method to Design Key Residues in Threonine Dehydrogenase for Amino-Acid Production.

Publications

Molecular Dynamics-Based Allosteric Prediction Method to Design Key Residues in Threonine Dehydrogenase for Amino-Acid Production.

Similar Datasets

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