2019
DOI: 10.1096/fj.201900948rr
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Algorithm‐based coevolution network identification reveals key functional residues of the α/β hydrolase subfamilies

Abstract: Covariant residues identified by computational algorithms have provided new insights into enzyme evolutionary routes. However, the reliability and accuracy of routine statistical coupling analysis (SCA) are unable to satisfy the needs of protein engineering because SCA depends only on sequence information. Here, we set up a new SCA algorithm, SCA.SIM, by integrating structure information and MD simulation data. The more reliable covariant residues with high‐quality scores are obtained from sequence alignment w… Show more

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Cited by 8 publications
(9 citation statements)
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“…Since KeyAlloSite can calculate the functional coupling between residues outside and inside the orthosteric pocket, we wonder whether it can also be used to identify key allo-residues for enzyme activity regulation. We used Candida antarctica lipase B (CALB, PDB ID: 1TCA), which has many annotated key functional residues ( Wu et al, 2020 ), as a test case. CALB is one of the most widely used biocatalysts in academia and industry that is often applied in acylating kinetic resolution of racemic alcohols and amines and desymmetrization of diols and diacetates, and is robust and easy to express ( Wu et al, 2013 ).…”
Section: Resultsmentioning
confidence: 99%
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“…Since KeyAlloSite can calculate the functional coupling between residues outside and inside the orthosteric pocket, we wonder whether it can also be used to identify key allo-residues for enzyme activity regulation. We used Candida antarctica lipase B (CALB, PDB ID: 1TCA), which has many annotated key functional residues ( Wu et al, 2020 ), as a test case. CALB is one of the most widely used biocatalysts in academia and industry that is often applied in acylating kinetic resolution of racemic alcohols and amines and desymmetrization of diols and diacetates, and is robust and easy to express ( Wu et al, 2013 ).…”
Section: Resultsmentioning
confidence: 99%
“…For example, A225 ranked the third out of the 52 predicted residues with a Z-score of 3.04, and A225M improves the catalytic efficiency of the enzyme by about 11 folds. V37 ranked the 35th out of the 52 predicted residues with a Z-score of 1.42, and V37I improves the catalytic efficiency of the enzyme by about threefolds ( Wu et al, 2020 ). Therefore, our method can also be used to predict allosteric functional residues that are important for enzyme catalysis, providing a new computational tool for identifying mutant enzyme with improved catalytic properties.…”
Section: Resultsmentioning
confidence: 99%
“…Our method predicted a total of 52 residues from the 296 residues outside the orthosteric pocket, of which 20 residues have been annotated as functional residues in literature according to mutagenesis experiments (Figure 5A, Supplementary file 5). For example, A225 ranked the third out of the 52 predicted residues with a Z-score of 3.04, and A225M improves the catalytic efficiency of the enzyme by about 11 folds( Wu et al, 2020 ). Therefore, our method can also be used to predict allosteric functional residues that are important for enzyme catalysis, providing a new computational tool for identifying mutant enzyme with improved catalytic properties.…”
Section: Resultsmentioning
confidence: 99%
“…6A, Table S5). For example, A225 ranked the third out of the 52 predicted residues with a Z-Score of 3.04, and A225M improves the catalytic efficiency of the enzyme by about 11 folds 55 . Therefore, our method can also be used to predict allosteric functional residues that are important for enzyme catalysis, providing a new computational tool for identifying mutant enzyme with improved catalytic properties.…”
Section: Keyallosite Can Also Identify Key Allosteric Functional Resi...mentioning
confidence: 99%
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