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CE16 acetylesterases: in silico analysis, catalytic machinery prediction and comparison with related SGNH hydrolases.

ABSTRACT: Bioinformatics analysis was focused on unique acetylesterases annotated in the CAZy database within the CE16 family and simultaneously belonging to the SGNH hydrolase superfamily. The CE16 acetylesterases were compared to structurally related SGNH hydrolases: (i) selected members of the CE2, CE3, CE6, CE12 and CE17 family of the CAZy database and (ii) structural representatives of the Lipase_GDSL and Lipase_GDSL_2 families according to the Pfam database. Sequence alignment based on four conserved sequence regions (CSRs) containing active-site residues was used to calculate sequence logos specific for each CE family and to construct a phylogenetic tree. In many members of the CE16 family, aspartic acid from the Ser-His-Asp catalytic triad has been replaced by asparagine, and based on structure-sequence comparison, an alternative catalytic dyad mechanism was predicted for these enzymes. In addition to four conserved regions, CSR-I, CSR-II, CSR-III and CSR-V, containing catalytic and oxyanion-hole residues, CSR-IV was found in the CE16 family as the only CAZy family. Tertiary structures of the characterized CE16 members prepared by homology modeling showed that the α/β/α sandwich fold as well as the topology of their active sites are preserved. The phylogenetic tree and sequence alignment indicate the existence of a subfamily in the CE16 family fully consistent with the known biochemical data. In addition, nonstandard CE16 members that differ from others were analyzed and their active-site residues were predicted. A better understanding of the structure-function relationship of acetylesterases can help in the targeted design of these enzymes for biotechnology.

Supplementary information

The online version contains supplementary material available at 10.1007/s13205-020-02575-w.

SUBMITTER: Urbanikova L

PROVIDER: S-EPMC7815855 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:TUBB4A-associated disorder is a rare condition affecting the central nervous system. It displays a wide phenotypic spectrum, ranging from isolated late-onset torsion dystonia to a severe early-onset disease with developmental delay, neurological deficits, and atrophy of the basal ganglia and cerebellum, therefore complicating variant interpretation and phenotype prediction in patients carrying TUBB4A variants. We applied entropy-based normal mode analysis (NMA) to investigate genotype-phenotype correlations in TUBB4A-releated disease and to develop an in-silico approach to assist in variant interpretation and phenotype prediction in this disorder. Variants included in our analysis were those reported prior to the conclusion of data collection for this study in October 2019. All TUBB4A pathogenic missense variants reported in ClinVar and Pubmed, for which associated clinical information was available, and all benign/likely benign TUBB4A missense variants reported in ClinVar, were included in the analysis. Pathogenic variants were divided into five phenotypic subgroups. In-silico point mutagenesis in the wild-type modeled protein structure was performed for each variant. Wild-type and mutated structures were analyzed by coarse-grained NMA to quantify protein stability as entropy difference value (ΔG) for each variant. Pairwise ΔG differences between all variant pairs in each structural cluster were calculated and clustered into dendrograms. Our search yielded 41 TUBB4A pathogenic variants in 126 patients, divided into 11 partially overlapping structural clusters across the TUBB4A protein. ΔG-based cluster analysis of the NMA results revealed a continuum of genotype-phenotype correlation across each structural cluster, as well as in transition areas of partially overlapping structural clusters. Benign/likely benign variants were integrated into the genotype-phenotype continuum as expected and were clearly separated from pathogenic variants. We conclude that our results support the incorporation of the NMA-based approach used in this study in the interpretation of variant pathogenicity and phenotype prediction in TUBB4A-related disease. Moreover, our results suggest that NMA may be of value in variant interpretation in additional monogenic conditions.

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CE16 acetylesterases: in silico analysis, catalytic machinery prediction and comparison with related SGNH hydrolases.

Supplementary information

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