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Visualizing the superfamily of metallo-β-lactamases through sequence similarity network neighborhood connectivity analysis.

ABSTRACT: Protein sequence similarity networks (SSNs) constitute a convenient approach to analyze large polypeptide sequence datasets, and have been successfully applied to study a number of protein families over the past decade. SSN analysis is herein combined with traditional cladistic and phenetic phylogenetic analysis (respectively based on multiple sequence alignments and all-against-all three-dimensional protein structure comparisons) in order to assist the ancestral reconstruction and integrative revision of the superfamily of metallo-β-lactamases (MBLs). It is shown that only 198 out of 15,292 representative nodes contain at least one experimentally obtained protein structure in the Protein Data Bank or a manually annotated SwissProt entry, that is to say, only 1.3 % of the superfamily has been functionally and/or structurally characterized. Besides, neighborhood connectivity coloring, which measures local network interconnectivity, is introduced for detection of protein families within SSN clusters. This approach provides a clear picture of how many families remain unexplored in the superfamily, while most MBL research is heavily biased towards a few families. Further research is suggested in order to determine the SSN topological properties, which will be instrumental for the improvement of automated sequence annotation methods.

SUBMITTER: Gonzalez JM

PROVIDER: S-EPMC7785958 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Metallo-β-Lactamases and Aptamer-Based Inhibition

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Project description: Not available

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Detection of Metallo-Beta Lactamases Among Carbapenem-Resistant Pseudomonas aeruginosa.

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| S-EPMC4862526 | biostudies-literature

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