Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks
Issued Date
2023-11-23
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85177803144
Pubmed ID
37996629
Journal Title
Scientific reports
Volume
13
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific reports Vol.13 No.1 (2023) , 20626
Suggested Citation
Bhadola P., Deo N. Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks. Scientific reports Vol.13 No.1 (2023) , 20626. doi:10.1038/s41598-023-48128-y Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/91319
Title
Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
The Beta-lactamase protein family is vital in countering Beta-lactam antibiotics, a widely used antimicrobial. To enhance our understanding of this family, we adopted a novel approach employing a multiplex network representation of its multiple sequence alignment. Each network layer, derived from the physiochemical properties of amino acids, unveils distinct insights into the intricate interactions among nodes, thereby enabling the identification of key motifs. Nodes with identical property signs tend to aggregate, providing evidence of the presence of consequential functional and evolutionary constraints shaping the Beta-lactamase family. We further investigate the distribution of evolutionary links across various layers. We observe that polarity manifests the highest number of unique links at lower thresholds, followed by hydrophobicity and polarizability, wherein hydrophobicity exerts dominance at higher thresholds. Further, the combinations of polarizability and volume, exhibit multiple simultaneous connections at all thresholds. The combination of hydrophobicity, polarizability, and volume uncovers shared links exclusive to these layers, implying substantial evolutionary impacts that may have functional or structural implications. By assessing the multi-degree of nodes, we unveil the hierarchical influence of properties at each position, identifying crucial properties responsible for the protein's functionality and providing valuable insights into potential targets for modulating enzymatic activity.