Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases
Issued Date
2022-05-01
Resource Type
eISSN
20797737
Scopus ID
2-s2.0-85129633646
Journal Title
Biology
Volume
11
Issue
5
Rights Holder(s)
SCOPUS
Bibliographic Citation
Biology Vol.11 No.5 (2022)
Suggested Citation
Sraphet S., Javadi B. Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases. Biology Vol.11 No.5 (2022). doi:10.3390/biology11050652 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/83247
Title
Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
The wealth of biological databases provides a valuable asset to understand evolution at a molecular level. This research presents the machine learning approach, an unsupervised agglom-erative hierarchical clustering analysis of invariant solvent accessible surface areas and conserved structural features of Amycolatopsis eburnea lipases to exploit the enzyme stability and evolution. Amycolatopsis eburnea lipase sequences were retrieved from biological database. Six structural conserved regions and their residues were identified. Total Solvent Accessible Surface Area (SASA) and structural conserved-SASA with unsupervised agglomerative hierarchical algorithm were clustered lipases in three distinct groups (99/96%). The minimum SASA of nucleus residues was related to Lipase-4. It is clearly shown that the overall side chain of SASA was higher than the backbone in all enzymes. The SASA pattern of conserved regions clearly showed the evolutionary conservation areas that stabilized Amycolatopsis eburnea lipase structures. This research can bring new insight in protein design based on structurally conserved SASA in lipases with the help of a machine learning approach.