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Persistent homology and application on residues 1 to 28 of amyloid beta peptide
Author(s) -
Gao Yaru,
Lei Fengchun,
Li Shu Xiao
Publication year - 2021
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.26026
Subject(s) - homology (biology) , peptide , principal component analysis , homology modeling , energy landscape , computational biology , chemistry , metastability , graph , crystallography , mathematics , biology , biochemistry , computer science , combinatorics , artificial intelligence , gene , enzyme , organic chemistry
This article combines the principal component analysis (PCA) with persistent homology for applications in biomolecular data analysis. We extend the technique of persistent homology to localized weighted persistent homology to fit the properties of molecules. We introduce this novel PCA in the study of the folding process of residues 1 to 28 of amyloid beta peptide in solution. We are able to determine seven metastable states of amyloid beta 1 to 28 using homology of dimension 2, corresponding to seven local minimums in the free energy landscape. We also give the transition information between the seven types and the disconnectivity graph. Our result is very robust under change of parameters. Furthermore persistent homology of dimension 1 also give consistent results. This method can be applied to different peptides and molecules.