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Automatic recognition of hydrophobic clusters and their correlation with protein folding units
Author(s) -
Zehfus Micheal H.
Publication year - 1995
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.5560040617
Subject(s) - folding (dsp implementation) , chemistry , lattice protein , protein folding , hydrophobic effect , cluster analysis , crystallography , protein secondary structure , cluster (spacecraft) , biochemistry , computer science , machine learning , electrical engineering , programming language , engineering
A method is described to objectively identify hydrophobic clusters in proteins of known structure. Clusters are found by examining a protein for compact groupings of side chains. Compact clusters contain seven or more residues, have an average of 65% hydrophobic residues, and usually occur in protein interiors. Although smaller clusters contain only side‐chain moieties, larger clusters enclose significant portions of the peptide backbone in regular secondary structure. These clusters agree well with hydrophobic regions assigned by more intuitive methods and many larger clusters correlate with protein domains. These results are in striking contrast with the clustering algorithm of J. Heringa and P. Argos (1991, J Mol Biol 220 :151–171). That method finds that clusters located on a protein's surface are not especially hydrophobic and average only 3–4 residues in size. Hydrophobic clusters can be correlated with experimental evidence on early folding intermediates. This correlation is optimized when clusters with less than nine hydrophobic residues are removed from the data set. This suggests that hydrophobic clusters are important in the folding process only if they have enough hydrophobic residues.