DocumentCode
1760829
Title
Burial Level Change Defines a High Energetic Relevance for Protein Binding Interfaces
Author
Zhenhua Li ; Ying He ; Limsoon Wong ; Jinyan Li
Author_Institution
Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Volume
12
Issue
2
fYear
2015
fDate
March-April 2015
Firstpage
410
Lastpage
421
Abstract
Protein-protein interfaces defined through atomic contact or solvent accessibility change are widely adopted in structural biology studies. But, these definitions cannot precisely capture energetically important regions at protein interfaces. The burial depth of an atom in a protein is related to the atom´s energy. This work investigates how closely the change in burial level of an atom/residue upon complexation is related to the binding. Burial level change is different from burial level itself. An atom deeply buried in a monomer with a high burial level may not change its burial level after an interaction and it may have little burial level change. We hypothesize that an interface is a region of residues all undergoing burial level changes after interaction. By this definition, an interface can be decomposed into an onion-like structure according to the burial level change extent. We found that our defined interfaces cover energetically important residues more precisely, and that the binding free energy of an interface is distributed progressively from the outermost layer to the core. These observations are used to predict binding hot spots. Our approach´s F-measure performance on a benchmark dataset of alanine mutagenesis residues is much superior or similar to those by complicated energy modeling or machine learning approaches.
Keywords
binding energy; bioinformatics; free energy; learning (artificial intelligence); molecular biophysics; molecular configurations; proteins; F-measure performance; alanine mutagenesis residues; atomic contact; atomic energy; benchmark dataset; binding free energy; binding hot spots; burial level change; complicated energy modeling; high energetic relevance; machine learning approaches; monomer; onion-like structure; protein binding interfaces; protein-protein interfaces; solvent accessibility change; structural biology; Atomic layer deposition; Atomic measurements; Bioinformatics; Computational biology; Educational institutions; Proteins; Structural rings; O-ring; Protein interface; hot spot; protein binding;
fLanguage
English
Journal_Title
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher
ieee
ISSN
1545-5963
Type
jour
DOI
10.1109/TCBB.2014.2361355
Filename
6915879
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