title: DBAC: a simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts.
abstract: BACKGROUND
  A protein binding hot spot is a cluster of residues in the interface that are energetically important for the binding of the protein with its interaction partner. Identifying protein binding hot spots can give useful information to protein engineering and drug design, and can also deepen our understanding of protein-protein interaction. These residues are usually buried inside the interface with very low solvent accessible surface area (SASA). Thus SASA is widely used as an outstanding feature in hot spot prediction by many computational methods. However, SASA is not capable of distinguishing slightly buried residues, of which most are non hot spots, and deeply buried ones that are usually inside a hot spot.
  
  
  RESULTS
  We propose a new descriptor called "burial level" for characterizing residues, atoms and atomic contacts. Specifically, burial level captures the depth the residues are buried. We identify different kinds of deeply buried atomic contacts (DBAC) at different burial levels that are directly broken in alanine substitution. We use their numbers as input for SVM to classify between hot spot or non hot spot residues. We achieve F measure of 0.6237 under the leave-one-out cross-validation on a data set containing 258 mutations. This performance is better than other computational methods.
  
  
  CONCLUSIONS
  Our results show that hot spot residues tend to be deeply buried in the interface, not just having a low SASA value. This indicates that a high burial level is not only a necessary but also a more sufficient condition than a low SASA for a residue to be a hot spot residue. We find that those deeply buried atoms become increasingly more important when their burial levels rise up. This work also confirms the contribution of deeply buried interfacial atomic contacts to the energy of protein binding hot spot.
authors.affiliation: Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore.
authors.forename: Zhenhua
authors.initials: Z
authors.lastname: Li
authors.forename: Limsoon
authors.initials: L
authors.lastname: Wong
authors.forename: Jinyan
authors.initials: J
authors.lastname: Li
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D016296:Mutagenesis; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*; D013816:Thermodynamics
chemical_list: D011506:Proteins