title: Protein family comparison using statistical models and predicted structural information.
abstract: BACKGROUND
  This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles.
  
  
  RESULTS
  Our tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used.
  
  
  CONCLUSIONS
  Integration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families.
authors.affiliation: Department of Computer Science, Cornell University, Ithaca, NY 14850, USA. rc238@cornell.edu
authors.forename: Richard
authors.initials: R
authors.lastname: Chung
authors.forename: Golan
authors.initials: G
authors.lastname: Yona
mesh_terms: D000595:Amino Acid Sequence / Q000235:genetics; D015233:Models, Statistical; D011237:Predictive Value of Tests; D017433:Protein Structure, Secondary / Q000235:genetics; D011506:Proteins / Q000737:chemistry*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins