Protein family comparison using statistical models and predicted structural information

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.