Sensitive detection of sequence similarity using combinatorial pattern discovery: a challenging study of two distantly related protein families.
We investigate the performance of combinatorial pattern discovery to detect remote sequence similarities in terms of both biological accuracy and computational efficiency for a pair of distantly related families, as a case study. The two families represent the cupredoxins and multicopper oxidases, both containing blue copper-binding domains. These families present a challenging case due to low sequence similarity, different local structure, and variable sequence conservation at their copper-binding active sites. In this study, we investigate a new approach for automatically identifying weak sequence similarities that is based on combinatorial pattern discovery. We compare its performance with a traditional, HMM-based scheme and obtain estimates for sensitivity and specificity of the two approaches. Our analysis suggests that pattern discovery methods can be substantially more sensitive in detecting remote protein relationships while at the same time guaranteeing high specificity.
Darzentas
Nikos
N
Computational Genomics Group, The European Bioinformatics Institute, EMBL Cambridge Outstation, Cambridge, UK.
Rigoutsos
Isidore
I
Ouzounis
Christos A
CA
D000595:Amino Acid Sequence; D001400:Azurin / Q000737:chemistry; D008958:Models, Molecular; D010940:Plant Proteins / Q000737:chemistry; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid
D010940:Plant Proteins; D011506:Proteins; C050107:cupredoxin; D001400:Azurin