Objective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences.

TitleObjective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferences.
Publication TypeJournal Article
Year of Publication2010
AuthorsSantos, M. A., Turinsky A. L., Ong S., Tsai J., Berger M. F., Badis G., Talukder S., Gehrke A. R., Bulyk M. L., Hughes T. R., and Wodak S. J.
JournalNucleic Acids Res
Volume38
Issue22
Pagination7927-42
Date Published2010 Dec
ISSN1362-4962
KeywordsAnimals, DNA, Homeodomain Proteins, Mice, Sequence Analysis, Protein
Abstract

Classifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73-83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.

DOI10.1093/nar/gkq714
Alternate JournalNucleic Acids Res.
PubMed ID20705649
PubMed Central IDPMC3001082
Grant ListMOP#82940 / / Canadian Institutes of Health Research / Canada
R01 HG003985 / HG / NHGRI NIH HHS / United States