Louie2009

CREATED: 201004071641 LINK: url:~/Modules/Literature/Louie2009.pdf TITLE: A statistical model of protein sequence similarity and function similarity reveals overly-specific function predictions

Developed statistical models to predict similarity of function given sequence similarity.

Models show nearly exact function similarity for proteins with high sequence similarity (bit score > 244.7, e-value > 1e^-62, non-redundant NCBI protein database)

Only small likelihood of specific function match for proteins with low sequence similarity (bit score < 54.6, e-value < 1e^-05, NRDB).

For sequence range in between, their annotation model show an increasing relationship between function similarity and sequence similarity, but with considerable variability.

RIC predictions from the experimental model are generally lower than those predicted by the electronic model. Implies that sequence threshold applied in many electronic annotations may be below the degree of sequence similarity required to transfer exact and specific functions. Implies that electronic annotations my be overly specific.

Measures of function specificity: depth of a GO term (problematic), Information Content. Less common terms have higher IC, which is interpreter as being more specific.

Gold-standard training set using single function proteins from RefSeq and Uniprot which were experimentally characterized (containing “IDA” GO evidence codes). Resulted in 425 proteins from RefSeq (training set) and 313 proteins from Uniprot (test set).

Relationship between two GO terms:

• GO term depth of LCA term
• Relative IC, ratio of IC of LCA to mean IC of GO terms of the two proteins

It is notoriously difficult to predict function in the “twilight zone” range of sequence similarity but vitally important.

IC(t) = -log_2(p(t)) where p(t) is the probability of that term occuring in the data set = number of times that it or any of its child terms occur in a dataset