Protein Function Prediction

AHRD removes descriptions that contain indicators for a previous annotation transfer because the description should be as close to the primary source as possible. After that, the descriptions are deconstructed into their words, from here on referred to as tokens. Tokens known to be common to all kinds of protein descriptions and generally considered as uninformative are ignored. All others are scored by their abundance in descriptions from proteins with a high bit-score search result, a good alignment overlap and an origin in a trusted database. Then, the description candidates can be ranked according to their tokens and the top result can be used for annotation transfer to the query.

Previous versions of AHRD transferred GO annotations from reference proteins
scored solely on the characteristic of their human readable descriptions. To increase AHRD’s GO term annotation performance we implemented a candidate protein scoring procedure based directly on GO annotations. But for the prediction of GO terms it is just as important to avoid electronically transferred protein annotations as it is for the description prediction. We thus subject the candidate reference proteins for the annotation with GO terms to the same filtering steps performed on the description candidates. So AHRD’s
GO term annotation procedure also benefits from quality indicators derived from
human readable descriptions. GO terms are then scored according to how often they are found in the annotations of proteins falling under the following criteria: They are from curated databases, they have a high search score (with respect to the query), have a good alignment overlap with the query and are annotated with experimental evidence codes. The candidate protein with the highest scoring GO term annotation is then used for transfer of the function to the query.