Cavity scaling: automated refinement of cavity-aware motifs in protein function prediction.

Algorithms for geometric and chemical comparison of protein substructure can be useful for many applications in protein function prediction. These motif matching algorithms identify matches of geometric and chemical similarity between well-studied functional sites, motifs, and substructures of functionally uncharacterized proteins, targets. For the purpose of function prediction, the accuracy of motif matching algorithms can be evaluated with the number of statistically significant matches to functionally related proteins, true positives (TPs), and the number of statistically insignificant matches to functionally unrelated proteins, false positives (FPs).

Cavity-aware motifs reduce false positives in protein function prediction.

Determining the function of proteins is a problem with immense practical impact on the identification of inhibition targets and the causes of side effects. Unfortunately, experimental determination of protein function is expensive and time consuming. For this reason, algorithms for computational function prediction have been developed to focus and accelerate this effort.