Figure 2.

FEATURE framework overview. The outline of the steps necessary to predict a possible function for a protein is illustrated. In order to build a FEATURE model, one must first define the function of interest and create positive and negative training sets from the appropriate data sources. Then, the model is trained and evaluated on the training sets. The validated model can be used for function prediction. Certain steps in the outline, such as extracting training sets and model building are straightforward, as described in section "An overview of the FEATURE system". Other steps, such as determination of data sources for training sites and application of models, are more flexible. For example, training sites may be derived manually or automatically selected using annotated hetero-groups or sequence motifs. In addition, the resulting models can be applied towards static structures from the PDB or structure prediction decoys, or for dynamic function prediction over ensembles of structures generated using molecular dynamics simulation.

Halperin et al. BMC Genomics 2008 9(Suppl 2):S2   doi:10.1186/1471-2164-9-S2-S2