Table 3 Advantages and disadvantages of computational and subproteomic approaches to localization analysis.
Computational methods | Proteomics analysis |
|---|---|
Advantages | |
Rapid predictions for all proteins deduced to be encoded in a given sequence | Can be performed under different conditions and provide condition-specific information |
Detailed information about specific features of proteins, e.g. signal peptides, TMHs | Confirms expression of hypothetical proteins |
Identification of potential contaminants in subproteome analyses | Large-scale source of data on SCL for hypothetical proteins that cannot be easily predicted computationally |
Identification of hydrophobic integral membrane proteins | |
Disadvantages | |
Does not perform as well (less predictions) when analyzing an organism that is not similar to well studied/model organisms. | Time-consuming |
May miss flagging some multiply-localized proteins | Low abundance and hydrophobic proteins not readily detected |
Poorly predicts particular localizations for which there is little training data, or the proteins are computationally difficult to differentiate between localizations. | Difficult to accurately identify all proteins found on the gel |
Cannot identify condition-specific data on SCL, particularly proteins that change SCL depending on the condition. | One subcellular fraction at once analyzed |
Subfractionation often results in contamination | |
Cannot identify multiply localized proteins | |