Protein Subcellular Localization

Assigning subcellular localization to a protein is an important step towards elucidating its interaction partners, function, and potential role(s) in the cellular machinery. Computational tools offer an attractive complement to time-consuming and laborious experimental methods.

We have designed several systems for predicting the subcellular localization of eukaryotic proteins.

Lokero was the first one and only supports prediction of four different localizations. MultiLoc is more recent and integrates several sources of sequence-based information in order to assign subcellular localization and supports 11 eukaryotic localizations. MultiLoc integrates several sources of relevant sequence-based information i.e. N-terminal targeting sequences, amino acid composition, and sequence motifs, in order to provide reliable predictions on a proteome-wide scale. MultiLoc is based on support vector machines (SVMs). TargetLoc, on the other hand was constructed to compare the idea underlying MultiLoc to systems described in literature that only support 4 and 3 localizations for plant and non-plant, respectively. Our most recent creation is SherLoc, which combines the information obtained from MultiLoc with text-based information.

Prediction Services

SherLoc

MultiLoc/TargetLoc

Lokero

Höglund, A, Dönnes, P, Blum, T, Adolph, H, and Kohlbacher, O (2006). MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs, and amino acid composition Bioinformatics in press.

Höglund, A, Blum, T, Brady, S, Dönnes, P, Miguel, JS, Rocheford, M, Kohlbacher, O, and Shatkay, H (2006). Significantly improved prediction of subcellular localization by integrating text and protein sequence data In: Proceedings of the Pacific Symposium on Biocomputing (PSB 2006). PSB.

Höglund, A, Dönnes, P, Adolph, H, and Kohlbacher, O (2005). From prediction of subcellular localization to functional classification: Discrimination of DNA-packing and other nuclear proteins Online Journal of Bioinformatics 6(1):51-64.

Höglund, A, Dönnes, P, Blum, T, Adolph, H, and Kohlbacher, O (2005). Using N-terminal targeting sequences, amino acid composition, and sequence motifs for predicting protein subcellular localization In: Proceedings of the German Conference on Bioinformatics (GCB 2005), edited by Andrew Torda, Stefan Kurtz, Matthias Rarey. GI, pages 45--59.

Dönnes, P, and Höglund, A (2004). Predicting Protein Subcellular Localization: Past, Present, and Future Genomics Proteomics Bioinformatics 2(4):209--215.