| 2. |
- Armenteros, Jose Juan Almagro, et al.
(författare)
-
Detecting sequence signals in targeting peptides using deep learning
- 2019
-
Ingår i: Life Science Alliance. - : LIFE SCIENCE ALLIANCE LLC. - 2575-1077. ; 2:5
-
Tidskriftsartikel (refereegranskat)abstract
- In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.
|
|
| 3. |
- Emanuelsson, Olof, et al.
(författare)
-
In silico prediction of the peroxisomal proteome in fungi, plants and animals.
- 2003
-
Ingår i: Journal of Molecular Biology. - 0022-2836 .- 1089-8638. ; 330:2, s. 443-456
-
Tidskriftsartikel (refereegranskat)abstract
- In an attempt to improve our abilities to predict peroxisomal proteins, we have combined machine-learning techniques for analyzing peroxisomal targeting signals (PTS1) with domain-based cross-species comparisons between eight eukaryotic genomes. Our results indicate that this combined approach has a significantly higher specificity than earlier attempts to predict peroxisomal localization, without a loss in sensitivity. This allowed us to predict 430 peroxisomal proteins that almost completely lack a localization annotation. These proteins can be grouped into 29 families covering most of the known steps in all known peroxisomal pathways. In general, plants have the highest number of predicted peroxisomal proteins, and fungi the smallest number.
|
|
