ASAFind 2.0: multi-class protein targeting prediction for diatoms and algae with complex plastids
Jazyk angličtina Země Anglie, Velká Británie Médium print
Typ dokumentu časopisecké články
Grantová podpora
Parazitologický ústav, Akademie Věd České Republiky
National Science Foundation
LM2023055
Ministry of Education, Youth and Science
21-26115S
Czech Science Foundation
23-06203S
Czech Science Foundation
PubMed
40464854
PubMed Central
PMC12136025
DOI
10.1111/tpj.70138
Knihovny.cz E-zdroje
- Klíčová slova
- chloroplast, diatoms, evolution, gene transfer, genome annotation, mitochondria, organelle, periplastidic compartment, protein transport, secretory pathway, technical advance,
- MeSH
- bílkoviny řas * metabolismus MeSH
- plastidy * metabolismus MeSH
- proteom MeSH
- Rhodophyta metabolismus MeSH
- rozsivky * metabolismus genetika MeSH
- software * MeSH
- výpočetní biologie * metody MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- bílkoviny řas * MeSH
- proteom MeSH
Plastids of diatoms and related algae with complex plastids of red algal origin are surrounded by four membranes, which also define the periplastidic compartment (PPC), the space between the second and third membranes. Metabolic reactions as well as cell biological processes take place in the PPC; however, genome-wide predictions of the proteins targeted to this compartment were so far based on manual annotation work. Using published experimental protein localizations as reference data, we developed the first automatic prediction method for PPC proteins, which we included as a new feature in an updated version of the plastid protein predictor ASAFind. With our method, at least a subset of the PPC proteins can be predicted with high specificity, with an estimate of at least 81 proteins (0.7% of the predicted proteome) targeted to the PPC in the model diatom Phaeodactylum tricornutum. The proportion of PPC proteins varies, since 180 PPC proteins (1.3% of the predicted proteome) were predicted in the genome of the diatom Thalassiosira pseudonana. The new ASAFind version can also generate a newly designed graphical output that visualizes the contribution of each position in the sequence to the score and accepts the output of the recent versions of SignalP (5.0) and TargetP (2.0) as input data. Furthermore, we release a script to calculate custom scoring matrices that can be used for predictions in a simplified score cut-off mode. This allows for adjustments of the method to other groups of algae.
Biology Centre Institute of Parasitology Czech Academy of Sciences České Budějovice Czech Republic
Faculty of Science University of South Bohemia České Budějovice Czech Republic
School of Oceanography University of Washington Seattle Washington USA
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