Algae and plants produce a significant portion of atmospheric oxygen during a process called photosynthesis. This takes place in specialized cell organelles called chloroplasts. These evolved from photosynthetic bacteria, in which photosynthesis originally evolved. Over the course of evolution, these bacteria became cell organelles, an integral part of plant cells. However, chloroplasts can no longer live on their own and must import most of the proteins they need from the host cell. Proteins destined for chloroplasts are recognized by cellular transport systems by virtue of an intracellular address, a specific sequence at the beginning of the protein that is necessary to target them to the correct organelle. Predicting such signals from genetic sequence data is an important tool not only for photosynthesis research. However, these signals vary between different groups of plants and algae, so methods for predicting chloroplast proteins must be adapted for different groups of algae.
Researchers from the University of South Bohemia, the Biology Centre of the Academy of Sciences of the Czech Republic, and the University of Washington have developed a software solution for predicting chloroplast proteins in diatoms, a species-rich group of algae whose contribution to global oxygen turnover is comparable to that of the Amazon rainforest. The software has now been published in the renowned scientific journal 'The Plant Journal'. The source code is open-source and is also offered as an online service by the University of South Bohemia with the support of ELIXIR-CZ and uses its storage and computing resources.
A) ASAFind scores the input sequences according to a matrix calculated from a set of known diatom plastid proteins.
B) Graphical output of ASAFind 2.0; the residues of the tested sequence are highlighted against the background of the scoring matrix.
Publication at The Plant Journal: https://onlinelibrary.wiley.com/doi/10.1111/tpj.70138
The service is available at: https://asafind.jcu.cz/
GitHub repository: https://github.com/ASAFind/ASAFind-2
The service is registered in bio.tools as: https://bio.tools/asafind
ASAFind 2.0 supports, in addition to local registration, also login via:
• Life Science AAI (preferred) - https://lifescience-ri.eu/home.html
• ORCID
• Google
