Ansgar GruberBiology Centre, Institute of Parasitology, Czech Academy of Sciences, Czech RepublicFaculty of Science, University of South Bohemia, Czech Republic, Cedar McKaySchool of Oceanography, University of Washington, United States of America, Miroslav OborníkBiology Centre, Institute of Parasitology, Czech Academy of Sciences, Czech RepublicFaculty of Science, University of South Bohemia, Czech Republic, Gabrielle RocapSchool of Oceanography, University of Washington, United States of America
{"title":"Multi class intracellular protein targeting predictions in diatoms and other algae with complex plastids: ASAFind 2.0","authors":"Ansgar GruberBiology Centre, Institute of Parasitology, Czech Academy of Sciences, Czech RepublicFaculty of Science, University of South Bohemia, Czech Republic, Cedar McKaySchool of Oceanography, University of Washington, United States of America, Miroslav OborníkBiology Centre, Institute of Parasitology, Czech Academy of Sciences, Czech RepublicFaculty of Science, University of South Bohemia, Czech Republic, Gabrielle RocapSchool of Oceanography, University of Washington, United States of America","doi":"arxiv-2303.02488","DOIUrl":null,"url":null,"abstract":"Cells of diatoms and related algae with complex plastids of red algal origin\nare highly compartmentalized. These plastids are surrounded by four envelope\nmembranes, which also define the periplastidic compartment (PPC), the space\nbetween the second and third membranes. The PPC corresponds to the cytosol of\nthe eukaryotic alga that was the ancestor of the complex plastid. Metabolic\nreactions as well as cell biological processes take place in this compartment;\nhowever, its exact function remains elusive. Automated predictions of protein\nlocations proved useful for genome wide explorations of metabolism in the case\nof plastid proteins, but until now, no automated method for the prediction of\nPPC proteins was available. Here, we present an updated version of the plastid\nprotein predictor ASAFind, which includes optional prediction of PPC proteins.\nThe new ASAFind version also accepts the output of the most recent versions of\nSignalP (5.0) and TargetP (2.0) input data. Furthermore, we release a Python\nscript to calculate custom scoring matrices for adjustment of the ASAFind\nmethod to other groups of algae, and included the option to run the predictions\nwith custom scoring matrices in a simplified score cut-off mode.","PeriodicalId":501170,"journal":{"name":"arXiv - QuanBio - Subcellular Processes","volume":"82 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Subcellular Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2303.02488","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Cells of diatoms and related algae with complex plastids of red algal origin
are highly compartmentalized. These plastids are surrounded by four envelope
membranes, which also define the periplastidic compartment (PPC), the space
between the second and third membranes. The PPC corresponds to the cytosol of
the eukaryotic alga that was the ancestor of the complex plastid. Metabolic
reactions as well as cell biological processes take place in this compartment;
however, its exact function remains elusive. Automated predictions of protein
locations proved useful for genome wide explorations of metabolism in the case
of plastid proteins, but until now, no automated method for the prediction of
PPC proteins was available. Here, we present an updated version of the plastid
protein predictor ASAFind, which includes optional prediction of PPC proteins.
The new ASAFind version also accepts the output of the most recent versions of
SignalP (5.0) and TargetP (2.0) input data. Furthermore, we release a Python
script to calculate custom scoring matrices for adjustment of the ASAFind
method to other groups of algae, and included the option to run the predictions
with custom scoring matrices in a simplified score cut-off mode.