Deducing Intracellular Distributions of Metabolic Pathways from Genomic Data

Abstract

In the recent years, a large number of genomes from a variety of different organisms have been sequenced. Most of the sequence data has been publicly released and can be assessed by interested users. However, this wealth of information is currently underexploited by scientists not directly involved in genome annotation. This is partially because sequencing, assembly, and automated annotation can be done much faster than the identification, classification, and prediction of the intracellular localization of the gene products. This part of the annotation process still largely relies on manual curation and addition of contextual information. Users of genome databases who are unfamiliar with the types of data available from (whole) genomes might therefore find themselves either overwhelmed by the vast amount and multiple layers of data or dissatisfied with less-than-meaningful analyses of the data.In this chapter we present procedures and approaches to identify and characterize gene models of enzymes involved in metabolic pathways based on their similarity to known sequences. Furthermore we describe how to predict the subcellular location of the proteins using publicly available prediction servers and how to interpret the obtained results. The strategies we describe are generally applicable to organisms with primary plastids such as land plants or green algae. Additionally, we describe strategies suitable for those groups of algae with secondary plastids (for instance diatoms), which are characterized by a different cellular topology and a larger number of intracellular compartments compared to plants.

Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage

“…The phenylalanine at the +1 position of the transit peptide is part of a conserved sequence motif ('ASAFAP' motif) surrounding the signal peptide cleavage site in diatoms Gruber et al, 2007), cryptophytes (Gould et al, 2006a;Patron and Waller, 2007), and dinoflagellates (Patron et al, 2005;Patron and Waller, 2007). This distinctive motif is a good marker for identifying nuclear-encoded plastid proteins based on DNA sequence data Gruber and Kroth, 2014).…”

Intracellular metabolic pathway distribution in diatoms and tools for genome-enabled experimental diatom research

“…Bioinformatic prediction of the intracellular location of a protein has become an important tool in genome annotations and has been summarized in a number of reviews or book chapters [43][44][45][46]. In the following, we give a brief summary of the general considerations and then concentrate on the methods/resources available for diatoms.…”

Nucleotide Transport and Metabolism in Diatoms

“…However, plastid proteins further possess a transit peptide, and they can be recognized by a distinct sequence motif at the signal peptide cleavage site, which, in reference to its one letter code amino acid sequence is called "ASAFAP"-motif [37,38]. This motif facilitates the prediction of diatom plastid proteins by manual sequence evaluation [33,51], or by prediction software [46,52]. In addition to proteins with plastid targeting pre-sequences consisting of signal peptides, transit peptides and "ASAFAP"-motif at the signal peptide cleavage site, sequences with signal and transit peptides but without "ASAFAP"-motif are also found, and these proteins are thought to be targeted to the periplastidic space [14][15][16]53,54].…”