Data analysis and label-free quantification
DDA raw data acquired with the Exploris 480 were processed with MaxQuant
(version 2.0.1) (), using the standard settings and label-free
quantification (LFQ) enabled for each parameter group, i.e. control and
affinity-purified samples (LFQ min ratio count 2, stabilize large LFQ
ratios disabled, match-between-runs). Data were searched against the
forward and reverse sequences of the P. falciparum proteome
(UniProtKB/TrEMBL, 5,445 entries, UP000001450, released April 2020) and
a list of common contaminants. For peptide identification, trypsin was
set as protease allowing two missed cleavages. Carbamidomethylation was
set as fixed and oxidation of methionine as well as acetylation of
protein N-termini as variable modifications. Only peptides with a
minimum length of 7 amino acids were considered. Peptide and protein
false discovery rates (FDR) were set to 1%. In addition, proteins had
to be identified by at least two peptides. Statistical analysis of the
data was conducted using Student’s t-test, which was corrected by the
Benjamini-Hochberg (BH) method for multiple hypothesis testing (FDR of
0.01). In addition, proteins in the affinity-enriched samples had to be
identified in all three biological replicates and show at least a
two-fold enrichment compared to the controls. The datasets of protein
hits were further edited by verification of the gene IDs and gene names
via the PlasmoDB database (www.plasmodb.org; ). PlasmoDB IDs were
extracted from the fasta headers provided by mass spectrometry and
verified manually.