In Silico Proteome-Wide Amino aCid and Elemental Composition (PACE) Analysis of Expression Proteomics Data Provides A Fingerprint of Dominant Metabolic Processes

Abstract

Proteome-wide Amino aCid and Elemental composition (PACE) analysis is a novel and informative way of interrogating the proteome. The PACE approach consists of in silico decomposition of proteins detected and quantified in a proteomics experiment into 20 amino acids and five elements (C, H, N, O and S), with protein abundances converted to relative abundances of amino acids and elements. The method is robust and very sensitive; it provides statistically reliable differentiation between very similar proteomes. In addition, PACE provides novel insights into proteome-wide metabolic processes, occurring, e.g., during cell starvation. For instance, both Escherichia coli and Synechocystis down-regulate sulfur-rich proteins upon sulfur deprivation, but E. coli preferentially down-regulates cysteine-rich proteins while Synechocystis mainly down-regulates methionine-rich proteins. Due to its relative simplicity, flexibility, generality and wide applicability, PACE analysis has the potential of becoming a standard analytical tool in proteomics.

Validation of Peptide Identification Results in Proteomics Using Amino Acid Counting

“…Relative abundances of amino acid residues among peptide and protein identifications are believed to be known and definitive values. [22] Figure S1, Supporting Information shows the calculated relative frequencies for amino acid residues present in the identified proteins ( Figure S1A, Supporting Information) and peptides ( Figure S1B, Supporting Information) for the colon cancer data set. To calculate amino acid residues frequencies for proteins, we artificially cleaved proteins by trypsin rule with no missed cleavage sites.…”

HICL table can manipulate all proteins in human complete proteome

“…The numbers as showed in the header column represent the sequential numbers of the list of entries in the human complete proteome in FAST format, and the titles of columns in the header row use NO, Ala, Cys, Asp, Glu, Phe, Gly, His, Ile, Lys, Leu, Met, Asn, Pro, Gln, Arg, Ser, Thr, Val, Lysine-rich proteins have nutritive and commercial value to establish transgenic lines of cereals with high lysine content of grains [20]. It was reported that down-regulation of cysteine-rich proteins and down-regulation of methionine-rich proteins can be respectively adopted by Escherichia coli and Synechocystis to sulfur deprivation [15] . Encoded by short open reading frames (sORF), small proteins take part in the developmental processes of plant and animal [21,22] .…”

“…The values of these features can be extracted simplely from a linear amino acid sequence. AAC and AAC-derived physicochemical features are powerful features that can predict protein-protein interactions, structural and functional classes of proteins and subcellular locations [14][15][16][17] .…”