Ribosomal protein database profiling lends clarity to ribosomal protein evolution and mass distribution

Existence of theoretical ribosomal protein mass fingerprint as well as utility of ribosomal protein as biomarkers in mass spectrometry microbial identification suggests phylogenetic significance for this class of proteins. To serve the above two functions, facile means of identifying and extracting important attributes of ribosomal proteins from proteome data file of microbial species must be found. Additionally, there is a need to calculate important properties of ribosomal proteins such as molecular weight and nucleotide sequence based on amino acid sequence information from FASTA proteome file. This work sought to support the above endeavour through developing a MATLAB software that extracts the amino acid sequence information of all ribosomal proteins from the FASTA proteome datafile of a microbial species downloaded from UniProt. Built-in functions in MATLAB are subsequently employed to calculate important properties of extracted ribosomal proteins such as number of amino acid residue, molecular weight and nucleotide sequence. All information above are output, as a database, to an Excel file for ease of storage and retrieval. Data available from the analysis of an Escherichia coli K-12 proteome revealed that the bacterium possess a total of 59 ribosomal proteins distributed between the large and small ribosome subunits. The ribosomal protein ranges in sequence length from 38 (50S ribosomal protein L36) to 557 (30S ribosomal protein S1). In terms of molecular weight distribution, the profiled ribosomal proteins range in weight from 4364.305 Da (50S ribosomal protein L36) to 61157.66 Da (30S ribosomal protein S1). More important, analysis of the distribution of the molecular weight of different ribosomal proteins in E. coli reveals a smooth curve that suggests strong co-evolution of ribosomal protein sequence and mass given the tight constraints that a functional ribosome presents. Finally, cluster analysis reveals a preponderance of small ribosomal proteins compared to larger ones, which remains to be a mystery to evolutionary biologists. Overall, the information encapsulated in the ribosomal protein database should find use in gaining a better appreciation for the molecular weight distribution of ribosomal proteins in a species, as well as delivering information for using ribosomal protein biomarkers in identifying particular microbial species in mass spectrometry microbial identification. Subject areas biochemistry, microbiology, bioinformatics, biotechnology, molecular biology, Graphical abstract Short description Smooth function describes the distribution of molecular weight of ribosomal proteins in Escherichia coli, which suggests strong co-evolution pressure that fine-tunes the molecular weight of individual proteins with the constraint coming from the overall structure of the ribosome that needs to deliver a consistent function to the living cell. Multitude of ribosomal proteins with different roles in the ribosomal proteins under tight co-evolution pressure likely engender the observed smooth curve in the above plot. Highlights Automated MATLAB software for extracting ribosomal protein sequence from FASTA proteome file of a microbial species was developed. In-built functions were used to calculate nucleotide sequence, number of residues and molecular weight of each of the extracted ribosomal protein All extracted and calculated information were encapsulated as a ribosomal protein database for output to an Excel file for ease of storage and retrieval. Information in database could be useful for theoretical ribosomal protein mass fingerprint or help assign ribosomal protein biomarker peaks in matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) microbial identification.