Sequence Protein IDEntification by Randomized sequence Trascriptomic-database and Mass Spectrometry (SPIDER-TMS): a transcriptomic-proteomic integrated software for rapid identification of proteins by FTMS

The ability to identify a protein with a high probability of being correct hinges primarily on mass measurement accuracy in MS experiments and the degree of peptide sequence completeness derived from MS/MS experiments [1]. Peptide Sequence tag is very useful and accurate approach for protein identification based on tandem mass spectrometry data from one or more peptides [2] but it needs highly specialized personnel and employs the use of several steps with high time-consuming. For peptide or protein identification based upon MS/MS, several algorithms have been described in the literature [3-5]. Here we present a user-friendly bioinformatic platform, named Sequence Protein IDEntification by Randomized Sequence Database and Transcriptome-Mass Spectrometry (SPIDER-TMS), able to cross huge amount of transcriptomic and proteomic data. The software offers the possibility of providing in output directly part of the amino acid sequence of a protein, sufficient to obtain an unequivocal identification, starting from MS/MS data. Simultaneously it translates thousands of nucleotide sequences in corresponding amino acid sequences in order to create a custom protein database. This step is required for protein identification especially in case of non-model organisms and allows correlating mass spectrometric protein data with the corresponding amino acidic sequence. The potentiality of SPIDER-TMS was tested for accuracy, rapidity and versatility on experimental MS/MS data previously interpreted manually for identification of two isoforms of Arginine Kinase proteins by bottom up-sequence tag approach (C. Labella, B. Kanawati, H. Vogel, P. Schmitt-Kopplin, S. Laurino, G. Bianco, P. Falabella. Identification of two arginine kinase forms of endoparasitoid Leptomastix dactylopii venom. J. Mass Spectrom. 2015;50:756-765). SPIDER-TMS is confirmed to be a versatile platform useful in proteomic field for unequivocal identification of proteins in a high-throughput manner with enormous advantages on time, economical resource and expertise