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RATIONALE One of the most challenging tasks of proteomics is peptide de novo sequencing. 4- Sulfophenyl isothiocyanate (SPITC) peptide derivatization enables acquisition of high- quality tandem mass spectra (MS/MS) for de novo sequencing, but unwanted non-specific reactions and reduced mass spectra (MS) signal intensities still represent the obstacles in high-throughput de novo sequencing. METHODS We developed a SPITC peptide derivatization procedure under acidic conditions (pH ≤5). Derivatized peptides were analyzed by matrix- assisted laser desorption/ionization (MALDI-MS) in negative ion mode followed by MS/MS in positive ion mode. A de novo sequencing tool, named DUST, adjusted to SPITC chemistry, was designed for successful high-throughput peptide de novo sequencing. This high-throughput peptide de novo sequencing was tested on Fusarium delphinoides, an organism with an uncharacterized genome. RESULTS The SPITC derivatization procedure under acidic conditions produced a significantly improved MS dataset in comparison to commonly used derivatization under basic conditions. Signal intensities were 6 to 10 times greater and the over-sulfonation effect measured on lysine- containing peptides was significantly decreased. Furthermore, development of a novel DUST algorithm enabled automated de novo sequencing with the calculated accuracy of 70.6%. CONCLUSIONS The SPITC derivatization and de novo sequencing approach outlined here provides a reliable method for high-throughput peptide de novo sequencing. High-throughput peptide de novo sequencing enabled protein mutation identification and identification of proteins from organisms with non-sequenced genomes.

RATIONALE We describe a novel negative chemically activated fragmentation/positive chemically activated fragmentation (CAF-/CAF+) technique for protein identification. The technique was used to investigate Lactobacillus brevis adaptation to nutrient deprivation. METHODS The CAF-/CAF+ method enables de novo sequencing of derivate peptides with negative and positive ion mode matrix-assisted laser desorption/ionization (MALDI) tandem mass spectrometry (MS/MS). Peptide sequences obtained from MS/MS spectra were matched against the National Center for Biotechnology Information (NCBI) non-redundant (nr) database and confirmed by the mass spectrometry data of elucidated peptide mass sequences derived from the annotated genome. This improved protein identification method highlighted 36 differentially expressed proteins in the proteome of L. brevis after 75 days of starvation. RESULTS The results revealed the key differences in the metabolic pathways that are responsible for the survival of L. brevis in a hostile environment. Proteomics analysis demonstrated that numerous proteins engaged in glucose and amino-acid catabolizing pathways, glycerolipid metabolizing pathways, and stress-response mechanisms are differentially expressed after long-term starvation. Amino acid and proteomics analysis indicated that starved L. brevis metabolized arginine, glycine, and histidine from dead cells as alternative nutrient sources. The production of lactic acid also varied between the parent cells and the starved cells. CONCLUSIONS Differentially expressed proteins identified exclusively by peptide sequence reading provided promising results for CAF-/CAF+ implementation in a standard proteomics workflow (e.g., biomarker and mutation discovery and biotyping). The practical performance of a reliable de novo sequencing technique in routine proteomics analysis is emphasized in this article. Copyright © 2013 John Wiley & Sons, Ltd. Copyright

ProteinReader (PT) is an expert software developed for the analysis of spectra produced with patented in-solution CAF-/CAF+ peptide derivatization reagent. PT is developed as client-server application to analyse MS/MS spectra on MALDI TOF/TOF mass spec. Four main advantages of CAF-/CAF+ technology are: Allows to capture same peptide in positive and negative mode of operation additional accuracy control In positive mode of operation obtaining mostly Y-ions while in negative mode obtaining mostly B-ions - eased de novo readout No degradation of the sample due to use of CAF-/CAF+ derivatisation reagent Except unambiguous biotypization (species identification), antibiotic resistance can be easily find by aforementioned method ProteinReader uses two methods for peptide search: database match (NCBInr) and de novo reading. Each peptide can be read with the two spectra, in the positive and the negative ion mode. Testing of more than 200 microorganisms showed positive identification on the species level (strain was also successfully identified if present in NCBInr database). ProteinReader was developed using open source technologies (Java, Eclipse RCP, Linux, Apache Tomcat, Infobright data warehouse) and is designed to work effectively (parallel processing) in a multitier client-server architecture. CAF-/CAF+ patent: http://www.google.com/patents/EP2529233A2?cl=en PBF Section for Bioinformatics: http://proteinreader.com/ IRB Laboratory for system biomedicine: http://www.irb.hr/eng/Research/Divisions-and-Centers/Division-of-Molecular-Medicine/Laboratory-for-system-biomedicine This work was funded by HRZZ (Croatian Science Foundation) research project Clinical proteomics of microorganisms.

Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ‘‘documents’’ and it is necessary to encode properties of the amino acid sequence as ‘‘terms’’ in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ‘‘concept’’ dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.