Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Temporal analysis of sucrose-induced phosphorylation changes in plasma membrane proteins of Arabidopsis

Sucrose is the main product of photosynthesis and the most common transport form of carbon in plants. In addition, sucrose is a compound that serves as a signal affecting metabolic flux and development. Here we provide first results of externally induced phosphorylation changes of plasma membrane proteins in Arabidopsis. In an unbiased approach, seedlings were grown in liquid medium with sucrose and then depleted of carbon before sucrose was resupplied. Plasma membranes were purified, and phosphopeptides were enriched and subsequently analyzed quantitatively by mass spectrometry. In total, 67 phosphopeptides were identified, most of which were quantified over five time points of sucrose resupply. Among the identified phosphorylation sites, the well described phosphorylation site at the C terminus of plasma membrane H(+)-ATPases showed a relative increase in phosphorylation level in response to sucrose. This corresponded to a significant increase of proton pumping activity of plasma membrane vesicles from sucrose-supplied seedlings. A new phosphorylation site was identified in the plasma membrane H(+)-ATPase AHA1 and/or AHA2. This phosphorylation site was shown to be crucial for ATPase activity and overrode regulation via the well known C-terminal phosphorylation site. Novel phosphorylation sites were identified for both receptor kinases and cytosolic kinases that showed rapid increases in relative intensities after short times of sucrose treatment. Seven response classes were identified including non-responsive, rapid increase (within 3 min), slow increase, and rapid decrease. Relative quantification of phosphorylation changes by phosphoproteomics provides a means for identification of fast responses to external stimuli in plants as a basis for further functional characterization.

Comparative evaluation of tandem MS search algorithms using a target-decoy search strategy

Peptide identification of tandem mass spectra by a variety of available search algorithms forms the foundation for much of modern day mass spectrometry-based proteomics. Despite the critical importance of proper evaluation and interpretation of the results generated by these algorithms there is still little consistency in their application or understanding of their similarities and differences. A survey was conducted of four tandem mass spectrometry peptide identification search algorithms, including Mascot, Open Mass Spectrometry Search Algorithm, Sequest, and X! Tandem. The same input data, search parameters, and sequence library were used for the searches. Comparisons were based on commonly used scoring methodologies for each algorithm and on the results of a target-decoy approach to sequence library searching. The results indicated that there is little difference in the output of the algorithms so long as consistent scoring procedures are applied. The results showed that some commonly used scoring procedures may lead to excessive false discovery rates. Finally an alternative method for the determination of an optimal cutoff threshold is proposed.

Virtual polymorphism: finding divergent peptide matches in mass spectrometry data

The prevailing method of analyzing tandem-MS data for protein identification involves the comparison of peptide molecular weight and fragmentation data to theoretically predicted values, based on known protein sequences in databases. This is generally effective since proteins from most species under study are in the database or have sufficient homology to allow significant matching. We have encountered difficulties identifying proteins from fungal species Alternaria alternata due to significant interspecies protein sequence differences (divergence) and its absence from the database. This common household mold causes asthma and allergy problems, but the genome has not been sequenced. De novo sequencing and error-tolerant methods can facilitate protein identifications in divergent, unsequenced species. But these standard methods can be laborious and only allow single amino acid substitution, respectively. We have developed an alternative approach focusing on database engineering, predicting biologically rational polymorphism using statistically weighted amino acid substitution information held in BLOSUM62. Like other second pass methods, it is based on the initially identified protein. However, this approach allows more control over sequences to be considered, including multiple changes per peptide. The results show considerable improvement for routine protein identification and the potential for rescuing otherwise unconvincing identifications in unusually divergent species.

Peptide retention prediction applied to proteomic data analysis

A retention prediction model was developed for peptides separated in reversed-phase chromatography. The model was utilized to identify and exclude the false positive (FP) peptide identifications obtained via database search. The selected database included human proteins, as well as decoy sequences of random proteins. The FP peptide detection rate was defined either as number of retention time outliers, or random decoy sequence identifications. The FP rate for various MASCOT scores was calculated. The peptides identified in one-dimensional (1D) and two-dimensional (2D) liquid chromatography/mass spectrometry (LC/MS) experiments were validated by prediction models. Multi-dimensional LC was based on two orthogonal reversed-phase chromatography modes; prediction models were successfully applied for data filtering in both separation dimensions.

Proteomics data validation: why all must provide data

The field of proteomics has gained considerable momentum over the last years as new technologies and better instrumentation allowed the field to mature from what resembled a cottage industry into a high-throughput means to identify, characterize and quantify hundreds of proteins. The identifications and (relative) quantitation values obtained are often controversial however, as various techniques and different software platforms are used in the many laboratories worldwide. This Opinion attempts to shed some light on some of the underlying issues, and proposes certain guidelines authors can adhere to in order to allow others to validate their findings.

Proteomics studies of pancreatic cancer

Pancreatic cancer is the fourth leading cause of cancer death in the United States, with 4% survival 5 years after diagnosis. Biomarkers are desperately needed to improve earlier, more curable cancer diagnosis and to develop new effective therapeutic targets. The development of quantitative proteomics technologies in recent years offers great promise for understanding the complex molecular events of tumorigenesis at the protein level, and has stimulated great interest in applying the technology for pancreatic cancer studies. Proteomic studies of pancreatic tissues, juice, serum/plasma, and cell lines have recently attempted to identify differentially expressed proteins in pancreatic cancer to dissect the abnormal signaling pathways underlying oncogenesis, and to detect new biomarkers. It can be expected that the continuing evolution of proteomics technology with better resolution and sensitivity will greatly enhance our capability in combating pancreatic cancer.