In this study we systematically analyzed the elution condition of tryptic peptides and the characteristics of identified peptides in reverse phase liquid chromatography and electrospray tandem mass spectrometry (RPLC-MS/MS) analysis. Following protein digestion with trypsin, the peptide mixture was analyzed by on-line RPLC-MS/MS. Bovine serum albumin (BSA) was used to optimize acetonitrile (ACN) elution gradient for tryptic peptides, and Cytochrome C was used to retest the gradient and the sensitivity of LC-MS/MS. The characteristics of identified peptides were also analyzed. In our experiments, the suitable ACN gradient is 5% to 30% for tryptic peptide elution and the sensitivity of LC-MS/MS is 50 fmol. Analysis of the tryptic peptides demonstrated that longer (more than 10 amino acids) and multi-charge state (+2, +3) peptides are likely to be identified, and the hydropathicity of the peptides might not be related to whether it is more likely to be identified or not. The number of identified peptides for a protein might be used to estimate its loading amount under the same sample background. Moreover, in this study the identified peptides present three types of redundancy, namely identification, charge, and sequence redundancy, which may repress low abundance protein identification.

Objective: To estimate the effect of two simple filters, two or more positive peptide filter and Unified Score filter on the true positive rate of protein and peptide. Methods: Twenty-two LC-MS/MS datasets were from 18 known protein mixture. Two or more positive peptide filter and Unified Score filter were applied to the 22 datasets. The filters effect was evaluated according to the true positive rate of protein and peptide for each filter. Results: The positive rates of protein and peptide from two or more peptide filter raised from 56.49% to 92.86%-99.12% (for protein) and from 90.67% to 97.74%-99.62% (for peptide), but many positive proteins were filtered out. The positive rates of protein and peptide from Unified Score (Thermo Finnigan value 2400) were only about 35.51% and 82.99%, but after adjusted the value (3900) according to the number of false positive peptide, those positive rate raised to 63.61% (for protein) and 91.97% (for peptide). Conclusions: Two or more peptides requirement could significantly decrease false positive rate, but it also may filter out many true positive proteins especially low molecular weight and less abundant proteins. Unified Score may be a better filter than Xcorr and Delta Cn combination and the value of 3900 is found to be more suitable for this particular datasets.

The urinary proteome is known to be a valuable field of study related to organ functions. There have been several extensive urine proteome studies. However, the overlapping rate among different studies is relatively low. Whether the low overlapping rate was caused by different sample sources, preparation, separation and identification methods is unknown. Moreover, low molecular mass (<10 kDa) proteins have not been studied extensively. In this report, male and female pooled urine samples were collected from healthy volunteers. The urinary proteins were acetone precipitated, separated and identified by three approaches, 1-DE plus 1-D LC/MS/MS, direct 1-D LC/MS/MS and 2-D LC/MS/MS. 1-D tricine gels were used to separate low molecular mass proteins. The tandem mass spectra of positive identifications were quality controlled both by manual validation and using advanced mass spectrum scanner software. A total of 226 urinary proteins were identified; 171 proteins were identified by proteomics approach for the first time, including 4 male-specific proteins. Twelve low molecular mass proteins were identified. Most urinary proteins had a molecular mass between 30 and 60 kDa and a pI between 4 and 10. The apparent molecular masses of many proteins were different from theoretical ones, which indicated their post-translational modification and degradation. The effects of sample preparation, separation and identification methods on the overlapping rate of different experiments are discussed.

The combinations of gel electrophoresis or LC and mass spectrometry are two popular approaches for large scale protein identification. However, the throughput of both approaches is limited by the speed of the protein digestion process. Present research into fast protein enzymatic digestion has been focused mainly on known proteins, and it is unclear whether these results can be extrapolated to complex protein mixtures. In this study microwave technology was used to develop a fast protein preparation and enzymatic digestion method for protein mixtures. The protein mixtures in solution or in gel were prepared and digested by microwave-assisted protein enzymatic digestion, which rapidly produces peptide fragments. The peptide fragments were further analyzed by capillary LC and ESI-ion trap-MS or MALDI-TOF-MS. The technique was optimized using bovine serum albumin and then applied to human urinary proteins and yeast lysate. The method enabled preparation and digestion of protein mixtures in solution (human urinary proteins) or in gel (yeast lysate) in 6 or 25 min, respectively. Equivalent (in-solution) or better (in-gel) digestion efficiency was obtained using microwave-assisted protein enzymatic digestion compared with the standard overnight digestion method. This new application of microwave technology to protein mixture preparation and enzymatic digestion will hasten the application of proteomic techniques to biological and clinical research.

Reverse-phase liquid chromatography (RPLC) has been widely used in proteomics research for peptide separation. When protein samples are separated by RPLC and identified with electrospray ion trap mass spectrometry (ES-IT-MS), the signals of high-abundance proteins may suppress those of low-abundance proteins, a phenomenon known as abundance suppression. To what degree the abundance suppression correlates to the number of tryptic peptides in the high-abundance proteins has not been carefully investigated. We tried to answer this question by studying the mixtures digested from five known proteins. The number of identified tryptic peptides (longer than five amino acids) of the five proteins ranged from 12 to 47. Four different peptide mixtures with 10- to 100-fold abundance differences of five known proteins were separated by RPLC and identified by ES-IT-MS. Our results showed that abundance suppression was related to the tryptic peptide numbers in the high-abundance protein. Within a 100-fold protein abundance difference range, tryptic peptide numbers in the low-abundance proteins could be suppressed up to seven times by high-abundance proteins. The procedure we suggest here can help to identify low-abundance proteins co-purified with their high-abundance binding protein. The results can also help to identify specific high-abundance proteins for removal by immunoaffinity.