fmpRPMF: A Web Implementation for Protein Identification by Robust Peptide Mass Fingerprinting

Peptide mass fingerprinting continues to play an important role in current proteomics studies based on its good performance in sample throughput, specificity for single peptides, and insensitive to unexpected post-translational modifications as compared with . Here, we proposed and evaluated the use of feature-matching pattern-based support vector machines (SVMs) for robust protein identification. This approach is now facilitated with an updated web server (fmpRPMF) incorporated with several newly developed or improved modules and workflows allowing identification of proteins from data.

Apolipoprotein C-II is a potential serum biomarker as a prognostic factor of locally advanced cervical cancer after chemoradiation therapy

PURPOSE

To determine pretreatment serum protein levels for generally applicable measurement to predict chemoradiation treatment outcomes in patients with locally advanced squamous cell cervical carcinoma (CC).

METHODS AND MATERIALS

In a screening study, measurements were conducted twice. At first, 6 serum samples from CC patients (3 with no evidence of disease [NED] and 3 with cancer-caused death [CD]) and 2 from healthy controls were tested. Next, 12 serum samples from different CC patients (8 NED, 4 CD) and 4 from healthy controls were examined. Subsequently, 28 different CC patients (18 NED, 10 CD) and 9 controls were analyzed in the validation study. Protein chips were treated with the sample sera, and the serum protein pattern was detected by surface-enhanced laser desorption and ionization-time-of-flight mass spectrometry (SELDI-TOF MS). Then, single MS-based peptide mass fingerprinting (PMF) and tandem MS (MS/MS)-based peptide/protein identification methods, were used to identify protein corresponding to the detected peak. And then, turbidimetric assay was used to measure the levels of a protein that indicated the best match with this peptide peak.

RESULTS

The same peak 8918 m/z was identified in both screening studies. Neither the screening study nor the validation study had significant differences in the appearance of this peak in the controls and NED. However, the intensity of the peak in CD was significantly lower than that of controls and NED in both pilot studies (P=.02, P=.04) and validation study (P=.01, P=.001). The protein indicated the best match with this peptide peak at 8918 m/z was identified as apolipoprotein C-II (ApoC-II) using PMF and MS/MS methods. Turbidimetric assay showed that the mean serum levels of ApoC-II tended to decrease in CD group when compared with NED group (P=.078).

CONCLUSION

ApoC-II could be used as a biomarker for detection in predicting and estimating the radiation treatment outcome of patients with CC.

Feature-matching pattern-based support vector machines for robust peptide mass fingerprinting

Peptide mass fingerprinting, regardless of becoming complementary to tandem mass spectrometry for protein identification, is still the subject of in-depth study because of its higher sample throughput, higher level of specificity for single peptides and lower level of sensitivity to unexpected post-translational modifications compared with tandem mass spectrometry. In this study, we propose, implement and evaluate a uniform approach using support vector machines to incorporate individual concepts and conclusions for accurate PMF. We focus on the inherent attributes and critical issues of the theoretical spectrum (peptides), the experimental spectrum (peaks) and spectrum (masses) alignment. Eighty-one feature-matching patterns derived from cleavage type, uniqueness and variable masses of theoretical peptides together with the intensity rank of experimental peaks were proposed to characterize the matching profile of the peptide mass fingerprinting procedure. We developed a new strategy including the participation of matched peak intensity redistribution to handle shared peak intensities and 440 parameters were generated to digitalize each feature-matching pattern. A high performance for an evaluation data set of 137 items was finally achieved by the optimal multi-criteria support vector machines approach, with 491 final features out of a feature vector of 35,640 normalized features through cross training and validating a publicly available "gold standard" peptide mass fingerprinting data set of 1733 items. Compared with the Mascot, MS-Fit, ProFound and Aldente algorithms commonly used for MS-based protein identification, the feature-matching patterns algorithm has a greater ability to clearly separate correct identifications and random matches with the highest values for sensitivity (82%), precision (97%) and F1-measure (89%) of protein identification. Several conclusions reached via this research make general contributions to MS-based protein identification. Firstly, inherent attributes showed comparable or even greater robustness than other explicit. As an inherent attribute of an experimental spectrum, peak intensity should receive considerable attention during protein identification. Secondly, alignment between intense experimental peaks and properly digested, unique or non-modified theoretical peptides is very likely to occur in positive peptide mass fingerprinting. Finally, normalization by several types of harmonic factors, including missed cleavages and mass modification, can make important contributions to the performance of the procedure.

A partial set covering model for protein mixture identification using mass spectrometry data

Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.

Proteomic identification of Bacillus thuringiensis subsp. israelensis toxin Cry4Ba binding proteins in midgut membranes from Aedes (Stegomyia) aegypti Linnaeus (Diptera, Culicidae) larvae

Novel Bacillus thuringiensis subsp. israelensis (Bti) Cry4Ba toxin-binding proteins have been identified in gut brush border membranes of the Aedes (Stegomyia) aegypti mosquito larvae by combining 2-dimensional gel electrophoresis (2DE) and ligand blotting followed by protein identification using mass spectrometry and database searching. Three alkaline phosphatase isoforms and aminopeptidase were identified. Other Cry4Ba binding proteins identified include the putative lipid raft proteins flotillin and prohibitin, V-ATPase B subunit and actin. These identified proteins might play important roles in mediating the toxicity of Cry4Ba due to their location in the gut brush border membrane. Cadherin-type protein was not identified, although previously, we identified a midgut cadherin AgCad1 as a putative Cry4Ba receptor in Anopheles gambiae mosquito larvae [Hua, G., Zhang, R., Abdullah, M.A., Adang, M.J., 2008. Anopheles gambiae cadherin AgCad1 binds the Cry4Ba toxin of Bacillus thuringiensis israelensis and a fragment of AgCad1 synergizes toxicity. Biochemistry 47, 5101-5110]. Other identified proteins in this study that might have lesser roles include mitochondrial proteins such as ATP synthase subunits, mitochondrial processing peptidase and porin; which are likely contaminants from mitochondria and are not brush border membrane components. Trypsin-like serine protease was also identified as a protein that binds Cry4Ba. Identification of these toxin-binding proteins will lead to a better understanding of the mode of action of this toxin in mosquito.