Comparative database search engine analysis on massive tandem mass spectra of pork-based food products for halal proteomics

Highly Sensitive Immunochromatographic Detection of Porcine Myoglobin as Biomarker for Meat Authentication Using Prussian Blue Nanozyme

This study was aimed at the sensitive immunodetection of porcine myoglobin (MG) as a species-specific biomarker in meat products. The enhanced lateral flow immunoassay (LFIA) was created in the sandwich format using monoclonal antibodies (Mab) with specificity to porcine MG and labeled by Prussian blue nanoparticles (PBNPs) as peroxidase-mimicking nanozymes. Signal amplification was provided by the colored product of oxidation catalyzed by the PBNPs. Several Mab-PBNP conjugates with different antibody loads were synthesized; the one that provided the best analytical characteristics of the LFIA was selected. Advanced optimization of the test system was carried out. As a result, the visual limit of detection (LOD) of MG was 1.5 ng/mL. Involvement of the catalytic nanozyme properties allowed the LOD to be decreased by ~9 times in comparison to the LFIA based on gold nanomarkers, and by ~27 times compared to the LFIA based on PBNP coloration. The assay time was 30 min, including catalytic enhancement. A simple technique of meat sample pre-treatment aimed at effective MG extraction and matrix disposal was proposed. The specificity of the LFIA towards the pork meat was demonstrated. The applicability of the created test system was shown by testing extracts obtained from finished meat products.

Assessment and Comparison of Database Search Engines for Peptidomic Applications

Protein database search engines are an integral component of mass spectrometry-based peptidomic analyses. Given the unique computational challenges of peptidomics, many factors must be taken into consideration when optimizing search engine selection, as each platform has different algorithms by which tandem mass spectra are scored for subsequent peptide identifications. In this study, four different database search engines, PEAKS, MS-GF+, OMSSA, and X! Tandem, were compared with Aplysia californica and Rattus norvegicus peptidomics data sets, and various metrics were assessed such as the number of unique peptide and neuropeptide identifications, and peptide length distributions. Given the tested conditions, PEAKS was found to have the highest number of peptide and neuropeptide identifications out of the four search engines in both data sets. Furthermore, principal component analysis and multivariate logistic regression were employed to determine whether specific spectral features contribute to false C-terminal amidation assignments by each search engine. From this analysis, it was found that the primary features influencing incorrect peptide assignments were the precursor and fragment ion m/z errors. Finally, an assessment employing a mixed species protein database was performed to evaluate search engine precision and sensitivity when searched against an enlarged search space containing human proteins.

Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification

Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlating tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of "omics" profiling for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric (HPLC-MS/MS) measurements without involving the stages of preprocessing and data identification seems promising. In our study, we tested the application of neural networks of two types, a 1D residual convolutional neural network (CNN) and a 3D CNN, for the classification of three cancers by analyzing metabolomic-proteomic HPLC-MS/MS data. In this work, we showed that both neural networks could classify the phenotypes of gender-mixed oncology, kidney cancer, gender-specific oncology, ovarian cancer, and the phenotype of a healthy person by analyzing 'omics' data in 'mgf' data format. The created models effectively recognized oncopathologies with a model accuracy of 0.95. Information was obtained on the remoteness of the studied phenotypes. The closest in the experiment were ovarian cancer, kidney cancer, and prostate cancer/kidney cancer. In contrast, the healthy phenotype was the most distant from cancer phenotypes and ovarian and prostate cancers. The neural network makes it possible to not only classify the studied phenotypes, but also to determine their similarity (distance matrix), thus overcoming algorithmic barriers in identifying HPLC-MS/MS spectra. Neural networks are versatile and can be applied to standard experimental data formats obtained using different analytical platforms.