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One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.
Amino acid analysis is considered to be the gold standard for quantitative peptide and protein analysis. Here, we would like to propose a simple HPLC/UV method based on a reversed-phase separation of the aromatic amino acids tyrosine (Tyr), phenylalanine (Phe), and optionally tryptophan (Trp) without any derivatization. The hydrolysis of the proteins and peptides was performed by an accelerated microwave technique, which needs only 30 minutes. Two internal standard compounds, homotyrosine (HTyr) and 4-fluorophenylalanine (FPhe) were used for calibration. The limit of detection (LOD) was estimated to be 0.05 µM (~10 µg/L) for tyrosine and phenylalanine at 215 nm. The LOD for a protein determination was calculated to be below 16 mg/L (~300 ng BSA absolute). Aromatic amino acid analysis (AAAA) offers excellent accuracy and a precision of about 5% relative standard deviation, including the hydrolysis step. The method was validated with certified reference materials (CRM) of amino acids and of a pure protein (bovine serum albumin, BSA). AAAA can be used for the quantification of aromatic amino acids, isolated peptides or proteins, complex peptide or protein samples, such as serum or milk powder, and peptides or proteins immobilized on solid supports.
Mistle: bringing spectral library predictions to metaproteomics with an efficient search index
(2023)
Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics.
Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step.
We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes.
Availability and implementation: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.