SARSCov2: In silico approach toward identification of unique peptides of viral protein infection.
The paper I'll discuss in this post is this one: In silico approach toward the identification of unique peptides from viral protein infection: Application to COVID-19. It's one of those pre-peer reviewed papers out of BioRxiv.
There's nothing particularly earth shattering about it; it borders on obvious, but not so obvious that anyone would think of doing it: In that sense it's creative. That said, most of the software tools mentioned are public domain, and one could in theory do it at home, which is easy to say, but these authors did do it, and thus are worthy of credit, admiration and appreciation. (It's quite possible that other people have done it as well.)
Most people working in proteomics will be familiar with the tools mentioned therein.
I mention the paper, because it's a nice tutorial on how things work; and is a reflection of the growing power of big data information systems and mass spectrometry, as well as a cheerful reminder that things may not be as bad as one sometimes hears: We have great tools for science, should we not destroy science.
The idea is fairly simple: Do an in silico translation (from the genomic data on the Sars-CoV2 virus) to the coded proteins, then do an in silico enzymatic digestion to find the possible peptides that can be detected by mass spec, eliminate peptides that exhibit cross reactivity with other sequences in endogenous proteins; eliminate those with likely analytical complications; eliminate those that represent genetic instabilities and isoforms, and voila: You have a diagnostic/developmental tool for investigating infection if you also have a nice mass spectrometer.
This graphic from the paper shows the process:
The introduction gives a nice feel for advances. When I was a kid, I used to make analytical tools called "radioimmunoassay assay" (RIA) kits, which involved labeling a species with a radioactive substance (almost always 125-I or more rarely, tritium) and then subjecting it to competitive binding experiments to an antibody to determine the concentration of the analyte. Most young scientists today never used (or necessarily even heard of) RIA kits, although at one time they were "the bee's knees." These were ultimately subsumed by other types of fluorescent/luminescent/electrophotochemical detection systems, the best known being "ELISA," now readily available commercially in monoplex (one protein at a time) and multiplex (lots of proteins at a time) formats. Collectively these systems (including RIA) are known as "ligand binding assays." (LBA).
I will be dead in a few years, but I predict that shortly after I check out of living systems, mass spectrometer will have rendered most LBA systems more or less obsolete, and the authors of the paper suggest as much.
The detection of viral proteins in body fluids can be a rapid and specific diagnostic for infection in severe acute respiratory syndrome (SARS).2–4 During the 2003 (SARS) outbreak, non-MS based methods of protein detection proved to be more successful5,6 than LCMS methods.7–9 Non-MS based methods, such as western blots, enzyme-linked immunosorbent assays (ELISAs), and protein arrays, rely on antibodies for the detection of proteins. Given recent studies concerning high variability in antibody production, LCMS-based methods are an attractive alternative approach for the rapid identification of small molecules, proteins, and peptides in clinical settings where consistency is paramount.10,11
In the 15 years since the 2003 SARS outbreak, LCMS technology has experienced a revolution led primarily by increases in the speed, sensitivity, and resolution of MS instruments. Today, protein array and antibody-based methods are falling out of favor in both research and clinical diagnostics, due in large part to the improvements in LCMS technology.12,13 A review of this growth by Grebe and Singh described a clinical lab with no LCMS systems in 1998 that completed over 2 million individual LCMS clinical assays in 2010.14 Incremental improvements in rapid sample preparation techniques, chromatography, and data processing have also contributed to the increasing use of LCMS-based clinical testing. A 2013 study demonstrated the level of advance by identifying 4,000 yeast proteins in one hour of LCMS run time, identifying approximately 75 proteins/min at a rate 100 times faster than studies a decade prior.15
The full paper is open sourced; anyone can read it. It's a little bit technical, but it is a nice overview of how things work for anyone who is interested.
Here's how their in silico analysis worked out:
The caption:
A summary of the peptide numbers as they are removed from an increasingly complex theoretical proteome background.
Good thinking. Good idea.
If we get a President who doesn't hate science like the racist moron in the office now, we will beat this disease.
