Proteomics – examining the panoply of proteins within a complex sample – has been around much longer than the word we use today to describe it. Proteomics the word was coined in around 1997 as a portmanteau coupling the now-ubiquitous “omics” suffix to the word protein. But two decades earlier two-dimensional gel electrophoresis was the cutting-edge technology that first visualised the “complete” catalogue of proteins in a sample, such as a cell lysate or blood sample.
Even the earliest 2D-gels revealed the complexity of the proteome, with beautiful trails of spots across the isoelectric point axis delineating proteins with multiple charged post-translational modifications such as phosphorylations. But almost half a century later, technology has struggled to deliver a reproducible and accurate picture of this complexity. Most proteomics methodology used today provides an enumeration of the major proteins present with poorly validated attempts at quantitation and even less focus on the subtly different variants of each “protein” present in the mixture.
Unsurprisingly in a world dominated by the molecular biology of DNA, the concept of protein has come to mean the product of translating a single mRNA from a single gene – ignoring the complexity that arises both from errors and from post-translational modifications that are both deliberate and regulated (such as phosphorylation) and those that simply damage the polypeptides (such as most oxidations). All these close-variants get consolidated in a single concept: in most people’s mind, a protein such as apoE is a homogeneous population of perfectly translated copies of the encoding gene; in reality it is a morass of subtly different chemical entities – so many in fact that hardly any two molecules of “apoE” are actually identical.
This chemical diversity within the population of molecules derived from a single gene has been termed “quantum resolution” proteomics, by analogy to the finer resolution of the quantum domain compared to “classical” physics. That it exists is interesting, but the real question is whether it matters? If this “quantum zoo” of subtly different variants is really nothing but noise (and all the variants have identical function), then the answer would clearly be a resounding ‘no’. But data is accumulating to suggest it matters a lot: resolving one variant from another – and understanding what drives their relative concentrations – may be just as important in biology as regulation of gene expression.
Some examples are already obvious: proteolytic cleavage is a well-known post-translational modification, not least because it creates a big change in the …
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