Protein stoichiometry is one of those aspects of bioscience that is often hard to infer or quantify. However, it is increasingly seen as more and more important in drug discovery and disease pathology. The confirmed stoichiometry between two proteins has the potential to save precious time and resources for companies and academic institutes alike.
Cellular processes are highly orchestrated and regulated events which are dependent on precise amounts of specific protein complexes, ligand, or enzymes. Therefore, the protein stoichiometry for a given protein complex is the description of the individual protein subunits. Determining stoichiometry of a given complex such as the number of antibodies to and antigen or visa versa helps us to understand the overall function of that complex. This is crucial when the stoichiometry of a given complex can change depending on the concentrations of the constituent protein subunits in a reaction mixture.
Stoichiometry in Flaviviruses
Flaviviruses are positive-stranded RNA viruses that cause significant morbidity and mortality in humans. Examples of this type of virus include: the mosquito-borne dengue virus, yellow fever virus, Japanese encephalitis virus, West Nile virus and the Zika virus. Flaviviruses are endemic in many regions of the globe, it has been estimated that 390 million Dengue virus infections occur each year, with 3.6 billion people at risk of infection in more than 100 countries1. Flaviviruses cause a variety of disease manifestations including encephalitis and paralysis, massive hepatic injury, and haemorrhagic and plasma leakage syndromes. At present, there is no specific therapy to treat flavivirus infections; only vaccines have proven effective at reducing the impact of these viruses on public health.
Almost 100 years ago, before the concept and identity of antibodies, interest in the mechanism and stoichiometry of “antibody-mediated neutralization” was debated over2. Early arguments focussed on how many antibodies would be required to neutralize a virus. One popular concept was that viruses could be neutralized following the binding of a single molecule3. The alternative theory was the “multiple hit” model that assumed viral particles would be coated with antibodies and neutralized at a critical occupancy4.
The leading theory at present is closer to the “multiple hit” theory, however there are other factors to consider: Resistant strains of flavivirus may express fewer epitopes or display them in an inaccessible manner. Other factors may limit epitope accessibility on the virion, such as steric constraints among densely arranged viral proteins5, the proximity to the viral membrane6, or carbohydrates that shield antibody-binding determinants7.
Stoichiometry doesn’t just effect antibody-antigen complexes
Alzheimer’s disease is characterized by the presence of intracellular neurofibrillary tangles and extracellular plaques which consist primarily of several Aβ peptide sequences8. It starts with the Amyloid precursor protein (APP) being cleaved to form Aβ peptides, which in turn aggregate to form Aβ fibrils that form plaques in the brain.
Figure 1: Overview of how APP cleaved Aβ monomers aggregate to form Aβ fibrils. Adapted from Jureschi et al. 2019.
Decades of research has now gone into trying to halt the formation of Aβ peptides and their oligomerization however little progress has been made despite this worldwide effort.
The Aβ peptide seen in Alzheimer’s disease is not just produced in the central nervous system (CNS) but in most cells of peripheral tissues10. Human serum albumin (HSA) has been identified as a plasma protein which binds to Aβ protofibrils and clears it from the CNS and into peripheral tissues10. Therefore, it has been proposed that dysfunctions in plasma proteins affect this clearance of Aβ protofibrils from the CNS which leads to Aβ peptide plaque deposition within the brain12.
A model of binding was proposed by Milojevic and Melancini, whereby HSA binds at least one Aβ oligomer in a specific fashion. Each of the three domains that makes up HSA binds at least one Aβ oligomer such that it can no longer bind to another Aβ monomer. Thus, inhibiting further growth of Aβ plaques11.
Figure 2: Schematic model that summarizes the prevailing stoichiometries and affinities between HSA and Aβ protofibrils. The curved dashed lines represent the possible steric hinderance between Aβ protofibrils binding to different domains. The black solid lines show the comparison of he sizes of the albumin domains and of the Aβ protofibril. The dashed arrows indicate the direction of protofibril growth. Adapted from Milojevic & Melacini, 2011
Based on this stoichiometry it was proven through Dynamic Light Scattering (DLS) that once all three domains of HSA were bound to Aβ protofibrils, the steric hinderance between these oligomers prevented them from binding to each other. Furthermore, the Aβ protofibrils were unable to bind to anymore Aβ monomers and thus Aβ assembly was halted in vivo11.
Whilst stoichiometry has shown to play a pivotal role in many protein-protein interactions, much of the technology that studies it is based on dated methods. Whilst many techniques exist to measure protein-protein interactions most do not also measure stoichiometry.
One of the latest methods that measures protein-protein interactions and their stoichiometry is microfluidic diffusional sizing (MDS). MDS has been used to measure stoichiometry of protein interactions in solution without surfaces, matrices or ionisation, thus allowing scientists to observe the proteins in their native state. For more information, click here, to read how the Fluidity One-W is changing the way protein research is being carried out
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- Jureschi, M., Lupaescu, A.V., Ion, L., Petre, B.A. and Drochioiu, G., 2019. Stoichiometry of Heavy Metal Binding to Peptides Involved in Alzheimer’s Disease: Mass Spectrometric Evidence. In Advancements of Mass Spectrometry in Biomedical Research (pp. 401-415). Springer, Cham.
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- Milojevic, J. and Melacini, G., 2011. Stoichiometry and affinity of the human serum albumin-Alzheimer's Aβ peptide interactions. Biophysical journal, 100(1), pp.183-192.
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