Researchers at the University of Cambridge have designed antibodies that target the protein deposits in the brain associated with Alzheimer’s disease, and stop their production.
Alzheimer’s disease is the most common form of dementia, which affects nearly one million people in the UK and about 50 million worldwide. One of the hallmarks of Alzheimer’s disease is the build-up of protein deposits, known as plaques and tangles, in the brains of affected individuals. These deposits, which accumulate when naturally-occurring proteins in the body fold into the wrong shape and aggregate, are formed primarily of two proteins: amyloid-beta and tau.
Professor Michele Vendruscolo and his group at the Centre for Misfolding Diseases within the Department of Chemistry have been using computer-based methods to develop antibodies – the star players of the body’s natural defence system – to target the deposits of these misfolded proteins. Early tests of the antibodies in test tubes and in nematode worms showed an almost complete elimination of these pathogens.
The antibodies were designed to systematically scan the sequence of amyloid-beta, the main component of the toxic deposits associated with Alzheimer’s disease. By targeting specific regions, or epitopes, of the amyloid-beta sequence, the different antibodies were able to block amyloid-beta’s ability to stick together, or aggregate. Their results are reported in the journal Science Advances.
“In the past few years, thanks to increasingly powerful computers and large structural databases, it has become possible to design antibodies in a computer, which substantially lowers the time and cost required,” said study co-author Dr Pietro Sormanni. “It also allows us to target specific regions within the antigen, as well as to control for other properties critical for clinical applications, such as antibody stability and solubility.”
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Reference:
Francesco A. Aprile et al. ‘Selective targeting of primary and secondary nucleation pathways in Aβ42 aggregation using a rational antibody scanning method.’ Science Advances (2017). DOI: 10.1126/sciadv.1700488