Computational Design of Defensive Proteins using Deep Learning

The project seeks to promote the computational design of defensive proteins such as nanobodies. These are small, highly stable domains derived from antibodies from a single heavy chain of camelids such as llamas and alpacas that maintain the recognition function. These domains were chosen because they can achieve affinities comparable to monoclonal antibodies and can be produced in E.coli and yeast. Because of their smaller size than antibodies they have the ability to access sites (epitopes) more difficult to access and enter inside cells.

Nanobodies can be used as a starting point for the development of new biological drugs that block the interaction of pathogens with human cells. These defensive molecules can also be used as modulators of intracellular metabolic pathways to access the inside of cells and pass the blood-brain barrier. They can also be used as detection agents when binding to fluorophores, or as radio transmitters when transporting radioactive atoms.

Members – Affiliation

Harry G. Saavedra, PhD – Centro Bio – UTEC
Ramiro Moro, PhD – University of Louisiana at Lafayette
Rodrigo Gallegos Dextre – BioingenierĂ­a – UTEC

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