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Procyk, J.

Publications and source records attributed to Procyk, J..

2 recordsLinked to original sources

High-affinity binding to the SARS-CoV-2 spike trimer by a nanostructured, trivalent protein-DNA synthetic antibody

Multivalency enables nanostructures to bind molecular targets with high affinity. Although IgG antibodies can be generated against a wide range of antigens, their shape and size cannot be tuned to match a given target. DNA nanotechnology provides an attractive approach for designing customized multivalent scaffolds due to the addressability and programmability of the nanostructure shape and size. Here, we use computational simulation to guide the design and synthesis of a DNA nanostructure-based synthetic antibody ("nano-synbody"). The nano-synbody is comprised of a three-helix bundle DNA nanostructure with three identical arms terminating in a mini-binder protein that targets the SARS-CoV-2 spike protein. The structure was designed to match the valence and distance between the three receptor binding domains (RBDs) in the spike trimer, in order to enhance binding through avidity effects. Moreover, the design allowed for the display of one, two, or three protein-displaying arms, thereby systematically probing the effect of multivalency on binding affinity. The binding strength of the nano-synbody increased with the increasing number of arms, yielding 11.2 pM affinity ([~]100-fold enhancement over monovalent binding) for the wild-type spike protein for the three-arm structure. Moreover, the multivalency was able to yield a 95 pM affinity for the Omicron variant, a mutant against which the monovalent protein was ineffective. The nano-synbody could also block infection of a spike protein-bearing pseudovirus, and similarly demonstrated effective inhibition of the Omicron variant when trimerized. The structure of the three-arm nano-synbody bound to the Omicron variant spike trimer was solved by negative-stain transmission electron microscopy reconstruction, and shows the protein-DNA nanostructure with all three arms bound to the RBD domains, confirming the intended trivalent attachment. Finally, nano-synbody binding could be reversed by removing one, two, or three arms in a programmable fashion, via toehold-mediated strand displacement. The ability to tune the size and shape of the nano-synbody, as well as its potential ability to attach (and then remove) two or more different binding ligands, will enable the high-affinity binding of a range of proteins, and pave the way towards their manipulation using DNA-based nano-robotic devices.

bioengineering↗

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection

Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target molecule of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. With the increasing amount of such high-throughput experimental data available, machine learning techniques have become increasingly popular for molecular datasets analysis. Here, we show that Restricted Boltzmann Machines (RBMs), a two-layer neural network architecture, can successfully be trained on sequence ensembles from SELEX experiments for thrombin aptamers, and used to estimate the fitness of the sequences obtained through the experimental protocol. As a direct consequence, we show that trained RBMs can be exploited to classify as well as generate novel molecules. To confirm our findings, we experimentally verify the generated sequences from RBM.

biophysics↗