Public articles linked to the same research event.
bioRxiv This work introduces latent generative search for binder design, a framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model, which codesigns sequence and structure together in a continuous latent space and thereby removes the inverse-folding step; in a screen of more than one million designs by multiplexed phage display, it produced more validated binders than every other method tested, its codesigned sequences surpassed post hoc redesign, it delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets, and it generated the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens.
This work introduces latent generative search for binder design, a framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model, which codesigns sequence and structure together in a continuous latent space and thereby removes the inverse-folding step; in a screen of more than one million designs by multiplexed phage display, it produced more validated binders than every other method tested, its codesigned sequences surpassed post hoc redesign, it delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets, and it generated the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens.
This work introduces latent generative search for binder design, a framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model, which codesigns sequence and structure together in a continuous latent space and thereby removes the inverse-folding step; in a screen of more than one million designs by multiplexed phage display, it produced more validated binders than every other method tested, its codesigned sequences surpassed post hoc redesign, it delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets, and it generated the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens.
This work introduces latent generative search for binder design, a framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model, which codesigns sequence and structure together in a continuous latent space and thereby removes the inverse-folding step; in a screen of more than one million designs by multiplexed phage display, it produced more validated binders than every other method tested, its codesigned sequences surpassed post hoc redesign, it delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets, and it generated the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens.