Ryan D. Kibler

This paper presents a modular strategy to design programmable, highly porous protein crystals with tunable architectures and composition, enabling applications in catalysis and biomedicine.

This paper introduces scalable experimental and computational workflows to accelerate protein design by improving throughput, reducing costs, and enabling rapid validation of thousands of protein designs.

This paper introduces a generative AI framework for designing transmembrane β-barrel nanopores with customizable properties for ion sensing, DNA translocation, and transport.

This study designs genetically encoded miniproteins to modulate FAK kinase activity, showing they can inhibit or activate FAK in vitro and in cells, and adapt them to target other kinases.

This study shows that computationally designed proteins can template and confine hydroxyapatite mineralization, providing a framework for programmable protein-guided mineralization.

This paper designs Ca2+ channels with specific selectivity filter geometries using a bottom-up approach, showing higher Ca2+ conductance and structural accuracy.

This paper presents a computational strategy to design bifaceted protein nanomaterials with two distinct sides, enabling anisotropic functionalization and controlled structural variation.

The paper introduces a modular bond-centric design approach for protein assemblies, demonstrating the successful creation of multiple polyhedral cages, 2D arrays, and 3D lattices with high success rates.

This paper introduces a generative model to design helical proteins by assembling helix fragments guided by a GAN architecture, enabling rapid and computationally efficient scaffold generation.