Banumathi Sankaran

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

The paper designs de novo proteins that form autocatalytic isopeptide bonds, enabling rigid covalent protein assemblies through intramolecular and intermolecular crosslinking.

This paper designs synthetic fusogens using machine learning, showing they enable efficient cell-cell and intracellular membrane fusion with higher activity than native SNARE complexes.

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

This study presents the first crystal structure of the Ubiquilin Sti1 domain bound to a transmembrane domain, showing that ALS mutations disrupt their interaction and reveal internal placeholder sequences that regulate phase separation.

The paper identifies a hidden binding pocket in FabB that allows alternative acyl chain binding, contrasting with FabF's loss of long-chain binding upon mutation.

This paper designs de novo protein nanoparticles with integrated functional motifs, demonstrating their ability to form structured assemblies and elicit immune responses in mice.

This paper designs twistless alpha-helical repeat proteins that inhibit ice recrystallization with activity comparable to native ice-binding proteins.

The paper designs proteins with controlled dissociation rates by introducing strained excited states, enabling rapid cytokine receptor dissociation.

The paper identifies a conserved sequence motif that enables natural enzymes to use non-canonical redox cofactors, demonstrating high activity with NMN+ and other synthetic cofactors.

The paper designs TrkA agonists that activate neuronal differentiation and neurite outgrowth without triggering pain-related gene expression.

The paper develops a method to assess solution properties of designed proteins using mass spectrometry and peptide barcoding, enabling analysis of large protein libraries.

This study designs novel serine hydrolases with high catalytic efficiency using computational methods, achieving structures close to design models and distinct from natural enzymes.

This study identifies conformational flexibility in GII proteases as a key factor affecting inhibitor potency, guiding the design of broad-spectrum norovirus protease inhibitors.

This paper designs stable transmembrane beta-barrel nanopores with tailored sizes and shapes using de novo protein design, achieving conductances ranging from 110 pS to 430 pS.

This paper introduces ProteinMPNN, a deep learning model that improves protein expression, stability, and function by incorporating evolutionary and structural information.

This paper designs 3D protein crystals with specified lattice architectures using computational methods, achieving atomic accuracy and demonstrating successful experimental validation.

Designed trimers matched to viral glycoprotein termini yielded tetrahedral, octahedral and icosahedral nanoparticles with cryo-EM-verified structures that display native-like HIV Env, influenza HA and RSV F.