Biology subjects

Hanikel, N.

Publications and source records attributed to Hanikel, N..

4 recordsLinked to original sources

bioRxiv2025

Computational design of metalloproteases

Although significant progress has been made in creating de novo metalloenzymes that hydrolyze activated esters, the energetically demanding cleavage of amide bonds has remained a major challenge for enzyme design: amide bonds are significantly more stable than ester bonds, the amine leaving groups in proteins are not activated, and peptide substrates are flexible making them difficult to bind precisely. Here, we report the de novo design of zinc proteases from minimal catalytic motifs using RoseTTAFold Diffusion 2 for Molecular Interfaces, optimized for both enzyme and protein-protein interaction design. Of 135 computational designs experimentally tested, 36% had activity and cleaved precisely at the intended site. The most active design accelerates peptide bond hydrolysis by more than 10^8-fold relative to the uncatalyzed reaction, and by over 10^10-fold following the introduction of four point mutations that enhanced active-site preorganization4. Building on these capabilities, we designed metalloproteases that specifically cleave human TDP-43, the amyloid-{beta} peptide, and serum amyloid A with rate accelerations up to 9.2x10^8-fold over background. We illustrate the potential of our approach for bio-orthogonal control over cell state by generating caged cytokines and caged receptor antagonists that are selectively unmasked by our designed proteases. These results demonstrate that de novo enzyme design has advanced well beyond model reactions with activated substrates and open the door to design of proficient metallohydrolases for a wide range of applications in medicine and bioremediation.

biochemistry↗

bioRxiv2024

Computational Design of Metallohydrolases

De novo enzyme design starts from a description of an ideal active site composed of catalytic residues surrounding the reaction transition state(s), and builds a protein structure that contains this site1-7. Generative AI methods such as RFdiffusion11,12 now enable the direct generation of proteins around active sites, but to date, such scaffolding has required specification of both the position in the sequence and the backbone coordinates of each catalytic residue, which complicates sampling. Here we introduce a generative AI method called RFdiffusion2 that overcomes these limitations and use it to design zinc metallohydrolases starting from a density functional theory description of the active site geometry. Of an initial set of 96 designs tested experimentally, the most active has a kcat/KM of 16,000 M-1 s-1, orders of magnitude higher than previously designed metallohydrolases.6,7,13,14 A second round of 96 designs yielded 3 additional highly active enzymes, with kcat/KM up to 53,000 M-1 s-1 and kcat up to 1.5 s-1. The structures of the four enzymes are very different from each other and from the structures in the PDB. Each enzyme positions the reaction substrate almost perfectly for nucleophilic attack by a water molecule activated by the bound metal, and are predicted by PLACER15 and Chai-144 to have highly preorganized active sites. The ability to generate highly active catalysts straight out of the computer, without experimental optimization, using quantum chemistry calculated active site geometries should open the door to a new generation of potent designer enzymes.16,17

biochemistry↗

bioRxiv2024

Controlling semiconductor growth with structured de novo protein interfaces

Protein design now enables the precise arrangement of atoms on the nanometer length scales of inorganic crystal nuclei, opening up the possibility of templating the growth of metal oxides including semiconductors. We designed proteins presenting regularly repeating interfaces containing functional groups that organize ions and water molecules, and characterized their ability to bind to and template metal oxides. Two interfaces promoted the growth of hematite under conditions that otherwise resulted in the formation of magnetite. Three interfaces promoted ZnO nucleation under conditions where traditional ZnO-binding peptides and control proteins were ineffective. Designed cyclic assemblies with these ZnO nucleating interfaces lining interior cavities promoted ZnO growth within the cavity. CryoEM analysis of a designed octahedral nanocage revealed atomic density likely corresponding to the growing ZnO directly adjacent to the designed nucleation promoting interfaces. These findings demonstrate that designed proteins can direct the formation of metal oxides not observed in biological systems, opening the door to protein-semiconductor hybrid materials. One Sentence SummaryWe describe the design of structured protein interfaces that bind to, promote, and localize the growth of zinc oxide and hematite, inorganic materials which are not found in biological systems.

bioengineering↗

bioRxiv2022

Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models

There has been considerable recent progress in designing new proteins using deep learning methods1-9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher order symmetric architectures, has yet to be described. Diffusion models10,11 have had considerable success in image and language generative modeling but limited success when applied to protein modeling, likely due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding, and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold Diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of new designs. In a manner analogous to networks which produce images from user-specified inputs, RFdiffusion enables the design of diverse, complex, functional proteins from simple molecular specifications.

biochemistry↗