Biology subjects

Sehgal, E.

Publications and source records attributed to Sehgal, E..

3 recordsLinked to original sources

bioRxiv2026

Generative design of sequence specific DNA binding proteins

De novo protein design has advanced rapidly in recent years, yet the programmable recognition of specific DNA sequences remains a longstanding challenge. Here we describe a deep learning based approach for designing sequence selective DNA binding proteins. Our method combines structure generation using RFdiffusion3 with explicit screening against off-target interactions using AlphaFold3. We test this approach by generating 96 designs for each of 15 diverse DNA targets and identify specific binders for 7 targets, representing a ~100-fold improvement in success rates over previous approaches. We further characterize the binding landscape using variant competition assays and randomized library screening, revealing robust sequence discrimination across diverse targets. Together, these results represent a significant step forward in de novo sequence specific DNA binder design.

synthetic biology↗

bioRxiv2025

De novo design of RNA and nucleoprotein complexes

Nucleic acids fold into sequence-dependent tertiary structures and carry out diverse biological functions, much like proteins. However, while considerable advances have been made in the de novo design of protein structure and function, the same has not yet been achieved for RNA tertiary structures of similar intricacy. Here, we describe a generative diffusion framework, RFDpoly, for generalized de novo biopolymer (RNA, DNA and protein) design, and use it to create diverse and designable RNA structures. We design RNA structures with novel folds and experimentally validate them using a combination of chemical footprinting (SHAPE-seq) and electron microscopy. We further use this approach to design protein-nucleic acid assemblies; the crystal structure of one such design is nearly identical to the design model. This work demonstrates that the principles of structure-based de novo protein design can be extended to nucleic acids, opening the door to creating a wide range of new RNA structures and protein-nucleic acid complexes.

biochemistry↗

bioRxiv2025

De novo Design of All-atom Biomolecular Interactions with RFdiffusion3

Deep learning has accelerated protein design, but most existing methods are restricted to generating protein backbone coordinates and often neglect interactions with other biomolecules. We present RFdiffusion3 (RFD3), a diffusion model that generates protein structures in the context of ligands, nucleic acids and other non-protein constellations of atoms. Because all polymer atoms are modeled explicitly, conditioning the model on complex sets of atom-level constraints for enzyme design and other challenges is both simpler and more effective than previous approaches. RFD3 achieves improved performance compared to prior approaches on a range of in silico benchmarks with one tenth the computational cost. Finally, we demonstrate the broad applicability of RFD3 by designing and experimentally characterizing DNA binding proteins and cysteine hydrolases. The ability to rapidly generate protein structures guided by complex sets of atom-level constraints in the context of arbitrary non-protein atoms should further expand the range of functions attainable through protein design.

biochemistry↗