Biology subjects

Mathis, S. V.

Publications and source records attributed to Mathis, S. V..

3 recordsLinked to original sources

bioRxiv2025

Generative inverse design of RNA structure and function with gRNAde

The design of RNA molecules with bespoke three-dimensional structures and functions is a central goal in synthetic biology and biotechnology. However, progress has been limited by the challenges of designing complex tertiary interactions such as pseudoknots, as well as engineering catalytic functions--problems that have remained largely intractable for automated methods. Here we present a high-throughput generative AI pipeline for inverse design of RNA structure and function. Central to the pipeline is gRNAde, an RNA language model conditioned on 3D backbone structures and sequence constraints. We have validated the gRNAde pipeline in a community-wide, blinded RNA design competition on the Eterna platform, where it proved able to design complex pseudoknotted RNAs at success rates matching that of human experts (95%), while significantly outperforming other physics- and AI-based automated algorithms (70%). We further demonstrate gRNAdes capabilities by generatively designing functional RNA polymerase ribozymes (RPR) with nearly 20% sequence divergence from the wild type RPR, discovering highly active variants at mutational distances inaccessible to rational design or adaptive walks by directed evolution. gRNAde thus provides an experimentally validated, open-source platform for automated design of complex RNA structures and accelerated engineering of complex RNA functions, providing a step towards programmable RNA catalysts and nanostructures. Open-source code: github.com/chaitjo/geometric-rna-design

synthetic biology↗

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↗

bioRxiv2024

gRNAde: Geometric Deep Learning for 3D RNA inverse design

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that gRNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. Open source code and tutorials are available at: github.com/chaitjo/geometric-rna-design

bioinformatics↗