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Biomedical subjects

Jamie H D Cate

Publications and source records attributed to Jamie H D Cate.

3 recordsLinked to original sources
PubMed2026

Structure and evolution-guided design of minimal RNA-guided nucleases.

The design of RNA-guided nucleases with properties not limited by evolution can expand programmable genome-editing capabilities. However, generating diverse multidomain proteins with robust enzymatic properties remains challenging. Here, we use a protein design strategy that couples a structure-guided inverse-folding model with evolution-informed residue constraints to generate active, divergent variants of TnpB, a minimal CRISPR-Cas12-like nuclease, termed SynTnpBs. High-throughput screening of artificial intelligence-generated variants yielded editors that retained or exceeded wild-type activity in bacterial, plant, and human cells. Cryo-electron microscopy-based structure determination of the most divergent variant revealed stabilizing contacts in the RNA-DNA interfaces across conformations, demonstrating the design potential of this approach. Together, these results establish a strategy for creating non-natural RNA-guided nucleases and conformationally active nucleic acid binders, enlarging the designable protein space.

Humans↗
PubMed2025

Improving RNA Secondary Structure Prediction Through Expanded Training Data.

In recent years, deep learning has revolutionized protein structure prediction, achieving remarkable speed and accuracy. RNA structure prediction, however, has lagged behind. Although several methods have shown some success in predicting RNA secondary and tertiary structures, none have reached the accuracy observed with contemporary protein models. The lack of success of these RNA structure prediction models has been proposed to be due to limited high-quality structural information that can be used as training data. To probe this proposed limitation, we developed a large and diverse dataset comprising paired RNA sequences and their corresponding secondary structures. We assess the utility of this enhanced dataset by retraining on a deep learning model, SincFold. We find that SincFold exhibited improved generalization to some previously unseen RNA families, enhancing its capability to predict accurate de novo RNA secondary structures. The RNASSTR dataset provides a substantial advance for RNA structure modeling, laying a strong foundation for the development of future RNA secondary structure prediction algorithms.

Journal Article↗