All Publications

  • Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting. Cell systems Wei, J., Lotfy, P., Faizi, K., Baungaard, S., Gibson, E., Wang, E., Slabodkin, H., Kinnaman, E., Chandrasekaran, S., Kitano, H., Durrant, M. G., Duffy, C. V., Pawluk, A., Hsu, P. D., Konermann, S. 2023


    Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biological discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage. Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons. Our Cas13d guide efficiency model was successfully generalized to DjCas13d, illustrating the power of combining machine learning with ortholog discovery to advance RNA targeting in human cells.

    View details for DOI 10.1016/j.cels.2023.11.006

    View details for PubMedID 38091991

  • A Genetically Encoded Protein Polymer for Uranyl Binding and Extraction Based on the SpyTag-SpyCatcher Chemistry. ACS synthetic biology Yang, X., Wei, J., Wang, Y., Yang, C., Zhao, S., Li, C., Dong, Y., Bai, K., Li, Y., Teng, H., Wang, D., Lyu, N., Li, J., Chang, X., Ning, X., Ouyang, Q., Zhang, Y., Qian, L. 2018; 7 (10): 2331-2339


    A defining goal of synthetic biology is to develop biomaterials with superior performance and versatility. Here we introduce a purely genetically encoded and self-assembling biopolymer based on the SpyTag-SpyCatcher chemistry. We show the application of this polymer for highly efficient uranyl binding and extraction from aqueous solutions, by embedding two functional modules-the superuranyl binding protein and the monomeric streptavidin-to the polymer via genetic fusion. We further provide a modeling strategy for predicting the polymer's physical properties, and experimentally demonstrate the autosecretion of component monomers from bacterial cells. The potential of multifunctionalization, in conjunction with the genetic design and production pipeline, underscores the advantage of the SpyTag-SpyCatcher biopolymers for applications beyond trace metal enrichment and environmental remediation.

    View details for DOI 10.1021/acssynbio.8b00223

    View details for PubMedID 30261140