Publications

Selected Publications, Annotated

For a current list of publications and patents, please see my Google Scholar page. Please note that my name appears variously as David Cox, David Benjamin Cox, and David Benjamin Turitz Cox in publications.

*denotes equal contribution

Ribonanza: deep learning of RNA structure through dual crowdsourcing (2024)

He S.*, Huang R.*, Townley J., Kretsch RC., Karagianes TG., Cox DBT., Blair H., Penzar D., Vyaltsev V., Aristova E., Zinkevich A., Bakulin A., Sohn H., Krstevski D., Fukui T., Tatematsu F., Uchida Y., Jang D., Lee JS., Shieh R., Ma T., Martynov E., Shugaev MV., Bukhari HST., Fujikawa K., Onodera K., Henkel C., Ron S., Romano J., Nicol JJ., Nye GP., Wu Y., Choe C., Reade W., Eterna participants, Das R. bioRxiv (2024) Preprint DOI: https://doi.org/10.1101/2024.02.24.581671

Summary: Predicting RNA structure from sequence remains challenging despite significant advances in protein structure prediction. A key obstacle is the limited availability of high-resolution experimental RNA structures, which are typically used for model training but are slow and expensive to collect. In this work from my postdoc with Rhiju Das, we demonstrate that chemical mapping experiments—which measure nucleotide-level, structure-dependent reactivity of RNA bases with chemical reagents—can serve as an alternative data source for learning RNA structure. Critically, chemical mapping data is highly scalable and relatively easy to collect, offering a new pathway to overcome the data bottleneck for RNA structure prediction. Working with Shujun He, we showed that a foundation model trained on RNA chemical mapping data and developed via a Kaggle online machine learning competition can be fine-tuned to predict secondary structure with high accuracy. This work establishes a promising approach for advancing RNA structure prediction and demonstrates that molecular structure can be learned from diverse experimental measurements.

Computational design of sequence-specific DNA-binding proteins (2023)

Glasscock CJ.*, Pecoraro R.*, McHugh R.*, Doyle LA., Chen W., Boivin O., Lonnquist B., Na E., Politanska Y., Haddox HK., Cox DBT., Norn C., Coventry B., Goreshnik I., Vafeados D., Lee GR., Gordan R., Stoddard BL., DiMaio F., Baker D. bioRxiv (2023) Preprint DOI: https://doi.org/10.1101/2023.09.20.558720

Summary: Protein-DNA interactions are crucial for fundamental biological processes, but engineering their specificity remains challenging. Motivated by difficulties I encountered re-engineering Cas12a nuclease specificity (Gao, Cox et al. Nat Biotechnol 2017), I moved to David Baker's lab to initiate a project designing de novo sequence-specific DNA-binding proteins with the goal of understanding how to computationally design sequence-specificity. Using a combination of traditional structural modeling and deep learning approaches, we successfully designed functional de novo sequence-specific DNA-binding proteins, with efforts led by Cameron Glasscock after I transitioned to residency training. This work establishes computational design principles and strategies for engineering sequence-specific protein-DNA interactions.

RNA editing with CRISPR-Cas13 (2017)

Cox DBT.*, Gootenberg JS.*, Abudayyeh OO.*, Franklin B., Kellner MJ., Joung J., Zhang F. Science. Nov 24. (2017) DOI: 10.1126/science.aaq0180

Summary: Nucleic acid editing holds significant promise for treating genetic disease. Although efforts have largely focused on DNA editing, RNA editing of pathogenic transcripts could be used to treat disease. In this project from my PhD, I led a team that combined the RNA-targeting CRISPR enzyme Cas13 with the RNA editing enzyme ADAR2 to achieve targeted editing of transcripts in mammalian cells. This work involved profiling a large number of recently discovered Cas13 enzymes, including Cas13b, which was first described in my prior work (Smargon*, Cox*, Pyzocha* et. al. Mol Cell 2017), as well as protein engineering to significantly increase the specificity of the system.

This research built on earlier efforts to achieve targeted RNA editing in mammalian cells and advanced the field, which remains highly active today. This work generated widespread attention, being featured in Nature, Science, BBC, US News & World Report, the Wall Street Journal, the Washington Post, and the Economist, among others. The technology developed in this paper was runner-up for the Science 2017 breakthrough of the year and has been explored as a clinical therapeutic.

Cas13b is a Type VI-B CRISPR-Associated RNA-Guided RNAse Differentially Regulated by Accessory Proteins Csx27 and Csx28 (2017)

Smargon AS.*, Cox DBT.*, Pyzocha N.*, Zhang K., Slaymaker IM, Gootenberg JS, Abudayyeh OA, Essletzbichler P, Shmakov S., Marakova KS., Koonin EV., Zhang F. Mol Cell. Jan 5. (2017) DOI: 10.1016/j.molcel.2016.12.023

Summary: CRISPR-Cas systems are bacterial adaptive immune systems that utilize RNA-guided nucleases to defend prokaryotic cells against mobile genetic elements, such as bacteriophages. In this work from my PhD, we describe a novel computational strategy to identify a new CRISPR system (type VI-B) which encodes the RNA-guided RNase Cas13b. We showed that this system has the unusual property of containing accessory proteins that can increase or decrease the activity of Cas13b.

This work significantly increased the number of Cas enzymes available to engineer for RNA targeting in new contexts, which was critical for the RNA editing work described above. The computational strategy we employed also suggested that the constraints defining a functional CRISPR system were broader than previously believed.

All Publications

  1. Ribonanza: deep learning of RNA structure through dual crowdsourcing. He S.*, Huang R.*, Townley J., Kretsch RC., Karagianes TG., Cox DBT., Blair H., Penzar D., Vyaltsev V., Aristova E., Zinkevich A., Bakulin A., Sohn H., Krstevski D., Fukui T., Tatematsu F., Uchida Y., Jang D., Lee JS., Shieh R., Ma T., Martynov E., Shugaev MV., Bukhari HST., Fujikawa K., Onodera K., Henkel C., Ron S., Romano J., Nicol JJ., Nye GP., Wu Y., Choe C., Reade W., Eterna participants, Das R. bioRxiv (2024) Preprint DOI: https://doi.org/10.1101/2024.02.24.581671

  2. Computational design of sequence-specific DNA-binding proteins. Glasscock CJ.*, Pecoraro R.*, McHugh R.*, Doyle LA., Chen W., Boivin O., Lonnquist B., Na E., Politanska Y., Haddox HK., Cox DBT., Norn C., Coventry B., Goreshnik I., Vafeados D., Lee GR., Gordan R., Stoddard BL., DiMaio F., Baker D. bioRxiv (2023) Preprint DOI: https://doi.org/10.1101/2023.09.20.558720

  3. A cytidine deaminase for programmable single-base RNA editing. Abudayyeh OO.*, Gootenberg JS.*, Franklin B., Koob J., Kellner MJ., Ladha A., Joung J., Kirchgatterer P., Cox DBT., Zhang F. Science July 26. (2019) DOI: 10.1126/science.aax7063

  4. RNA editing with CRISPR-Cas13. Cox DBT.*, Gootenberg JS.*, Abudayyeh OO.*, Franklin B., Kellner MJ., Joung J., Zhang F. Science. Nov 24. (2017) DOI: 10.1126/science.aaq0180

  5. RNA targeting with CRISPR-Cas13a. Abudayyeh OO., Gottenberg JS., Essletzbichler P., Han S., Juong J., Belanto JJ., Verdine V., Cox DBT., Kellner MJ., Regev A., Lander ES., Voytas DF., Ting AY., Zhang F. Nature. Oct 12. (2017) DOI: 10.1038/nature24049

  6. Engineered Cpf1 variants with altered PAM specificities. Gao L., Cox DBT., Yan WX., Mantiega JC., Schneider MW., Yamano T., Nishimasu H., Nureki O., Crosetto N., Zhang F. Nat Biotechnol. June 5. (2017) DOI: 10.1038/nbt.3900

  7. Cas13b is a Type VI-B CRISPR-Associated RNA-Guided RNAse Differentially Regulated by Accessory Proteins Csx27 and Csx28. Smargon AS.*, Cox DBT.*, Pyzocha N.*, Zhang K., Slaymaker IM, Gootenberg JS, Abudayyeh OA, Essletzbichler P, Shmakov S., Marakova KS., Koonin EV., Zhang F. Mol Cell. Jan 5. (2017) DOI: 10.1016/j.molcel.2016.12.023

  8. Diversity and evolution of Class 2 CRISPR-Cas systems. Shamkov S., Smargon A., Scott D., Cox DBT., Pyzocha N., Yan W., Abudayyeh OO., Gootenberg JS., Makarova KS., Wolf YI., Severinov KS., Zhang F., Koonin EV. Nat Rev Microbiol. Jan 23. (2017) DOI:10.1038/nrmicro.2016.184

  9. Engineering and optimising deaminase fusions for genome editing. Yang L., Briggs AW., Chew WL., Mali P., Guell M., Goodman DB., Cox DBT., Kan Y., Lesha E., Soundarajan V., Zhang F., Church G. Nat Commun. Nov 2. (2016) DOI: 10.1038/ncomms13330

  10. C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector. Abudayyeh OO.*, Gootenberg JS.*, Konermann S.*, Joung J., Slaymaker IM., Cox DBT., Shmakov S., Makarova KS., Semenova E., Minakhin L., Severinov K., Regev A., Lander ES., Koonin EV., Zhang F. Science. Jun 2. (2016). DOI: 10.1126/science.aaf5573

  11. CRISPR/Cas9 cleavage of viral DNA efficiently suppresses hepatitis B virus. Ramanan V.*, Shlomai A.*, Cox DBT.*, Schwartz RE., Michailidis E., Bhatta A., Scott DA., Zhang F., Rice CM., Bhatia SN. Sci Rep Jun 2;5:10833 (2015) DOI: 10.1038/srep10833

  12. Therapeutic genome editing: prospects and challenges. Cox DBT., Platt RJ., Zhang F. Nat Med Feb 5;21(2):121-131. (2015). DOI: 10.1038/nm.3793

  13. The progranulin cleavage products, granulins, exacerbate TDP-43 toxicity and increase TDP-43 levels. Salazar DA., Butler VJ., Argouarch AR., Hsu TY., Mason A., Nakamura A., McCurdy H., Cox DBT., Ng R., Pan G., Seely WW., Miller BL., Kao AW. J neurosci Jun 24;35(25):9315-28 (2015) DOI: 10.1523/JNEUROSCI.4808-14.2015

  14. RNA-guided editing of bacterial genomes using CRISPR-Cas systems. Jiang W., Bikard D., Cox DBT., Zhang F, Marraffini LA. Nat Biotechnol Mar 31(3):233-9 (2013) DOI: 10.1038/nbt.2508

  15. Multiplex genome engineering using CRISPR/Cas systems. Cong L*., Ran FA*., Cox DBT., Lin S., Barretto R., Habib N., Hsu PD., Wu X., Jiang W., Marraffini LA., & Zhang F. Science Feb 15;339(6121):819-23 (2013) DOI: 10.1126/science.1231143