Recent advances in artificial intelligence and machine learning are rapidly transforming how we model biological macromolecular behavior. In proteins, tools like AlphaFold have shown, that with enough data, deep learning can accurately infer structure from sequence alone. We aim to bring this data-driven paradigm to RNA. However, AI approaches for RNA design have been hampered by their dynamic nature. RNAs are extraordinary molecules whose functions arise not from static architectures but from dynamic, continuously shifting conformational ensembles. As they fold, refold, and sample alternative helices, loops, and long-range interactions, RNAs sense metabolites, regulate translation, assemble with proteins, and catalyze reactions.
To address these challenges, we are developing high-resolution experimental and computational tools to elucidate these folding processes and incorporate this information into new RNA deep learning approaches. We pioneered the development of SHAPE-Seq that combines chemical probing and next-generation sequencing (NGS) to structurally characterize hundreds of thousands of RNAs in a single experiment, and introduced cotranscriptional SHAPE-Seq that captures RNA folding pathways at nucleotide resolution. Our most recent advance, Probing Resolved Inference of Molecular Energetics (PRIME) uses this framework to uncover quantitative, nucleotide-resolution thermodynamic information about RNA folding generating data as rigorous as NMR but at the scale of NGS. These measurements open new avenues for exploring how intrinsic fluctuations at individual nucleotides influence global folding behavior, revealing how RNAs navigate complex, out-of-equilibrium landscapes during transcription.
Our breakthroughs in structural measurement are informing new approaches to RNA deep learning, where we are integrating heterogeneous data types, such as sequence, chemical probing measurements, and 3D structural information to design functional RNAs for applications ranging from biosensing, condensate formation, gene expression control and RNA-based therapeutics.
Ultimately, our goal is to unify measurement, modeling, and design to achieve predictive control over RNA structure and function—from the behavior of individual nucleotides to the dynamics of complex, multi-state systems.
Featured Publications
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How does RNA fold dynamically?
Bushhouse, D.Z., Choi, E.K., Hertz, L.M. and Lucks, J.B.*, Journal of Molecular Biology (2022) doi:10.1016/j.jmb.2022.167665
Links: Journal, PDFAbout
In this perspective, we engage three questions regarding RNA cotranscriptional folding:
1) What is the Appropriate Biophysical Framework to Describe Non- equilibrium Conformational Switching?
2) How Widespread is Strand Displacement as a General Feature of RNA Conformational Switching?
3) How do Proteins Mediate Cotranscriptional RNA Conformational Switching? -
Chemical roadblocking of DNA transcription for nascent RNA display
Eric J. Strobel, John T. Lis, Julius B. Lucks. Journal of Biological Chemistry.(2020) doi: 10.1101/779827
Links: Journal, PDF, BioRxivAbout
Here we present a new method for stalling transcription complexes using chemically modified DNA transcription templates. Templates can be generated by using primer extension reactions that makes them inexpensive to use and amenable to multiplexing.
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Computational design of three-dimensional RNA structure and function
J. D. Yesselman, D. Eiler, E. D. Carlson, M. R. Gotrik, A. E. d’Aquino, A. N. Ooms, W. Kladawang, P. D. Carlson, X. Shi, D. A. Constantino, D. Herschlag, J. B. Lucks, M. C. Jewett, J. S. Keift, R. Das. Nature Nanotechnology. (2019). doi: 10.1038/s41565-019-0517-8.
Links: Journal, PDFAbout
This paper describes RNA-Make, a computational tool that can design dimensional RNA structures. RNA-Make is used to design tethers for tethered ribosomes and stabilizing scaffolds for fluorescent RNA aptamers. RNA-Make opens up a new capability in computational RNA design.
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DUETT quantitatively identifies known and novel events in nascent RNA structural dynamics from chemical probing data
A. Y Xu, A. M Yu, J. B. Lucks, N. Bagheri.* Bioinformatics. (2019). doi: 10.1093/bioinformatics/btz449.
Links: Journal, PDF, Blog PostAbout
Here we present a method to find reactivity patterns within cotranscriptional SHAPE-Seq datasets in an automated fashion without human intervention. Patterns of increasing or decreasing reactivities in specific nucleotides across different transcript lengths reveal cotranscriptional RNA folding events.
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Tracking RNA structures as RNAs transit through the cell
A. M Yu, J. B. Lucks*. Nature Strucutral and Molecular Biology. (2019). doi: 10.1038/s41594-019-0213-2.
Links: Journal, PDF, SharedIT (Free)About
Our news-and-views piece for Sun et al.’s report “RNA structure maps across mammalian cellular compartments” in Nature Chemical Biology. We highlight what we think is the most interesting finding, that RNA folds established near chromatin can exist throughout the cellular lifecycle of the RNA, potentially pointing to the importance of cotranscriptional folding for determining functional RNA states.
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Snapshot: RNA Structure Probing Technologies
P. D. Carlson, M. E. Evans, A. M Yu, E. J. Strobel, J. B. Lucks. Cell. (2018). doi: 10.1016/j.cell.2018.09.024
Links: Journal, PDF, Blog PostAbout
This two-page SnapShot highlights the RNA chemical probing workflow, biological questions that can be informed by these experiments, and some options for probes.
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High-throughput determination of RNA structures
E. J. Strobel, A. M Yu, J. B. Lucks. Nature Reviews Genetics. (2018). doi: 10.1038/s41576-018-0034-x
Links: Journal, PDF, Blog PostAbout
A comprehensive review of high throughput sequencing-based techniques to characterize RNA structures. This review includes a historical perspective, an overview of experimental approaches, discussion of data analysis approaches and highlights for applications to a range of questions in RNA biology. We also include a perspective on what is needed to make these approaches more accurate and more powerful.
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Estimating RNA structure chemical probing reactivities from reverse transcriptase stops and mutations
A. M Yu, M. E. Evans, J. B. Lucks. (2018) doi: 10.1101/292532
Links: BioRxivAbout
In this work, we present a derivation of RNA chemical probing reactivity calculations that includes signal from both reverse transcriptase stops and mutations. Intriguingly, the reactivity at each nucleotide is the sum of the stop signal and the mutation signal due to the mutual exclusivity of stops and mutations in a single cDNA.
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Characterizing the structure-function relationship of a naturally- occurring RNA thermometer
S. Meyer, J. B. Lucks. Biochemistry. (2017). doi: 10.1021/acs.biochem.7b01170
Links: Journal, PDF, BioRxiv (Open Access), SHAPE-Seq DataSets: RMDB EntriesAbout
Temperature-sensing RNAs known as RNA thermometers can regulate heat shock responses in bacteria. In this paper, we use SHAPE-Seq to observe how the agsA thermometer’s structure shifts in response to temperature to carry out this function.
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Probing of RNA structures in a positive sense RNA virus reveals selection pressures for structural elements
K. E. Watters, K. Choudhary, S. Aviran, J. B. Lucks, K. L. Perry, J. R. Thompson. Nucleic Acids Research. (2017). doi: 10.1093/nar/gkx1273
Links: Jounral, PDF, SHAPE-Seq DataSets: RMDB EntriesAbout
Our report on the SHAPE-Seq-determined structure of the cucumber mosaic virus and how RNA structural elements are selected for for viral infectivity.
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Mapping RNA structure in vitro with SHAPE chemistry and next-generation sequencing (SHAPE-Seq)
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Characterizing RNA structures in vitro and in vivo with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq)
K. E. Watters, A. M. Yu, E. J. Strobel, A. H. Settle, J. B. Lucks. Methods. (2016). doi: 10.1016/j.ymeth.2016.04.002
Links: Journal, PDF, BioRxiv (Open Access), SHAPE-seq DataSets: RMDB Entries, Blog PostAbout
This methods paper provides the details to understand and perform SHAPE-seq v2.1 in cells and in vitro. Two key improvement over v2.0 are increased flexibility in barcoding the cDNA library for sequencing and a selective PCR step in library preparation to reduce unwanted side products.
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Simultaneous characterization of cellular RNA structure and function with in-cell SHAPE-Seq
K. E. Watters, Timothy R. Abbott, J. B. Lucks. Nucleic Acids Research. (2015). doi: 10.1093/nar/gkv879
Links: Journal, PDF, SHAPE-Seq DataSets: RMDB Entries, Blog PostAbout
In this paper, a method is developed to characterize RNA structure and function simultaneously with SHAPE-seq and functional assays. This approach is then applied to correlate RNA structure with function of synthetic RNA-RNA regulatory pairs and endogenous RNAs in E. coli.
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Rapidly characterizing the fast dynamics of RNA genetic circuitry with cell-free transcription-translation (TX-TL) systems
M. K. Takahashi, J. Chappell, C. A. Hayes, Z. Z. Sun, J. Kim, V. Singhal, K. J. Spring, S. Al-Khabouri, C. P. Fall, V. Noireaux, R. M. Murray, J. B. Lucks. ACS Synthetic Biology. (2014). doi: 10.1021/sb400206c
Links: Journal, PDF, BioRxiv (Open Access)About
In this paper, an E. coli-based cell free transcription-translation (TX-TL) system is adapted for prototyping RNA genetic networks. This work demonstrates that the response time for an RNA cascade is approximately 5 minute, which allows for more rapid prototyping than protein-based circuits. By prototyping a single input molecule (SIM) RNA circuit in this system, the first in vivo SIM RNA circuit is created and characterized.
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RNA Structure Characterization from Chemical Mapping Experiments
S. Aviran, J. B. Lucks, L. Pachter. Forty-Ninth Allerton Conference, UIUC Illinois, 1743-1750.(2011). Conference PDF DOI: 10.1109/Allerton.2011.6120379 (FREE)
Links: Conference PDF, Arxiv(q-bio)About
An updated maximum likelihood model for SHAPE-Seq reactivities from Aviran et. al, PNAS, 2011. This much simpler derivation is easier to extend to different experimental scenarios (such as multiple reverse transcriptase primers) and introduces a new quantity, beta-reactivities, which are easier to interpret.
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Modeling and automation of sequencing-based characterization of RNA structure
S. Aviran, C. Trapnell, J. B. Lucks, S. A. Mortimer, S. Luo, G. P. Schroth, J. A. Doudna, A. P. Arkin, L. Pachter. PNAS. (2011). doi: 10.1073/pnas.1106541108
Links: Journal, PDF
See Commentary! K. M. Weeks. “RNA Structure Probing dash Seq.”, PNAS, 108, 10933, 2011.About
This work describes a new approach for analyzing next generation sequencing datasets from high throughput RNA structure chemical probing experiments. Importantly it was the first approach that allowed fully automated analysis, and presents a maximum-likelihood framework for estimating this structural information that has been extended in a number of important works.
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Multiplexed RNA structure characterization with selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq)
J. B. Lucks*, S. A. Mortimer, C. Trapnell, S. Luo, S. Aviran, G. P. Schroth, L. Pachter, J. A. Doudna*, A. P. Arkin*. PNAS. (2011). (* Co-corresponding author) doi: 10.1073/pnas.1106501108
Links: Journal, PDF
See Commentary! K. M. Weeks. “RNA Structure Probing dash Seq.”, PNAS, 108, 10933, 2011.About
This paper presents the first method to capture nucleotide-resolution structural information for hundreds of RNAs in a single experiment. This method, called SHAPE-Seq, works by merging chemical probing of RNA structures, with next-generation sequencing to read out modification patterns and infer structures. It opened the door to very high throughput RNA structural biology and it and similar techniques are used by laboratories around the world to address fundamental questions in RNA biology and RNA engineering.