Our RNA research focuses on theoretical modeling of RNA in vitro selection (an experimental technique for discovering novel RNAs), predicting RNA tertiary structures, and designing novel RNAs for biological applications. The RNA group in the lab consists of researchers with backgrounds in computer science, mathematics, bioinformatics, chemistry and physics. Currently, our RNA projects are supported by the joint NSF/NIH initiative in mathematical biology, NSF, and the Human Frontier Science Program (HFSP).

Background: RNA graph theory and RNA-As-Graphs (RAG) web resource

The Schlick lab has developed a theoretical framework for RNA analysis and design based on graph theory and computational methods¹. Graph theory is a field in mathematics widely used for analyzing networks and enumerating structural possibilities, including chemical structures (e.g., hydrocarbons, drug compounds, and polymer networks), genetic and biochemical networks, transportation, and the Internet. It has already been used for analysis of RNA secondary structures in pioneering works by M. Waterman, B. A. Shapiro, and others. In addition to the familiar tree graphs, we have developed RNA dual graphs (see Figure 1) and demonstrated the potential of graph theory as a mathematical tool for enumerating and constructing all possible RNA tree and pseudoknot motifs, some of which may be either naturally occurring or generated in the laboratory. Significantly, the graph theory approach predicts many new RNA-like motifs; these can be used to guide the search for novel RNAs in genomes and experimental in vitro selection of functional RNAs². We have used RNA graphs to: (a) classify and catalog all RNA motifs^3,4; (b) estimate the size of RNA's structural/functional repertoire²; (c) search for novel functional RNA motifs in genomes⁵; (d) design structurally diverse RNA pools to enhance in vitro selection of functional RNAs (elaborated below); and (e) detect structural and functional similarity among existing RNAs⁶.

By using the RNA graph-based techniques, our lab has established an RNA-As-Graphs (RAG) web resource (http://monod.biomath.nyu.edu/rna/rna.php) which archives natural, “missing” RNA-like, and hypothetical motifs; RAG lists many missing motifs. To discover the missing motifs, we are collaborating with experimentalists to identify novel synthetic RNAs in laboratory, as described below.

Fig. 2. RNA web resource and server based on RNA graphs developed by the Schlick group. RAG catalogues existing, probable and hypothetical RNA motifs. RAGPOOLS enables analysis and design of RNA pools using RNA graphs.

Theoretical modeling of in vitro selection of RNAs

In vitro selection is a versatile experimental technology for discovering novel functional RNAs from random-sequence pools.⁷ In the past decade, many target-binding and catalytic RNAs have been identified for wide-ranging applications, including RNA-based biosensors, molecular therapeutics, synthetic biology, and nanotechnology. However, current experimental approaches have inherent limitations, including non-exhaustive coverage of sequence space and prevalence of simple topological motifs (e.g., stem-loop, stem-bulge-stem-loop). To improve RNA in vitro selection technology, synthesized RNA pools must possess sufficient sequence and structural complexity to enable discovery of complex RNA molecules (e.g., allosteric ribozymes).

Our aim is to develop systematic computational approaches to designed RNA pools with diverse sequences and complex structures. Recently, we have made progress in analysis and design of sequence pools. We showed quantitatively after simulating in vitro RNA pools that random pools are not structurally diverse, confirming results from various in vitro selection experiments.⁸ We have also developed an automated algorithm for designing pools with user-specified target structural distribution (e.g., specific topological structures or graphs representing RNA trees).⁹ The algorithm optimizes the nucleotide mixing matrix or nucleotide mixing ratios in synthesis ports. It is implemented in a web server we call RAGPOOLS (http://rubin2.biomath.nyu.edu ) to allow experimentalists and other researchers to analyze and design RNA pools.¹⁰

Essentially, our pool engineering incorporates the complexity of secondary structure space as described by our RNA graph theory.^1-4 Designed pools with a good coverage of the secondary structure space are more likely to yield functional RNAs than random sequence pools since an RNA’s functional properties strongly correlate with corresponding secondary rather than primary structure. To meet this challenge, our RNA pool design strategies incorporate elements of graph theory analysis of pool structures and biased pool synthesis via the mixing matrix, as well as application of applied mathematics techniques to analyze sequence/structure space to ensure structural diversity in designed pools. Figure 3 illustrates our theoretical modeling of RNA in vitro selection.

We are collaborating with the experimental laboratory of Luc Jaeger at UC Santa Barbara to test and improve pool design strategies with the goal of optimizing existing RNAs and discovering novel RNAs. To achieve this goal, we are continuing development of efficient and complementary computational techniques to engineering RNA pools; for example, we are exploring Monte Carlo strategies for efficient sampling of the sequence and structure space. We are also developing theoretical approaches to directed evolution, an experimental method for refining RNA function via cycles of mutations and function selection. Theoretical modeling of RNA in vitro selection and direct evolution requires: close collaborations with experimentalists; a deep understanding of the relation between RNA structure and function; and innovative computational/mathematical techniques to model aspects of the experimental technology (i.e., pool synthesis, RNA selection and evolution).

Computational RNA tertiary folding and design

Current tertiary RNA folding simulations are limited to small systems (e.g., RNA hairpins), and RNA design algorithms are confined to analysis of 2D structures. In contrast, in protein folding and design, emerging computational concepts and technologies such as fragment assembly and 3D-based design have proven successful.

To overcome current limitations in RNA structure prediction, we are combining computational technologies for proteins with knowledge of various RNA tertiary interaction motifs to develop effective approaches for predicting RNA tertiary structures.

We are also planning to develop a 3D-based framework for computational RNA engineering to extend the capability and precision of current 2D design algorithms. This framework for molecular engineering, which includes advanced dynamics analysis tools, allows analysis of the functional properties of designed RNAs through computation of tertiary structures, RNA-ligand binding and dynamics. Specifically, we are interested in engineering RNA systems such as allosteric ribozymes, riboswitches and fluorescent aptamers for emerging applications in bioengineering. We are collaborating with the experimental laboratory of Evgeny Nudler of NYU Medical School to design fluorescent riboswitches for biomedical applications.

Complementary approaches to RNA structure and function

We are also pursuing other approaches to RNA structure and function. For example, we have used in vitro selected RNAs to find RNA motifs in genomes¹¹ and employed statistical techniques to estimate the fraction of non-coding RNAs in mammalian transcriptomes (i.e., total expressed transcripts in cells).

In addition, with the laboratory of Ada Yonath (Weizmann Institute, Israel) we are combining computational and experimental approaches to identify novel target sites for antibiotics on bacterial ribosomal RNAs. The computational work involves assessing the energetics of antibiotic/rRNA binding and relating sequence conservation to phenotypes of rRNA mutations.

Current group members: Tamar Schlick (PI), Shereef Elmetwaly (computer programmer), Hin Hark Gan (research faculty), Namhee Kim (Computational Biology graduate student), Christian Laing (postdoctoral fellow), Giulio Quarta (NYU Medical School student), Christian Rose (graduate student), Jin Sup Shin (Chemistry/Computer Science undergraduate student), and Yurong Xin (postdoctoral fellow).

Previous group members (current affiliation): Sabera Asar, Danny Barash (Ben-Gurion University, Israel), Sean D’Souza, Daniela Fera (U Penn), Jana Gevertz (Princeton), Frank Lalezarzadeh (Cornell), Uri Laserson (MIT), Samuela Pasquali (ESPCI, Paris), Jimmy Potter (Brown), Evan Sherman (Columbia), Nahum Shiffeldrim (NYU), Joseph S. Sofaer, Padmavarti Sridhar (Columbia), Michael Tang (Princeton), and Julie Zorn (UCSF).

Support from HFSP and NSF Awards 0201160 and 0727001 are gratefully acknowledged.

References

1. Gan,H.H., Pasquali,S., & Schlick,T. Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design. Nucleic Acids Res. 31, 2926-2943 (2003).

2. Kim,N., Shiffeldrim,N., Gan,H.H., & Schlick,T. Candidates for novel RNA topologies. J. Mol. Biol. 341, 1129-1144 (2004).

3. Gan,H.H., Fera,D., Zorn,J., Shiffeldrim,N., Tang,M., Laserson,U., Kim,N., & Schlick,T. RAG: RNA-As-Graphs Database - Concepts, Analysis, and Features. Bioinformatics 20, 1285-1291 (2004).

4. Fera,D., Kim,N., Shiffeldrim,N., Zorn,J., Laserson,U., Gan,H.H., & Schlick,T. RAG: RNA-As-Graphs web resource. Bmc Bioinformatics 5, (2004).

5. Gan, H. H., Yuen, T., Sealfon, S. C., and Schlick, T. Novel mammalian noncoding RNAs identified by graph theory and modeling. 2005. Ref Type: Unpublished Work

6. Pasquali,S., Gan,H.H., & Schlick,T. Modular RNA architecture revealed by computational analysis of existing pseudoknots and ribosomal RNAs. Nucleic Acids Res. 33, 1384-1398 (2005).

7. Wilson,D.S. & Szostak,J.W. In vitro selection of functional nucleic acids. Annu. Rev. Biochem. 68, 611-647 (1999).

8. Gevertz,J., Gan,H.H., & Schlick,T. In vitro RNA random pools are not structurally diverse: A computational analysis. RNA 11, 853-863 (2005).

9. Kim,N., Gan,H.H., & Schlick,T. A computational proposal for designing structured RNA pools for in vitro selection of RNAs. RNA. 13, 478-492 (2007).

10. Kim,N., Shin,J.S., Elmetwaly,S., Gan,H.H., & Schlick,T. RAGPOOLS: RNA-As-Graph-Pools A Web Server for Assisting the Design of Structured RNA Pools forIn VitroSelection. Bioinformatics. (2007).

11. Laserson,U., Gan,H.H., & Schlick,T. Predicting candidate genomic sequences that correspond to synthetic functional RNA motifs. Nucleic Acids Res. 33, 6057-6069 (2005).

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