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Bruce A. Shapiro, Ph.D.

Bruce A. Shapiro, Ph.D.

  • Center for Cancer Research
  • National Cancer Institute
NCI Scientist Emeritus
RNA Biology Laboratory

RESEARCH SUMMARY

Dr. Shapiro directed research on computational and experimental RNA structure prediction and analysis and pioneered research in the emerging field of RNA nanobiology. His work led to several novel RNA folding and analysis algorithms, experimental techniques and discoveries in RNA biology. His interests included RNA nanobiology, nucleic acid structure prediction and analysis, the relationships between RNA structure and function. He fostered a synergy between computational and experimental techniques, where computationally designed novel RNA based nanostructures were shown to be able to self-assemble as predicted and be delivered to cell cultures and mouse models to control gene expression and thus show potential for use in RNA-based therapeutics. For additional information, please visit our web site at https://rnastructure.cancer.gov

Areas of Expertise

RNA Structure
RNA Folding
RNA Nanobiology – Computational And Experimental
Computational RNA Structure Prediction And Analysis
Molecular Dynamics
RNA 3D Modeling

Publications

Selected Publications

Truncated tetrahedral RNA nanostructures exhibit enhanced features for delivery of RNAi substrates

Zakrevsky P, Kasprzak WK, Heinz WF, Wu W, Khant H, Bindewald E, Dorjsuren N, Fields EA, de Val N5, Jaeger L, Shapiro BA
Nanoscale. 12(4): 2555-2568, 2020. [ Journal Article ]

Multifunctional RNA nanoparticles

Afonin KA, Viard M, Koyfman AY, Martins AN, Kasprzak WK, Panigaj M, Desai R, Santhanam A, Grabow WW, Jaeger L, Heldman E, Reiser J, Chiu W, Freed EO, and Shapiro BA.
Nano Lett. 14: 5662-71, 2014. [ Journal Article ]

Characterization of Cationic Bolaamphiphile Vesicles for siRNA Delivery into Tumors and Brain.

Kim T, Viard M, Afonin KA, Gupta K, Popov M, Salotti J, Johnson PF, Linder C, Heldman E, Shapiro BA
Molecular Therapy - Nucleic Acids. 20: 359-372, 2020. [ Journal Article ]

Ribosomal frameshifting in the CCR5 mRNA is regulated by miRNAs and the NMD pathway

Belew AT, Meskauskas A, Musalgaonkar S, Advani VM, Sulima SO, Kasprzak WK, Shapiro BA, and Dinman JD.
Nature. 512: 265-9, 2014. [ Journal Article ]

MPGAfold in dengue secondary structure prediction

Kasprzak WK, and Shapiro BA.
Methods Mol Biol. 1138: 199-224, 2014. [ Journal Article ]

Covers

RNA Nanostructures - Methods and Protocols cover, 2017

RNA Nanostructures – Methods and Protocols

Published Date

RNA nanotechnology is a young field with many potential applications. The goal is to utilize designed RNA strands, such that the obtained constructs have specific properties in terms of shape and functionality. RNA has potential functionalities that are comparable to that of proteins, but possesses (compared to proteins) simpler design principles akin to DNA. The promise is that designed RNA complexes may make possible novel types of molecular assemblies with applications in medicine (as therapeutics or diagnostics), material science, imaging, structural biology, and basic research.

Using this approach, scientists have shown that they can design RNAs that self-assemble into predefined shapes (such as rings, cubes, tetrahedrons, or lattices). Furthermore, designed RNAs can be programmed to impart different functionalities such as gene knockdown via RNA interference, temperature-specific behavior or RNA-based logic or multi-functional assemblies.

These successes, however, are typically only possible due to the use of specialized computational and experimental approaches. Repeating achievements based on regular research papers are frequently challenging if the methods are described only briefly. It is therefore, particularly useful that detailed protocols provided by leading experts in the field are compiled as a unit, thus making the current state of the art accessible to scientists entering the field. Presented in this book are 23 chapters representing a spectrum of computational and experimental protocols pertaining to the creation, characterization, and utilization of RNA nanostructures.

Citation

Bindewald E, Shapiro BA (Editors). RNA Nanostructures – Methods and Protocols, Methods in Molecular Biology, vol. 1632, Humana Press, New York, 2017.

Methods cover, July 1, 2016

Advances in RNA Structure Determination

Published Date

The recent years have witnessed a revolution in the field of RNA structure and function. Until recently the main contribution of RNA in cellular and disease functions was considered to be a role defined by the central dogma, namely DNA codes for mRNAs, which in turn encode for proteins, a notion facilitated by non-coding ribosomal RNA and tRNA. It was also assumed at the time that less than 2% of DNA in the human genome was used to encode genes, the remainder considered “junk”. Subsequent research has unequivocally determined that RNA mediates a plethora of functions vital to cellular activity as well as clinically-significant diseases. In turn, it was discovered that the amount of DNA that encodes functional RNAs also increased significantly. This special journal issue, containing 19 articles, describes several of the computational and experimental methodologies that are used to determine RNA structure and function that enables the application of this knowledge for therapeutic purposes.

Citation

Shapiro BA, Le Grice SF (Editors). Advances in RNA Structure Determination. Methods. 2016 Jul 1;103:1-3. doi: 10.1016/j.ymeth.2016.06.006. PubMed PMID: 27342006.

Journal of Biomolecular Structure and Dynamics cover, February, 2007

The impact of dyskeratosis congenita mutations on the structure and dynamics of the human telomerase RNA pseudoknot domain

Published Date

The pseudoknot domain is a functionally crucial part of telomerase RNA and influences the activity and stability of the ribonucleoprotein complex. Autosomal dominant dyskeratosis congenita (DKC) is an inherited disease that is linked to mutations in telomerase RNA and impairs telomerase function. In this paper, we present a computational prediction of the influence of two base DKC mutations on the structure, dynamics, and stability of the pseudoknot domain. We use molecular dynamics simulations, MM-GBSA free energy calculations, static analysis, and melting simulations analysis. Our results show that the DKC mutations stabilize the hairpin form and destabilize the pseudoknot form of telomerase RNA. Moreover, the P3 region of the predicted DKC-mutated pseudoknot structure is unstable and fails to form as a defined helical stem. We directly compare our predictions with experimental observations by calculating the enthalpy of folding and melting profiles for each structure. The enthalpy values are in very good agreement with values determined by thermal denaturation experiments. The melting simulations and simulations at elevated temperatures show the existence of an intermediate structure, which involves the formation of two UU base pairs observed in the hairpin form of the pseudoknot domain.

Citation

Yingling YG, Shapiro BA. The impact of dyskeratosis congenita mutations on the structure and dynamics of the human telomerase RNA pseudoknot domain. J Biomol Struct Dyn. 2007 Feb;24(4):303-20. PubMed PMID: 17206847.

Pattern Discovery in Biomolecular Data cover, November 1999

Pattern Discovery in Biomolecular Data – Tools, Techniques, and Applications

Published Date

Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. This book provides a summary of principal techniques. Each chapter describes techniques that are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components.

Citation

Wang JTL, Shapiro BA, Shasha D (Editors). Pattern Discovery in Biomolecular Data – Tools, Techniques, and Applications. Oxford University Press, New York, 1999.