Our Science – Shapiro Website

Bruce A. Shapiro, Ph.D.

CCR Nanobiology Program Head, Computational RNA Structure Group Senior Investigator Building 469, Room 150A NCI-Frederick Frederick, MD 21702-1201 Phone:   301-846-5536 Fax:   301-846-5598 E-Mail:   bshapiro@ncifcrf.gov Link: Other Homepage

Biography

Dr. Shapiro has been with the NIH since 1973 and the laboratory since 1983. He received his Ph.D. in computer science from the University of Maryland in 1978, with undergraduate work in mathematics and physics. During his association with the NIH, Dr. Shapiro has done extensive work in image processing, nucleic acid structure prediction and analysis, and nanobiology, leading to several novel algorithms, computer systems and discoveries in RNA biology. His latest interests include RNA nanobiology, RNA structure-function and the use of parallel high performance computer architectures to solve problems related to RNA computational biology and molecular modeling.

Research

A complete understanding of the function of RNA molecules requires knowledge of their higher order structures (2D and 3D) as well as the characteristics of their primary sequence. RNA structure is important for many functions, including regulation of transcription and translation, catalysis, transport of proteins across membranes and the regulation of RNA viruses. The understandings of these functions are important for basic biology as well as for the development of drugs that can intervene in cases where pathological functionality of these molecules occurs.

Our group does research and development of methodologies for improving RNA folding and analysis techniques to help further our understanding of the functional properties of these molecules. In addition, we are focusing on the emerging field of RNA nanobiology. RNA represents a relatively new molecular material for the development of biologically oriented nano devices. It is an interesting material because of its natural functionalities, its ability to fold into complex structures and self assemble. We have developed computational methodologies that permit the design of RNA based nanoparticles that potentially have a variety of uses. Thus, our research on RNA covers four highly related and integrated areas of computational research; 1) Research in algorithms for RNA secondary structure prediction and analysis; 2) RNA biology and its relationship to sequence and secondary structure folding characteristics; 3) Research in algorithms for RNA 3D structure prediction and analysis and their application to RNA biology; 4) Research in algorithms for the design and analysis of RNA nanoparticles. What is learned in one area is applied to the other areas, enhancing our understanding of RNA structure, function, and RNA nanobiology and self-assembly.


Parallel/Heterogeneous Computational Biology and RNA Structure

Revolutionary changes in computational paradigms are required to maintain the necessary computational power to solve problems in molecular biology. Methodologies based on sequential computer architectures cannot be expected to continually keep pace with the needed computational speeds. In order to accommodate the high speeds that are necessary, heterogeneous and highly parallel computational techniques are required. Our group was one of the pioneers in the area of computational biology and the use of parallel high performance computer architectures for this endeavor.


Parallel/Heterogeneous Computation and RNA Structure
We were the first to develop an RNA folding technique that uses concepts from genetic algorithms. Our algorithm, MPGAfold, was originally developed to run on a massively parallel SIMD supercomputer, a MasPar MP-2 with 16384 processors. This algorithm was modified and now runs on parallel MIMD high performance computers which include multicore Linux clusters. Exceptional scaling characteristics are obtained with the ability to run the algorithm with hundreds of thousands of population elements. RNA pseudoknot prediction is part of the GA, resulting in its ability to predict tertiary interactions. Other features include the ability to incorporate different energy rules, and the forced inhibition and embedding of desired helical stems. In addition, STRUCTURELAB, our heterogeneous bioinformatical RNA analysis workbench can be used in conjunction with MPGAfold and RNA2D3D to produce predicted 3D atomic coordinates of RNA structures along with the visualization of these structures. Also, we developed a novel interactive visualization methodology that is part of STRUCTURELAB. This technique enables the comparison and analysis of multiple sequence RNA folds from a phylogenetic point of view, thus allowing improvement of predicted structural results across a family of sequences.

We developed one of the best algorithms, KNetFold, for RNA structure prediction from sequences alignments. The algorithm uses a unique hierarchical classification network based on mutual information, thermodynamics and Watson-Crick base-pairedness to predict structures. In addition, we have developed a web based application, CorreLogo, that uses mutual information derived from RNA sequence alignments to determine covariations amongst base-paired positions. The algorithm includes a unique error measure and depicts results in 3D.

These systems have been adapted to other environments inside and outside our laboratory and the NIH and are accessible through our web site at http://www.CCRNP.ncifcrf.gov/~bshapiro.


Computational Studies of RNA Folding Pathways
RNA folding pathways are proving to be quite important in the determination of RNA function. Studies indicate that RNA may enter intermediate conformational states that are key to its functionality. These states may have a significant impact on gene expression. It is known that the biologically functional states of RNA molecules may not correspond to their minimum energy state, that kinetic barriers may exist that trap the molecule in a local minimum, that folding often occurs during transcription, and cases exist in which a molecule will transition between one or more functional conformations before reaching its native state. Thus, methods for simulating the folding pathway of an RNA molecule and locating significant intermediate states are important for the prediction of RNA structure and its associated function. Several biological RNA folding pathways have been successfully studied using MPGAfold and STRUCTURELAB. Examples include potato spindle tuber viroid, the host-killing mechanism of Escherichia coli plasmid R1, the hepatitis delta virus and HIV. Computational results are consistent with those derived from biological experiments and novel structural interactions and important functional intermediate and native states have been predicted. These have lead to further successful confirmatory experiments.

Computational Studies of Three-Dimensional RNA Structures
Some structural elements of RNA molecules have been studied using molecular mechanics and molecular dynamics simulations. The structures examined included an RNA tetraloop where temperature-dependent denaturation of the tetraloop and the subsequent refolding to the original crystal structure were performed. A three-way junction from the core central domain of the 30S ribosomal subunit from Thermus thermophilus was explored. It has been experimentally determined that the intermolecular interactions between the three-way junction and the S15 ribosomal protein initiate the process of the assembly of the 30S ribosomal subunit. By using molecular dynamics simulations we obtained insights into the conformational transitions of the junction associated with the binding of S15.

We have also examined the pseudoknot domain of telomerase. Molecular modeling and molecular dynamics of the pseudoknot domain including its hairpin loop were performed. Results indicated how the hairpin loop dynamics affected the opening and closing of the non-canonical U-U base pairs found in the stem. The opening suggested nucleation points for the formation of the pseudoknot. We have also examined the effect of dyskeratosis congenita (DKC)mutations in the loop and how they reduced the propensity for the opening of the stem by forming a relatively stable hydrogen bond network in the hairpin loop. We modeled the pseudoknot itself using our RNA2D3D software combined with phylogenetic analysis. We studied the dynamical impact of the DKC mutations on the pseudoknot with the result that the pseudoknot became unstable while the hairpin form became more stable.

We also recently discovered and elucidated the 3D structure of a new type of translational enhancer that is found in the 3' UTR of the Turnip Crinkle Virus (the first of its kind found). This was accomplished with the combined use of MPGAfold and our 3D molecular modeling software RNA2D3D.

In addition, we have employed methods based on elastic network interpolation to reduce the computational costs related to RNA 3D dynamics. Three-dimensional dynamics trajectories can be determined using a reduced atom representation and given conformational states. Compute time can be reduced from weeks to hours using this approach.

RNA Nanobiology
As previously indicated, RNA nanobiology represents a new modality for the development of nanodevices which have the potential for use in a number of areas including therapeutics. We developed several computational tools that provide a means to determine a set of nucleotide sequences that can assemble into a desired nano complex. One of these tools is a newly developed relational database called RNAJunction. The database contains structural and sequence information for all known RNA helical junctions and kissing loop interactions. These motifs can be searched for in a variety of ways, providing a source for RNA nano building blocks. Another computational tool, NanoTiler, permits a user to interactively construct specified RNA-based nanoscale shapes. NanoTiler provides a 3D graphical view of the objects being designed and provides the means to work interactively on the design process even though the precise RNA sequences may not yet be specified, and an all-atom model is not available. It is easy to change the scale on which one works. NanoTiler can use the 3D motifs found in the RNAJunction database with those derived from specified RNA secondary structure patterns to build a defined RNA nano shape. Or, a combinatorial search can be applied to enumerate structures that would not normally be considered.

This page was last updated on 6/27/2008.