Exploring protein-RNA interactions with site-directed mutagenesis and phage display

The importance of RNA as a mediator of genetic information is widely appreciated. RNA molecules also participate in the regulation of various post-transcriptional activities, such as mRNA splicing, editing, RNA stability and transport. Their regulatory roles for these activities are highly dependent on finely tuned associations with cognate proteins. The RNA recognition motif (RRM) is an ancient RNA binding module that participates in hundreds of essential activities where specific RNA recognition is required. We have applied phage display and site-directed mutagenesis to dissect principles of RRM-controlled RNA recognition. The model systems we are investigating are U1A and CUG-BP1. In this dissertation, the molecular basis of the binding affinity of U1A-RNA beyond individual contacts was investigated. We have identified and evaluated the contributions of the local cooperativity formed by three neighboring residues (Asn15, Asn16 and Glu19) to the stability of the U1A-RNA complex. The localized cooperative network was mapped by double-mutant cycles and explored using phage display. We also showed that a cluster of these residues forms a “hot spot” on the surface of U1A; a single substitution at position 19 with Gln or His can alter the binding properties of U1A to recognize a non-cognate G4U RNA. Finally, we applied a deletion analysis of CUG-BP1 to define the contributions of individual RRMs and RRM combinations to the stability of the complex formed between CUG-BP1 and the GRE sequence. The preliminary results showed RRM3 of CUG-BP1 is a key domain for RNA binding. It possibly binds to the GRE sequence cooperatively with RRM2 of CUG-BP1. RRM1 of CUG-BP1 is not required for GRE recognition, but may be important for maintaining the stability of the full-length CUG-BP1.

A parallel numerical solver using hierarchically tiled arrays

Solving linear systems is an important problem for scientific computing. Exploiting parallelism is essential for solving complex systems, and this traditionally involves writing parallel algorithms on top of a library such as MPI. The SPIKE family of algorithms is one well-known example of a parallel solver for linear systems. The Hierarchically Tiled Array data type extends traditional data-parallel array operations with explicit tiling and allows programmers to directly manipulate tiles. The tiles of the HTA data type map naturally to the block nature of many numeric computations, including the SPIKE family of algorithms. The higher level of abstraction of the HTA enables the same program to be portable across different platforms. Current implementations target both shared-memory and distributed-memory models. In this thesis we present a proof-of-concept for portable linear solvers. We implement two algorithms from the SPIKE family using the HTA library. We show that our implementations of SPIKE exploit the abstractions provided by the HTA to produce a compact, clean code that can run on both shared-memory and distributed-memory models without modification. We discuss how we map the algorithms to HTA programs as well as examine their performance. We compare the performance of our HTA codes to comparable codes written in MPI as well as current state-of-the-art linear algebra routines.