STRUCTURE AND FUNCTION OF THE GROUP III CHAPERONINS, A UNIQUE CLADE OF PROTEIN FOLDING NANOMACHINES

The survival and descent of cells is universally dependent on maintaining their proteins in a properly folded condition. It is widely accepted that the information for the folding of the nascent polypeptide chain into a native protein is encrypted in the amino acid sequence, and the Nobel Laureate Christian Anfinsen was the first to demonstrate that a protein could spontaneously refold after complete unfolding. However, it became clear that the observed folding rates for many proteins were much slower than rates estimated in vivo. This led to the recognition of required protein-protein interactions that promote proper folding. A unique group of proteins, the molecular chaperones, are responsible for maintaining protein homeostasis during normal growth as well as stress conditions. Chaperonins (CPNs) are ubiquitous and essential chaperones. They form ATP-dependent, hollow complexes that encapsulate polypeptides in two back-to-back stacked multisubunit rings, facilitating protein folding through highly cooperative allosteric articulation. CPNs are usually classified into Group I and Group II. Here, I report the characterization of a novel CPN belonging to a third Group, recently discovered in bacteria. Group III CPNs have close phylogenetic association to the Group II CPNs found in Archaea and Eukarya, and may be a relic of the Last Common Ancestor of the CPN family. The gene encoding the Group III CPN from Carboxydothermus hydrogenoformans and Candidatus Desulforudis audaxviator was cloned in E. coli and overexpressed in order to both characterize the protein and to demonstrate its ability to function as an ATPase chaperone. The opening and closing cycle of the Chy chaperonin was examined via site-directed mutations affecting the ATP binding site at R155. To relate the mutational analysis to the structure of the CPN, the crystal structure of both the AMP-PNP (an ATP analogue) and ADP bound forms were obtained in collaboration with Sun-Shin Cha in Seoul, South Korea. The ADP and ATP binding site substitutions resulted in frozen forms of the structures in open and closed conformations. From this, mutants were designed to validate hypotheses regarding key ATP interacting sites as well as important stabilizing interactions, and to observe the physical properties of the resulting complexes by calorimetry.

STRUCTURAL AND FUNCTIONAL STUDIES OF CYCLIC K48-LINKED DIUBIQUITIN

K48-linked di-ubiquitin exists in a dynamic equilibrium between open and closed states. The structure of K48-Ub2 in the closed conformation features a hydrophobic interface formed between the two Ub domains. The same hydrophobic residues at the interface are involved in binding to ubiquitin-associated (UBA) domains. Cyclization of K48-Ub2 should limit the range of conformations available for such interactions. Interestingly, cyclic K48-linked Ub2 (cycUb2) has been found in vivo and can be isolated in vitro to study its structure and dynamics. In this study, a crystal structure of cycUb2 was obtained, and the dynamics of cycUb2 were characterized by solution NMR. The crystal structure of cycUb2, which is in agreement with solution NMR data, is closed with the hydrophobic patches of each Ub domain buried at the interface. Despite its structural constraints, cycUb2 was still able to interact with UBA domains, albeit with lower affinity.

Building a Successful (and Flexible) Partnership with UMD's Gymkana Troupe

Presented at the 2016 Library Research and Innovative Practice Forum, this poster provides an overview of a successful partnership between the University of Maryland Archives and UMD's Gymkana Troupe to publicize Gymkana's 70th anniversary and to digitize the troupe's holdings in the Archives. Gymkana is an exhibition gymnastics troupe founded on campus in 1946 which runs a variety of educational and healthy-living outreach programs. Various stages of the project are highlighted, including an exhibit in McKeldin Library, a LaunchUMD fundraising campaign, and the troupe's participation in metadata creation for digital objects. By maintaining an open and flexible dialogue throughout the project planning and execution, both the library and the troupe members ultimately benefited from this collaboration.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.