Joohyun Kim

A mechanism for S-adenosyl methionine assisted formation of a riboswitch conformation: a small molecule with a strong arm

The S-adenosylmethionine-1 (SAM-I) riboswitch mediates expression of proteins involved in sulfur metabolism via formation of alternative conformations in response to binding by SAM. Models for kinetic trapping of the RNA in the bound conformation require annealing of nonadjacent mRNA segments during a transcriptional pause. The entropic cost required to bring nonadjacent segments together should slow the folding process. To address this paradox, we performed molecular dynamics simulations on the SAM-I riboswitch aptamer domain with and without SAM, starting with the X-ray coordinates of the SAM-bound RNA. Individual trajectories are 200 ns, among the longest reported for an RNA of this size. We applied principle component analysis (PCA) to explore the global dynamics differences between these two trajectories. We observed a conformational switch between a stacked and nonstacked state of a nonadjacent dinucleotide in the presence of SAM. In the absence of SAM the coordination between a bound magnesium ion and the phosphate of A9, one of the nucleotides involved in the dinucleotide stack, is destabilized. An electrostatic potential map reveals a ‘hot spot’ at the Mg binding site in the presence of SAM. These results suggest that SAM binding helps to position J1/2 in a manner that is favorable for P1 helix formation.

The Herpes Simplex Virus Type 1 UL20 Protein and the Amino Terminus of Glycoprotein K (gK) Physically Interact with gB

ABSTRACT Herpes simplex virus type 1 (HSV-1) glycoprotein K (gK) and the UL20 protein (UL20p) are strictly required for virus-induced cell fusion, and mutations within either the gK or UL20 gene cause extensive cell fusion (syncytium formation). We have shown that gK forms a functional protein complex with UL20p, which is required for all gK and UL20p-associated functions in the HSV-1 life cycle. Recently, we showed that the amino-terminal 82 amino acids (aa) of gK (gKa) were required for the expression of the syncytial phenotype of the mutant virus gBΔ28 lacking the carboxyl-terminal 28 amino acids of gB (V. N. Chouljenko, A. V. Iyer, S. Chowdhury, D. V. Chouljenko, and K. G. Kousoulas, J. Virol. 83:12301-12313, 2009). This work suggested that the amino terminus of gK may directly or indirectly interact with gB and/or other viral glycoproteins. Two-way coimmunoprecipitation experiments revealed that UL20p interacted with gB in infected cells. Furthermore, the gKa peptide was coimmunoprecipitated with gB but not gD. Three recombinant baculoviruses were constructed, expressing the amino-terminal 82 aa of gKa together with either the extracellular portion of gB (30 to 748 aa), gD (1 to 340 aa), or gH (1 to 792 aa), respectively. Coimmunoprecipitation experiments revealed that gKa physically interacted with the extracellular portions of gB and gH but not gD. Three additional recombinant baculoviruses expressing gKa and truncated gBs encompassing aa 30 to 154, 30 to 364, and 30 to 500 were constructed. Coimmunoprecipitation experiments showed that gKa physically interacted with all three truncated gBs. Computer-assisted prediction of possible gKa binding sites on gB suggested that gKa may interact predominantly with gB domain I (E. E. Heldwein, H. Lou, F. C. Bender, G. H. Cohen, R. J. Eisenberg, and S. C. Harrison, Science 313:217-220, 2006). These results imply that the gK/UL20p protein complex modulates the fusogenic properties of gB and gH via direct physical interactions.

Efficient Runtime Environment for Coupled Multi-physics Simulations: Dynamic Resource Allocation and Load-Balancing

Coupled Multi-Physics simulations, such as hybrid CFD-MD simulations, represent an increasingly important class of scientific applications. Often the physical problems of interest demand the use of high-end computers, such as TeraGrid resources, which are often accessible only via batch-queues. Batch-queue systems are not developed to natively support the coordinated scheduling of jobs – which in turn is required to support the concurrent execution required by coupled multi-physics simulations. In this paper we develop and demonstrate a novel approach to overcome the lack of native support for coordinated job submission requirement associated with coupled runs. We establish the performance advantages arising from our solution, which is a generalization of the Pilot-Job concept – which in of itself is not new, but is being applied to coupled simulations for the first time. Our solution not only overcomes the initial co-scheduling problem, but also provides a dynamic resource allocation mechanism. Support for such dynamic resources is critical for a load balancing mechanism, which we develop and demonstrate to be effective at reducing the total time-to-solution of the problem. We establish that the performance advantage of using Big Jobs is invariant with the size of the machine as well as the size of the physical model under investigation. The Pilot-Job abstraction is developed using SAGA, which provides an infrastructure agnostic implementation, and which can seamlessly execute and utilize distributed resources.

Distributed Application Runtime Environment (DARE): A Standards-based Middleware Framework for Science-Gateways

Gateways have been able to provide efficient and simplified access to distributed and high-performance computing resources. Gateways have been shown to support many common and advanced requirements, as well as proving successful as a shared access mode to production cyberinfrastructure such as the TG/XSEDE. There are two primary challenges in the design of effective and broadly-usable gateways: the first revolves around the creation of interfaces that catpure existing and future usage modes so as to support desired scientific investigation. The second challenge and the focus of this paper, is concerned about the requirement to integrate the user-interfaces with computational resources and specialized cyberinfrastructure in an interoperable, extensible and scalable fashion. Currently, there does not exist a commonly usable middleware to that enables seamless integration of different gateways to a range of distributed and high-performance infrastructures. The development of multiple similar gateways that can work over a range of production cyberinfrastructures, usage modes and application requirements is not scalable without a effective and extensible middleware. Some of the challenges that make using production cyberinfrastructure as a collective resource difficult are also responsible for the absence of middleware that enables multiple gateways to utilize the collective capabilities. We introduce the SAGA-based, Distributed Application Runtime Environment (DARE) framework, using which gateways that seamlessly and effectively utilize scalable distributed infrastructure can be built. We discuss the architecture of DARE-based gateways, and show using several different prototypes—DARE-HTHP, DARE-NGS, how gateways can be constructed by utilizing the DARE middleware framework.

Adaptive distributed replica–exchange simulations

Owing to the loose coupling between replicas, the replica–exchange (RE) class of algorithms should be able to benefit greatly from using as many resources as available. However, the ability to effectively use multiple distributed resources to reduce the time to completion remains a challenge at many levels. Additionally, an implementation of a pleasingly distributed algorithm such as replica–exchange, which is independent of infrastructural details, does not exist. This paper proposes an extensible and scalable framework based on Simple API for Grid Applications that provides a general-purpose, opportunistic mechanism to effectively use multiple resources in an infrastructure-independent way. By analysing the requirements of the RE algorithm and the challenges of implementing it on real production systems, we propose a new abstraction (BigJob), which forms the basis of the adaptive redistribution and effective scheduling of replicas.

Comparative genomic analysis of two Burkholderia glumae strains from different geographic origins reveals a high degree of plasticity in genome structure associated with genomic islands

Burkholderia glumae is the major causal agent of bacterial panicle blight of rice, a growing disease problem in global rice production. To better understand its genome-scale characteristics, the genome of the highly virulent B. glumae strain 336gr-1 isolated from Louisiana, USA was sequenced using the Illumina Genome Analyser II system. De novo assembled 336gr-1 contigs were aligned and compared with the previously sequenced genome of B. glumae strain BGR1, which was isolated from an infected rice plant in South Korea. Comparative analysis of the whole genomes of B. glumae 336gr-1 and B. glumae BGR1 revealed numerous unique genomic regions present only in one of the two strains. These unique regions contained accessory genes including mobile elements and phage-related genes, and some of the unique regions in B. glumae BGR1 corresponded to predicted genomic islands. In contrast, little variation was observed in known and potential virulence genes between the two genomes. The considerable amount of plasticity largely based on accessory genes and genome islands observed from the comparison of the genomes of these two strains of B. glumae may explain the versatility of this bacterial species in various environmental conditions and geographic locations.

The Impact of a Ligand Binding on Strand Migration in the SAM-I Riboswitch

Riboswitches sense cellular concentrations of small molecules and use this information to adjust synthesis rates of related metabolites. Riboswitches include an aptamer domain to detect the ligand and an expression platform to control gene expression. Previous structural studies of riboswitches largely focused on aptamers, truncating the expression domain to suppress conformational switching. To link ligand/aptamer binding to conformational switching, we constructed models of an S-adenosyl methionine (SAM)-I riboswitch RNA segment incorporating elements of the expression platform, allowing formation of an antiterminator (AT) helix. Using Anton, a computer specially developed for long timescale Molecular Dynamics (MD), we simulated an extended (three microseconds) MD trajectory with SAM bound to a modeled riboswitch RNA segment. Remarkably, we observed a strand migration, converting three base pairs from an antiterminator (AT) helix, characteristic of the transcription ON state, to a P1 helix, characteristic of the OFF state. This conformational switching towards the OFF state is observed only in the presence of SAM. Among seven extended trajectories with three starting structures, the presence of SAM enhances the trend towards the OFF state for two out of three starting structures tested. Our simulation provides a visual demonstration of how a small molecule (<500 MW) binding to a limited surface can trigger a large scale conformational rearrangement in a 40 kDa RNA by perturbing the Free Energy Landscape. Such a mechanism can explain minimal requirements for SAM binding and transcription termination for SAM-I riboswitches previously reported experimentally.

All-Atom Molecular Dynamics Simulations of β-Hairpins Stabilized by a Tight Turn: Pronounced Heterogeneous Folding Pathways

Formation of beta-hairpins for a series of peptides having the same general sequence, RYVEV-XG-KKILQ-NH(2), where the i + 1th residue, X, at the beta-turn is varied (Aib or B in BG12, (D)Pro or (D)P in (D)PG12, (L)Pro or P in PG12, and Asn or N in NG12) was studied by means of all-atom Molecular Dynamics (MD) simulations. Trajectories of the tryptophan zipper beta-hairpin peptide, TZ2 (SWTWE-NG-KWTWK), were also run under similar conditions to provide a comparison with results for a like-sized peptide with a different characteristic folding mechanism. Four-residue peptides with a sequence, X-BG-K-NH(2) (where X is a V or (D)V) were further simulated, particularly focusing on the mirror turn propensity in these hairpins. Microscopic bases for several previous experimental observations are clearly indicated in our MD trajectories. Our results suggest that the two Gellman hairpins, BG12 and (D)PG12, have stabilities with a pronounced contribution from the tight turn and a moderate contribution from the cross-strand interactions, resulting in a complicated interplay between turn and strand, while TZ2 appears to undergo simpler (un)folding, dominated by cross-strand interactions. Such an interplay between the turn and the strand observed with BG12 and (D)PG12 may underlie their heterogeneous folding dynamics and the absence of a sigmoidal transition in their thermal unfolding profiles determined with IR spectra, as has been shown in previous experimental studies.

Characterizing deep sequencing analytics using BFAST: towards a scalable distributed architecture for next-generation sequencing data

Next Generation DNA Sequencing platforms produce significantly larger amounts of data compared to early Sanger technology sequencers. In addition to the challenges of data-management that arise from unprecedented volumes of data, there exists the important requirement of effectively analyzing the data. In this paper, we use BFAST -- genome-wide mapping application, as a representative example of the typical analysis that is required on data from NGS machines. We investigate two model genomes -- human genome and a microbe (Burkerholderia Glumae), that represent an eukaryotic and a prokaryotic system. The computational complexity of genome-wide mapping using BFAST, amongst other factors depends upon the size of a reference genome, the data size of short reads. We analyze the performance characteristics of BFAST and understand its dependency on different input parameters. Characterizing the performance suggests that genome-wide mapping benefits from both scaling-up (increased fine-grained parallelism) and scaling-out (task-level parallelism -- local and distributed). For certain problem instances, scaling-out can be a more efficient approach than scaling-up. We then design, develop and demonstrate a runtime-environment that supports both the scale-up and scale-out of BFAST on production grid and cloud environments.

Exploring the RNA folding energy landscape using scalable distributed cyberinfrastructure

The increasing significance of RNAs in transcriptional or post-transcriptional gene regulation processes has generated considerable interest towards the prediction of RNA folding and its sensitivity to environmental factors. We use Boltzmann-weighted sampling to generate RNA secondary structures, which are used to characterize the energy landscape, via the distributions of energies and base-pair distances. Depending upon the length of an RNA, the number of sequences investigated, and the sample size of generated structures --- generating and analyzing sufficient samples can be computationally challenging. We introduce and develop a lightweight and extensible runtime environment that is effective across a range of RNA sizes and other parameters, as well as over a range of infrastructure -- from traditional HPC grids to clouds, without requiring any changes at the application or user level. The Adaptive Distributed Application Management System (ADAMS) is built upon an extensbile and interoperable pilot-job and supports the concurrent execution of a broad range of task sizes across a range of infrastructure. We use ADAMS to investigate the folding energy landscape for two RNA systems of different sizes: a set of S-adenosyl methionine (SAM) binding RNA sequences known as SAM-I riboswitches and the S gene of the Bovine Corona Virus (BCoV) RNA genome that comprises 4092 nucleotides. Results of the energy and base-pair distance distributions suggest different energy landscapes, implying different folding dynamics. With obtained results, we demonstrated the possibility of utilizing this protocol to explore microscopic origins for reported sequence-dependent variation of binding affinity and gene expression in the two RNA systems.