Albert Kim

An endogenous dAMP ligand inBacillus subtilisclass Ib RNR promotes assembly of a noncanonical dimer for regulation by dATP.

Significance Negative feedback regulation of ribonucleotide reductase (RNR) activity by dATP is important for maintaining balanced intracellular 2ʹ-deoxynucleoside triphosphate (dNTP) pools essential for the high fidelity of DNA replication and repair. To date, this type of allostery has been nearly universally associated with dATP binding to the N-terminal ATP-cone domain of the class Ia RNR large subunit (canonical α2), resulting in an altered quaternary structure that is unable to productively bind the second subunit (β2). Here, we report our studies on activity inhibition by dATP of the Bacillus subtilis class Ib RNR, which lacks a traditional ATP-cone domain. This unprecedented allostery involves deoxyadenosine 5′-monophosphate (dAMP) binding to a newly identified site in a partial N-terminal cone domain, forming an unprecedented noncanonical α2. The high fidelity of DNA replication and repair is attributable, in part, to the allosteric regulation of ribonucleotide reductases (RNRs) that maintains proper deoxynucleotide pool sizes and ratios in vivo. In class Ia RNRs, ATP (stimulatory) and dATP (inhibitory) regulate activity by binding to the ATP-cone domain at the N terminus of the large α subunit and altering the enzyme’s quaternary structure. Class Ib RNRs, in contrast, have a partial cone domain and have generally been found to be insensitive to dATP inhibition. An exception is the Bacillus subtilis Ib RNR, which we recently reported to be inhibited by physiological concentrations of dATP. Here, we demonstrate that the α subunit of this RNR contains tightly bound deoxyadenosine 5′-monophosphate (dAMP) in its N-terminal domain and that dATP inhibition of CDP reduction is enhanced by its presence. X-ray crystallography reveals a previously unobserved (noncanonical) α2 dimer with its entire interface composed of the partial N-terminal cone domains, each binding a dAMP molecule. Using small-angle X-ray scattering (SAXS), we show that this noncanonical α2 dimer is the predominant form of the dAMP-bound α in solution and further show that addition of dATP leads to the formation of larger oligomers. Based on this information, we propose a model to describe the mechanism by which the noncanonical α2 inhibits the activity of the B. subtilis Ib RNR in a dATP- and dAMP-dependent manner.

Generalizing Over Uncertain Dynamics for Online Trajectory Generation

We present an algorithm which learns an online trajectory generator that can generalize over varying and uncertain dynamics. When the dynamics is certain, the algorithm generalizes across model parameters. When the dynamics is partially observable, the algorithm generalizes across different observations. To do this, we employ recent advances in supervised imitation learning to learn a trajectory generator from a set of example trajectories computed by a trajectory optimizer. In experiments in two simulated domains, it finds solutions that are nearly as good as, and sometimes better than, those obtained by calling the trajectory optimizer on line. The online execution time is dramatically decreased, and the off-line training time is reasonable.

Optimally Leveraging Density and Locality for Exploratory Browsing and Sampling

Exploratory data analysis often involves repeatedly browsing a small sample of records that satisfy certain predicates. We propose a fast query evaluation engine, called NeedleTail, aimed at letting analysts browse a subset of the query result on large datasets as quickly as possible, independent of the overall size of the result. NeedleTail introduces DensityMaps, a lightweight in-memory indexing structure, and a set of efficient and theoretically sound algorithms to quickly locate promising blocks, trading off locality and density. In settings where the samples are used to compute aggregates, we extend techniques from survey sampling to mitigate the bias in our samples. Our experimental results demonstrate that NeedleTail returns results 7× faster on average on HDDs while occupying up to 23× less memory than existing techniques.

S ilk M oth : an efficient method for finding related sets with maximum matching constraints

Determining if two sets are related - that is, if they have similar values or if one set contains the other -- is an important problem with many applications in data cleaning, data integration, and information retrieval. For example, set relatedness can be a useful tool to discover whether columns from two different databases are joinable; if enough of the values in the columns match, it may make sense to join them. A common metric is to measure the relatedness of two sets by treating the elements as vertices of a bipartite graph and calculating the score of the maximum matching pairing between elements. Compared to other metrics which require exact matchings between elements, this metric uses a similarity function to compare elements between the two sets, making it robust to small dissimilarities in elements and more useful for real-world, dirty data. Unfortunately, the metric suffers from expensive computational cost, taking O(n3) time, where n is the number of elements in the sets, for each set-to-set comparison. Thus for applications that try to search for all pairings of related sets in a brute-force manner, the runtime becomes unacceptably large. To address this challenge, we developed SilkMoth, a system capable of rapidly discovering related set pairs in collections of sets. Internally, SilkMoth creates a signature for each set, with the property that any other set which is related must match the signature. SilkMoth then uses these signatures to prune the search space, so only sets that match the signatures are left as candidates. Finally, SilkMoth applies the maximum matching metric on remaining candidates to verify which of these candidates are truly related sets. An important property of SilkMoth is that it is guaranteed to output exactly the same related set pairings as the brute-force method, unlike approximate techniques. Thus, a contribution of this paper is the characterization of the space of signatures which enable this property. We show that selecting the optimal signature in this space is NP-complete, and based on insights from the characterization of the space, we propose two novel filters which help to prune the candidates further before verification. In addition, we introduce a simple optimization to the calculation of the maximum matching metric itself based on the triangle inequality. Compared to related approaches, SilkMoth is much more general, handling a larger space of similarity functions and relatedness metrics, and is an order of magnitude more efficient on real datasets.