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The putative Major Facilitator Superfamily (MFS) transporter, SV2A, is the target for levetiracetam (LEV), which is a successful antiepileptic drug. Furthermore, SV2A knock out mice display a severe seizure phenotype and die after a few weeks. Despite this, the mode of action of LEV is not known at the molecular level. It would be extremely desirable to understand this more fully in order to aid the design of improved antiepileptic compounds. Since there is no structure for SV2A, homology modelling can provide insight into the ligandbinding site. However, it is not a trivial process to build such models, since SV2A has low sequence identity to those MFS transporters whose structures are known. A further level of complexity is added by the fact that it is not known which conformational state of the receptor LEV binds to, as multiple conformational states have been inferred by tomography and ligand binding assays or indeed, if binding is exclusive to a single state. Here, we explore models of both the inward and outward facing conformational states of SV2A (according to the alternating access mechanism for MFS transporters). We use a sequence conservation analysis to help guide the homology modelling process and generate the models, which we assess further with Molecular Dynamics (MD). By comparing the MD results in conjunction with docking and simulation of a LEVanalogue used in radioligand binding assays, we were able to suggest further residues that line the binding pocket. These were confirmed experimentally. In particular, mutation of D670 leads to a complete loss of binding. The results shed light on the way LEV analogues may interact with SV2A and may help with the ongoing design of improved antiepileptic compounds. © 2015 Lee et al. Source


Law Y.-N.,Bioinformatics Institute | Wang H.,Microsoft | Zaniolo C.,University of California at Los Angeles
ACM Transactions on Database Systems | Year: 2011

Most data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of N B-completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are N B-complete on unordered sets of tuples, and the problem is even more serious when the datamodel is extended to support order-a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine (N B-completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data streammodels that are more sophisticated than append-only relations, along with datamining queries, and other complex applications. © 2011 ACM. Source


Cheng L.,Bioinformatics Institute | Gong M.,Memorial University of Newfoundland | Schuurmans D.,University of Alberta | Caelli T.,University of Melbourne
IEEE Transactions on Image Processing | Year: 2011

The authors examine the problem of segmenting foreground objects in live video when background scene textures change over time. In particular, we formulate background subtraction as minimizing a penalized instantaneous risk functional - yielding a local online discriminative algorithm that can quickly adapt to temporal changes. We analyze the algorithm's convergence, discuss its robustness to nonstationarity, and provide an efficient nonlinear extension via sparse kernels. To accommodate interactions among neighboring pixels, a global algorithm is then derived that explicitly distinguishes objects versus background using maximum a posteriori inference in a Markov random field (implemented via graph-cuts). By exploiting the parallel nature of the proposed algorithms, we develop an implementation that can run efficiently on the highly parallel graphics processing unit (GPU). Empirical studies on a wide variety of datasets demonstrate that the proposed approach achieves quality that is comparable to state-of-the-art offline methods, while still being suitable for real-time video analysis (≥75 fps on a mid-range GPU). © 2006 IEEE. Source


Antibiotic resistance is a worldwide growing problem. Traditional antibiotics target specific intracellular microbial proteins, which are easily mutated. These mutations then alter recognition sites, which prevent drug molecules from killing bacteria or controlling their growth. Membrane-active antimicrobials are expected to thwart this resistance by selectively penetrating and disrupting bacterial membranes, which are more difficult to reconfigure than proteins. However, the mechanism for this disruption remains unclear. A lack of general design principles has limited the development of membrane-active antimicrobials as a viable tool. Now, teams led by Chandra Verma from the A*STAR Bioinformatics Institute and collaborator Roger Beuerman from the Singapore Eye Research Institute have developed a combined computational and experimental strategy for the rational design and synthesis of these next-generation antimicrobials. The researchers focused on the bacterial inner membrane to generate anti MRSA drug prototypes. By dividing the membrane into fragments according to wettability, they identified one hydrophobic—or water-repelling—section, sandwiched between two negatively charged regions. Next, they constructed a model comprising a hydrophobic core bearing positively charged terminal groups, which interact with the fragments. Finally, they derived the prototypes from this model using the natural substance xanthone as the core. The prototypes caused the bacterial membranes to leak, which demonstrated their antimicrobial activity. The higher leakage and permeation they displayed in the presence of bacterial membranes compared with their mammalian analogs was consistent with a low toxicity toward mammalian membranes. The researchers discovered that the antimicrobial action mechanism followed an adsorption–translocation–disruption sequence. Lead author, Jianguo Li, explains that the drug molecules initially took on a U-shaped configuration, promoting both electrostatic interactions between their terminal groups and outer membrane layer, or 'leaflet', and hydrophobic core insertion. Their accumulation gradually neutralized the outer leaflet, inducing membrane deformation and electrostatic attraction from the inner leaflet. This caused one of the antimicrobial terminal groups to cross the hydrophobic membrane section to interact with the inner leaflet, changing drug configuration and altering both membrane interfaces. "A certain number of membrane-active antimicrobials currently in clinical trials, such as XF-73, LTX-109, and brilacidin, match our model," says Li. "This is quite exciting because it is the first reported instance of membrane-based fragment assembly," he adds. The team is currently working on the development of antimicrobials that simultaneously target gram-positive and gram-negative pathogens. "We hope that the same concept can be extended to cancer cell membranes," says Verma. Explore further: How an emerging anti-resistance antibiotic targets the bacterial membrane More information: Jianguo Li et al. A novel fragment based strategy for membrane active antimicrobials against MRSA, Biochimica et Biophysica Acta (BBA) - Biomembranes (2015). DOI: 10.1016/j.bbamem.2015.01.001


Shi Q.,University of Adelaide | Cheng L.,Bioinformatics Institute | Wang L.,Nanjing Forestry University | Smola A.,Yahoo!
International Journal of Computer Vision | Year: 2011

A challenging problem in human action understanding is to jointly segment and recognize human actions from an unseen video sequence, where one person performs a sequence of continuous actions. In this paper, we propose a discriminative semi-Markov model approach, and define a set of features over boundary frames, segments, as well as neighboring segments. This enable us to conveniently capture a combination of local and global features that best represent each specific action type. To efficiently solve the inference problem of simultaneous segmentation and recognition, a Viterbi-like dynamic programming algorithm is utilized, which in practice is able to process 20 frames per second. Moreover, the model is discriminatively learned from large margin principle, and is formulated as an optimization problem with exponentially many constraints. To solve it efficiently, we present two different optimization algorithms, namely cutting plane method and bundle method, and demonstrate that each can be alternatively deployed in a "plug and play" fashion. From its theoretical aspect, we also analyze the generalization error of the proposed approach and provide a PAC-Bayes bound. The proposed approach is evaluated on a variety of datasets, and is shown to perform competitively to the state-of-the-art methods. For example, on KTH dataset, it achieves 95.0% recognition accuracy, where the best known result on this dataset is 93.4% (Reddy and Shah in ICCV, 2009). © 2010 Springer Science+Business Media, LLC. Source

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