Browsing by Author "Li, Sheng"

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    Detection of lipid-induced structural changes of the Marburg virus matrix protein VP40 using hydrogen/deuterium exchange-mass spectrometry
    (American Society for Biochemistry and Molecular Biology, 2017-04-14) Wijesinghe, Kaveesha J.; Urata, Sarah; Bhattarai, Nisha; Kooijman, Edgar E.; Gerstman, Bernard S.; Chapagain, Prem P.; Li, Sheng; Stahelin, Robert V.; Biochemistry and Molecular Biology, School of Medicine
    Marburg virus (MARV) is a lipid-enveloped virus from the Filoviridae family containing a negative sense RNA genome. One of the seven MARV genes encodes the matrix protein VP40, which forms a matrix layer beneath the plasma membrane inner leaflet to facilitate budding from the host cell. MARV VP40 (mVP40) has been shown to be a dimeric peripheral protein with a broad and flat basic surface that can associate with anionic phospholipids such as phosphatidylserine. Although a number of mVP40 cationic residues have been shown to facilitate binding to membranes containing anionic lipids, much less is known on how mVP40 assembles to form the matrix layer following membrane binding. Here we have used hydrogen/deuterium exchange (HDX) mass spectrometry to determine the solvent accessibility of mVP40 residues in the absence and presence of phosphatidylserine and phosphatidylinositol 4,5-bisphosphate. HDX analysis demonstrates that two basic loops in the mVP40 C-terminal domain make important contributions to anionic membrane binding and also reveals a potential oligomerization interface in the C-terminal domain as well as a conserved oligomerization interface in the mVP40 N-terminal domain. Lipid binding assays confirm the role of the two basic patches elucidated with HD/X measurements, whereas molecular dynamics simulations and membrane insertion measurements complement these studies to demonstrate that mVP40 does not appreciably insert into the hydrocarbon region of anionic membranes in contrast to the matrix protein from Ebola virus. Taken together, we propose a model by which association of the mVP40 dimer with the anionic plasma membrane facilitates assembly of mVP40 oligomers.
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    Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
    (Springer, 2018) Ding, Zhengming; Li, Sheng; Shao, Ming; Fu, Yun; Electrical and Computer Engineering, School of Engineering and Technology
    Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches.
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    Marginalized Multiview Ensemble Clustering
    (IEEE, 2019-04) Tao, Zhiqiang; Liu, Hongfu; Li, Sheng; Ding, Zhengming; Fu, Yun; Computer Information and Graphics Technology, School of Engineering and Technology
    Multiview clustering (MVC), which aims to explore the underlying cluster structure shared by multiview data, has drawn more research efforts in recent years. To exploit the complementary information among multiple views, existing methods mainly learn a common latent subspace or develop a certain loss across different views, while ignoring the higher level information such as basic partitions (BPs) generated by the single-view clustering algorithm. In light of this, we propose a novel marginalized multiview ensemble clustering (M 2 VEC) method in this paper. Specifically, we solve MVC in an EC way, which generates BPs for each view individually and seeks for a consensus one. By this means, we naturally leverage the complementary information of multiview data upon the same partition space. In order to boost the robustness of our approach, the marginalized denoising process is adopted to mimic the data corruptions and noises, which provides robust partition-level representations for each view by training a single-layer autoencoder. A low-rank and sparse decomposition is seamlessly incorporated into the denoising process to explicitly capture the consistency information and meanwhile compensate the distinctness between heterogeneous features. Spectral consensus graph partitioning is also involved by our model to make M 2 VEC as a unified optimization framework. Moreover, a multilayer M 2 VEC is eventually delivered in a stacked fashion to encapsulate nonlinearity into partition-level representations for handling complex data. Experimental results on eight real-world data sets show the efficacy of our approach compared with several state-of-the-art multiview and EC methods. We also showcase our method performs well with partial multiview data.
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    Structures and Functions of the Multiple KOW Domains of Transcription Elongation Factor Spt5
    (American Society for Microbiology, 2015-10) Meyer, Peter A.; Li, Sheng; Zhang, Mincheng; Yamada, Kentaro; Takagi, Yuichiro; Hartzog, Grant A.; Fu, Jianhua; Department of Biochemistry & Molecular Biology, IU School of Medicine
    The eukaryotic Spt4-Spt5 heterodimer forms a higher-order complex with RNA polymerase II (and I) to regulate transcription elongation. Extensive genetic and functional data have revealed diverse roles of Spt4-Spt5 in coupling elongation with chromatin modification and RNA-processing pathways. A mechanistic understanding of the diverse functions of Spt4-Spt5 is hampered by challenges in resolving the distribution of functions among its structural domains, including the five KOW domains in Spt5, and a lack of their high-resolution structures. We present high-resolution crystallographic results demonstrating that distinct structures are formed by the first through third KOW domains (KOW1-Linker1 [K1L1] and KOW2-KOW3) of Saccharomyces cerevisiae Spt5. The structure reveals that K1L1 displays a positively charged patch (PCP) on its surface, which binds nucleic acids in vitro, as shown in biochemical assays, and is important for in vivo function, as shown in growth assays. Furthermore, assays in yeast have shown that the PCP has a function that partially overlaps that of Spt4. Synthesis of our results with previous evidence suggests a model in which Spt4 and the K1L1 domain of Spt5 form functionally overlapping interactions with nucleic acids upstream of the transcription bubble, and this mechanism may confer robustness on processes associated with transcription elongation.
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    Toward Resolution-Invariant Person Reidentification via Projective Dictionary Learning
    (IEEE, 2019-06) Li, Kai; Ding, Zhengming; Li, Sheng; Fu, Yun; Computer Information and Graphics Technology, School of Engineering and Technology
    Person reidentification (ReID) has recently been widely investigated for its vital role in surveillance and forensics applications. This paper addresses the low-resolution (LR) person ReID problem, which is of great practical meaning because pedestrians are often captured in LRs by surveillance cameras. Existing methods cope with this problem via some complicated and time-consuming strategies, making them less favorable, in practice, and meanwhile, their performances are far from satisfactory. Instead, we solve this problem by developing a discriminative semicoupled projective dictionary learning (DSPDL) model, which adopts the efficient projective dictionary learning strategy, and jointly learns a pair of dictionaries and a mapping function to model the correspondence of the cross-view data. A parameterless cross-view graph regularizer incorporating both positive and negative pair information is designed to enhance the discriminability of the dictionaries. Another weakness of existing approaches to this problem is that they are only applicable for the scenario where the cross-camera image sets have a globally uniform resolution gap. This fact undermines their practicality because the resolution gaps between cross-camera images often vary person by person in practice. To overcome this hurdle, we extend the proposed DSPDL model to the variational resolution gap scenario, basically by learning multiple pairs of dictionaries and multiple mapping functions. A novel technique is proposed to rerank and fuse the results obtained from all dictionary pairs. Experiments on five public data sets show the proposed method achieves superior performances to the state-of-the-art ones.