Matching Items (147)
Description
Serial femtosecond X-ray crystallography (SFX) using an X-ray free electron laser (XFEL) is a recent advancement in structural biology for solving crystal structures of challenging membrane proteins, including G-protein coupled receptors (GPCRs), which often only produce microcrystals. An XFEL delivers highly intense X-ray pulses of femtosecond duration short enough to enable the collection of single diffraction images before significant radiation damage to crystals sets in. Here we report the deposition of the XFEL data and provide further details on crystallization, XFEL data collection and analysis, structure determination, and the validation of the structural model. The rhodopsin-arrestin crystal structure solved with SFX represents the first near-atomic resolution structure of a GPCR-arrestin complex, provides structural insights into understanding of arrestin-mediated GPCR signaling, and demonstrates the great potential of this SFX-XFEL technology for accelerating crystal structure determination of challenging proteins and protein complexes.
ContributorsZhou, X. Edward (Author) / Gao, Xiang (Author) / Barty, Anton (Author) / Kang, Yanyong (Author) / He, Yuanzheng (Author) / Liu, Wei (Author) / Ishchenko, Andrii (Author) / White, Thomas A. (Author) / Yefanov, Oleksandr (Author) / Han, Gye Won (Author) / Xu, Qingping (Author) / de Waal, Parker W. (Author) / Suino-Powell, Kelly M. (Author) / Boutet, Sebastien (Author) / Williams, Garth J. (Author) / Wang, Meitian (Author) / Li, Dianfan (Author) / Caffrey, Martin (Author) / Chapman, Henry N. (Author) / Spence, John (Author) / Fromme, Petra (Author) / Weierstall, Uwe (Author) / Stevens, Raymond C. (Author) / Cherezov, Vadim (Author) / Melcher, Karsten (Author) / Xu, H. Eric (Author) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / Department of Physics (Contributor)
Created2016-04-12
Description
Diacylglycerol kinase catalyses the ATP-dependent conversion of diacylglycerol to phosphatidic acid in the plasma membrane of Escherichia coli. The small size of this integral membrane trimer, which has 121 residues per subunit, means that available protein must be used economically to craft three catalytic and substrate-binding sites centred about the membrane/cytosol interface. How nature has accomplished this extraordinary feat is revealed here in a crystal structure of the kinase captured as a ternary complex with bound lipid substrate and an ATP analogue. Residues, identified as essential for activity by mutagenesis, decorate the active site and are rationalized by the ternary structure. The γ-phosphate of the ATP analogue is positioned for direct transfer to the primary hydroxyl of the lipid whose acyl chain is in the membrane. A catalytic mechanism for this unique enzyme is proposed. The active site architecture shows clear evidence of having arisen by convergent evolution.
ContributorsLi, Dianfan (Author) / Stansfeld, Phillip J. (Author) / Sansom, Mark S. P. (Author) / Keogh, Aaron (Author) / Vogeley, Lutz (Author) / Howe, Nicole (Author) / Lyons, Joseph A. (Author) / Aragao, David (Author) / Fromme, Petra (Author) / Fromme, Raimund (Author) / Basu, Shibom (Author) / Grotjohann, Ingo (Author) / Kupitz, Christopher (Author) / Rendek, Kimberley (Author) / Weierstall, Uwe (Author) / Zatsepin, Nadia (Author) / Cherezov, Vadim (Author) / Liu, Wei (Author) / Bandaru, Sateesh (Author) / English, Niall J. (Author) / Gati, Cornelius (Author) / Barty, Anton (Author) / Yefanov, Oleksandr (Author) / Chapman, Henry N. (Author) / Diederichs, Kay (Author) / Messerschmidt, Marc (Author) / Boutet, Sebastien (Author) / Williams, Garth J. (Author) / Seibert, M. Marvin (Author) / Caffrey, Martin (Author) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / Department of Physics (Contributor)
Created2015-12-17
Description
Phytochromes are a family of photoreceptors that control light responses of plants, fungi and bacteria. A sequence of structural changes, which is not yet fully understood, leads to activation of an output domain. Time-resolved serial femtosecond crystallography (SFX) can potentially shine light on these conformational changes. Here we report the room temperature crystal structure of the chromophore-binding domains of the Deinococcus radiodurans phytochrome at 2.1 Å resolution. The structure was obtained by serial femtosecond X-ray crystallography from microcrystals at an X-ray free electron laser. We find overall good agreement compared to a crystal structure at 1.35 Å resolution derived from conventional crystallography at cryogenic temperatures, which we also report here. The thioether linkage between chromophore and protein is subject to positional ambiguity at the synchrotron, but is fully resolved with SFX. The study paves the way for time-resolved structural investigations of the phytochrome photocycle with time-resolved SFX.
ContributorsEdlund, Petra (Author) / Takala, Heikki (Author) / Claesson, Elin (Author) / Henry, Leocadie (Author) / Dods, Robert (Author) / Lehtivuori, Heli (Author) / Panman, Matthijs (Author) / Pande, Kanupriya (Author) / White, Thomas (Author) / Nakane, Takanori (Author) / Berntsson, Oskar (Author) / Gustavsson, Emil (Author) / Bath, Petra (Author) / Modi, Vaibhav (Author) / Roy Chowdhury, Shatabdi (Author) / Zook, James (Author) / Berntsen, Peter (Author) / Pandey, Suraj (Author) / Poudyal, Ishwor (Author) / Tenboer, Jason (Author) / Kupitz, Christopher (Author) / Barty, Anton (Author) / Fromme, Petra (Author) / Koralek, Jake D. (Author) / Tanaka, Tomoyuki (Author) / Spence, John (Author) / Liang, Mengning (Author) / Hunter, Mark S. (Author) / Boutet, Sebastien (Author) / Nango, Eriko (Author) / Moffat, Keith (Author) / Groenhof, Gerrit (Author) / Ihalainen, Janne (Author) / Stojkovic, Emina A. (Author) / Schmidt, Marius (Author) / Westenhoff, Sebastian (Author) / College of Liberal Arts and Sciences (Contributor) / School of Molecular Sciences (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / Department of Physics (Contributor)
Created2016-10-19
Description
Serial femtosecond crystallography (SFX) using X-ray free-electron laser sources is an emerging method with considerable potential for time-resolved pump-probe experiments. Here we present a lipidic cubic phase SFX structure of the light-driven proton pump bacteriorhodopsin (bR) to 2.3 Å resolution and a method to investigate protein dynamics with modest sample requirement. Time-resolved SFX (TR-SFX) with a pump-probe delay of 1 ms yields difference Fourier maps compatible with the dark to M state transition of bR. Importantly, the method is very sample efficient and reduces sample consumption to about 1 mg per collected time point. Accumulation of M intermediate within the crystal lattice is confirmed by time-resolved visible absorption spectroscopy. This study provides an important step towards characterizing the complete photocycle dynamics of retinal proteins and demonstrates the feasibility of a sample efficient viscous medium jet for TR-SFX.
ContributorsNogly, Przemyslaw (Author) / Panneels, Valerie (Author) / Nelson, Garrett (Author) / Gati, Cornelius (Author) / Kimura, Tetsunari (Author) / Milne, Christopher (Author) / Milathianaki, Despina (Author) / Kubo, Minoru (Author) / Wu, Wenting (Author) / Conrad, Chelsie (Author) / Coe, Jesse (Author) / Bean, Richard (Author) / Zhao, Yun (Author) / Bath, Petra (Author) / Dods, Robert (Author) / Harimoorthy, Rajiv (Author) / Beyerlein, Kenneth R. (Author) / Rheinberger, Jan (Author) / James, Daniel (Author) / Deponte, Daniel (Author) / Li, Chufeng (Author) / Sala, Leonardo (Author) / Williams, Garth J. (Author) / Hunter, Mark S. (Author) / Koglin, Jason E. (Author) / Berntsen, Peter (Author) / Nango, Eriko (Author) / Iwata, So (Author) / Chapman, Henry N. (Author) / Fromme, Petra (Author) / Frank, Matthias (Author) / Abela, Rafael (Author) / Boutet, Sebastien (Author) / Barty, Anton (Author) / White, Thomas A. (Author) / Weierstall, Uwe (Author) / Spence, John (Author) / Neutze, Richard (Author) / Schertler, Gebhard (Author) / Standfuss, Jorg (Author) / College of Liberal Arts and Sciences (Contributor) / Department of Physics (Contributor) / Department of Chemistry and Biochemistry (Contributor) / Biodesign Institute (Contributor) / Applied Structural Discovery (Contributor) / School of Molecular Sciences (Contributor)
Created2016-08-22
Description
Magnetic resonance imaging (MRI) data of metastatic brain cancer patients at the Barrow Neurological Institute sparked interest in the radiology department due to the possibility that tumor size distributions might mimic a power law or an exponential distribution. In order to consider the question regarding the growth trends of metastatic brain tumors, this thesis analyzes the volume measurements of the tumor sizes from the BNI data and attempts to explain such size distributions through mathematical models. More specifically, a basic stochastic cellular automaton model is used and has three-dimensional results that show similar size distributions of those of the BNI data. Results of the models are investigated using the likelihood ratio test suggesting that, when the tumor volumes are measured based on assuming tumor sphericity, the tumor size distributions significantly mimic the power law over an exponential distribution.
ContributorsFreed, Rebecca (Co-author) / Snopko, Morgan (Co-author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Prostate cancer is the second most common kind of cancer in men. Fortunately, it has a 99% survival rate. To achieve such a survival rate, a variety of aggressive therapies are used to treat prostate cancers that are caught early. Androgen deprivation therapy (ADT) is a therapy that is given in cycles to patients. This study attempted to analyze what factors in a group of 79 patients caused them to stick with or discontinue the treatment. This was done using naïve Bayes classification, a machine-learning algorithm. The usage of this algorithm identified high testosterone as an indicator of a patient persevering with the treatment, but failed to produce statistically significant high rates of prediction.
ContributorsMillea, Timothy Michael (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Open source image analytics and data mining software are widely available but can be overly-complicated and non-intuitive for medical physicians and researchers to use. The ASU-Mayo Clinic Imaging Informatics Lab has developed an in-house pipeline to process medical images, extract imaging features, and develop multi-parametric models to assist disease staging and diagnosis. The tools have been extensively used in a number of medical studies including brain tumor, breast cancer, liver cancer, Alzheimer's disease, and migraine. Recognizing the need from users in the medical field for a simplified interface and streamlined functionalities, this project aims to democratize this pipeline so that it is more readily available to health practitioners and third party developers.
ContributorsBaer, Lisa Zhou (Author) / Wu, Teresa (Thesis director) / Wang, Yalin (Committee member) / Computer Science and Engineering Program (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Video object segmentation (VOS) is an important task in computer vision with a lot of applications, e.g., video editing, object tracking, and object based encoding. Different from image object segmentation, video object segmentation must consider both spatial and temporal coherence for the object. Despite extensive previous work, the problem is still challenging. Usually, foreground object in the video draws more attention from humans, i.e. it is salient. In this thesis we tackle the problem from the aspect of saliency, where saliency means a certain subset of visual information selected by a visual system (human or machine). We present a novel unsupervised method for video object segmentation that considers both low level vision cues and high level motion cues. In our model, video object segmentation can be formulated as a unified energy minimization problem and solved in polynomial time by employing the min-cut algorithm. Specifically, our energy function comprises the unary term and pair-wise interaction energy term respectively, where unary term measures region saliency and interaction term smooths the mutual effects between object saliency and motion saliency. Object saliency is computed in spatial domain from each discrete frame using multi-scale context features, e.g., color histogram, gradient, and graph based manifold ranking. Meanwhile, motion saliency is calculated in temporal domain by extracting phase information of the video. In the experimental section of this thesis, our proposed method has been evaluated on several benchmark datasets. In MSRA 1000 dataset the result demonstrates that our spatial object saliency detection is superior to the state-of-art methods. Moreover, our temporal motion saliency detector can achieve better performance than existing motion detection approaches in UCF sports action analysis dataset and Weizmann dataset respectively. Finally, we show the attractive empirical result and quantitative evaluation of our approach on two benchmark video object segmentation datasets.
ContributorsWang, Yilin (Author) / Li, Baoxin (Thesis advisor) / Wang, Yalin (Committee member) / Cleveau, David (Committee member) / Arizona State University (Publisher)
Created2013
Description
Learning from high dimensional biomedical data attracts lots of attention recently. High dimensional biomedical data often suffer from the curse of dimensionality and have imbalanced class distributions. Both of these features of biomedical data, high dimensionality and imbalanced class distributions, are challenging for traditional machine learning methods and may affect the model performance. In this thesis, I focus on developing learning methods for the high-dimensional imbalanced biomedical data. In the first part, a sparse canonical correlation analysis (CCA) method is presented. The penalty terms is used to control the sparsity of the projection matrices of CCA. The sparse CCA method is then applied to find patterns among biomedical data sets and labels, or to find patterns among different data sources. In the second part, I discuss several learning problems for imbalanced biomedical data. Note that traditional learning systems are often biased when the biomedical data are imbalanced. Therefore, traditional evaluations such as accuracy may be inappropriate for such cases. I then discuss several alternative evaluation criteria to evaluate the learning performance. For imbalanced binary classification problems, I use the undersampling based classifiers ensemble (UEM) strategy to obtain accurate models for both classes of samples. A small sphere and large margin (SSLM) approach is also presented to detect rare abnormal samples from a large number of subjects. In addition, I apply multiple feature selection and clustering methods to deal with high-dimensional data and data with highly correlated features. Experiments on high-dimensional imbalanced biomedical data are presented which illustrate the effectiveness and efficiency of my methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013