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De novo protein fold families expand the designable ligand binding site space

PLoS Computational Biology 2021年第11期17卷 e1009620-e1009620页

作者： Pan, Xingjie Kortemme, Tanja Department of Bioengineering and Therapeutic Sciences University of California San Francisco San FranciscoCA United States UC Berkeley-UCSF Graduate Program in Bioengineering University of California San Francisco San FranciscoCA United States University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States

A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place ("match") these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. Each matching step involves engineering new binding site residues into each protein "scaffold", which is distinct from the problem of comparing already existing binding pockets. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions. © 2021 Pan, Kortemme. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Topology

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Physics-driven Deep Learning for PET/MRI

arXiv

引用

arXiv 2022年

作者： Rajagopal, Abhejit Leynes, Andrew P. Dwork, Nicholas Scholey, Jessica E. Hope, Thomas A. Larson, Peder E.Z. Department of Radiology and Biomedical Imaging University of California San Francisco94158 United States Uc Berkeley-UCSF Joint Graduate Program in Bioengineering University of California San Francisco United States University of California BerkeleyCA United States Department of Radiation Oncology University of California San Francisco94158 United States Departments of Pediatrics and Radiology University of Colorado Anschutz 80045 United States

In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease. These reconstruction approaches utilize priors, either structural or statistical, together with a physics-based description of the PET system response. However, due to the nested representation of the forward problem, direct PET/MRI reconstruction is a nonlinear problem. We elucidate how a multi-faceted approach accommodates hybrid data- and physics-driven machine learning for reconstruction of 3D PET/MRI, summarizing important deep learning developments made in the last 5 years to address attenuation correction, scattering, low photon counts, and data consistency. We also describe how applications of these multi-modality approaches extend beyond PET/MRI to improving accuracy in radiation therapy planning. We conclude by discussing opportunities for extending the current state-of-the-art following the latest trends in physics- and deep learning-based computational imaging and next-generation detector hardware. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Attenuation coefficient estimation for PET/MRI with Bayesian deep learning pseudo-CT and maximum likelihood estimation of activity and attenuation

arXiv

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arXiv 2020年

作者： Leynes, Andrew Palmera Ahn, Sangtae P. Wangerin, Kristen A. Kaushik, Sandeep S. Wiesinger, Florian Hope, Thomas A. Larson, Peder E.Z. The Department of Radiology and Biomedical Imaging University of California San Francisco San FranciscoCA94158 United States The UC Berkeley Joint Graduate Program in Bioengineering BerkeleyCA United States UC San Francisco Joint Graduate Program in Bioengineering San FranciscoCA United States GE Research NiskayunaNY United States GE Healthcare WaukeshaWI United States GE Healthcare Munich Germany The Department of Computer Science Technical University of Munich Munich Germany The Department of Radiology San Francisco VA Medical Center San FranciscoCA United States

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of MRI artifacts (e.g. implants, motion) and uncertainties due to the limitations of MRI contrast (e.g. accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that, in addition to generating an initial pseudo-CT from MR data, also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with MLAA reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher RMSE than Zero-echo-time and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant;however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy;however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions. © 2020, CC BY.

关键词： Magnetic resonance

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Simulation of lung alveolar type II epithelial wound healing in vitro

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Journal of Critical Care 2009年第3期24卷 e21-e22页

作者： Sean H.J. Kim C. Anthony Hunt UCSF/UC Berkeley Joint Graduate Group in Bioengineering University of California Berkeley Department of Bioengineering and Therapeutic Sciences University of California San Francisco

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Mechanistic simulations explain paradoxical saquinavir metabolism during in vitro vectorial transport study

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Journal of Critical Care 2009年第3期24卷 e19-e20页

作者： Lam, Tai Ning Hunt, C. Anthony UCSF/UC Berkeley Joint Graduate Group in Bioengineering University of California Berkeley Department of Bioengineering and Therapeutic Sciences University of California San Francisco

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Linear Dynamical System Response Modeling of Trial-to-Trial Amplitude Variability in Event-Related MEG/EEG

Linear Dynamical System Response Modeling of Trial-to-Trial ...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Tulaya Limpiti Barry D. Van Veen Srikantan S. Nagarajan Hagai T. Attias Department of Electrical and Computer Engineering University of Wisconsin Madison Madison WI USA Biomagnetic Imaging Lab Department of Radiology University of California San Francisco San Francisco CA USA Joint Graduate Group in Bioengineering UCSF UC Berkeley CA USA Golden Metallic Inc. San Francisco CA USA

We propose a linear dynamical system response (LDSR) model to describe amplitude variability across trials in event-related magnetoencephalographic/electroencephalographic (MEG/EEG) data. Variability across trials may reflect habituation, fatigue, or changes in cognitive states of the brain. A wide range of trial-to-trial variability can be represented using the LDSR model, including the constant response (CR) model as a limiting case. The spatiotemporal signal waveform is assumed constant but unknown, and is represented using a linear combination of spatial and temporal basis functions. The background noise is assumed to be spatially correlated with unknown covariance matrix. We obtain the maximum-likelihood estimates of the amplitude variability and signal waveform via a generalized Expectation-Maximization algorithm. The expectation step involves a Kalman fixed-interval smoother which tracks the trial-to-trial amplitude variability while the maximization step estimates the signal waveform, spatial noise covariance, and LDSR model parameters. We demonstrate the effectiveness of the proposed model using both real and simulated evoked response data. The performance of the algorithm is analyzed in terms of the mean squared error of the amplitude estimates.

关键词： Brain modeling Electroencephalography Amplitude estimation Fatigue Chromium Spatiotemporal phenomena Background noise Covariance matrix Maximum likelihood estimation Expectation-maximization algorithms

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Detection of Isoforms Differing by a Single Charge Unit in Individual Cells

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Angewandte Chemie 2016年第40期128卷

作者： Augusto M. Tentori Kevin A. Yamauchi Amy E. Herr The UC Berkeley/UCSF Graduate Program in Bioengineering Berkeley CA USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Bioengineering UC Berkeley 308B Stanley Hall Berkeley CA 94720 USA

To measure protein isoforms in individual mammalian cells, we report single‐cell resolution isoelectric focusing (scIEF) and high‐selectivity immunoprobing. Microfluidic design and photoactivatable materials establish the tunable pH gradients required by IEF and precisely control the transport and handling of each 17‐pL cell lysate during analysis. The scIEF assay resolves protein isoforms with resolution down to a single‐charge unit, including both endogenous cytoplasmic and nuclear proteins from individual mammalian cells.

关键词： Einzelzellanalyse Immunassays Isoelektrische Fokussierung Lab-on-a-Chip Proteomik

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55.1:Distinguished Paper: Motion Artifacts on 240Hz OLED Stereoscopic 3D Displays

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SID Symposium Digest of Technical Papers 2014年第1期45卷

作者： Paul Johnson Joohwan Kim David M. Hoffman Andres Vargas Martin S. Banks UC Berkeley – UCSF Graduate Program in Bioengineering University of California Berkeley Vision Science Program 360 Minor Hall Berkeley CA USA Samsung Display Americas Lab San Jose CA USA

We examined the visibility of motion artifacts—judder, motion blur, and edge banding—on a Samsung 240Hz stereoscopic 3D (S3D) OLED display. We determined the relative contributions of the frame rate of the content, update rate of the panel, duty cycle, and flash number. We compared the visibility of artifacts on the Samsung display with those on a 60Hz S3D LCD. Short duty cycles and low flash numbers reduce the visibility of motion artifacts.

关键词： OLED stereoscopic 3D motion perception motion sampling frame rate

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Contributors to the Lab on a Chip cell and tissue engineering in microsystems special issue

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Lab on a Chip 2007年第6期7卷 667-671页

作者： Bhatia, Sangeeta N. Chen, Christoper Bashir, Rashid Beebe, David J. Desai, Tejal Gottwald, Eric Khademhosseini, Ali Langer, Robert S. Piel, Matthieu Takayama, Shuichi Thorsen, Todd Tien, Joe Voldman, Joel Vunjak-Novakovic, Gordana Wu, Mingming Yu, Hanry Massachusetts Institute of Technology Harvard-MIT Division of Health Sciences and Technology Department of Electrical Engineering and Computer Science United States Laboratory for Multiscale Regenerative Technologies United States Department of Innovation University of Pennsylvania Department of Bioengineering United States Cell Biology and Physiology Program Cell Growth and Cancer Program Tissue Microfabrication Laboratory Department of Electrical and Computer Engineering and Biomedical Engineering Purdue University United States Department of Electronics University of Glasgow United Kingdom Department of Physiology and Bioengineering University of California San Francisco CA United States California Institute for Quantitative Biomedical Research UCSF/UC Berkeley Graduate Group in Bioengineering United States Laboratory of Therapeutic Micro and Nanotechnology Institute for Biological Interfaces Germany 3D-Cell Culture Techniques Group Institute for Biological Interfaces Germany Department of Medicine and Health Sciences and Technology Massachusetts Institute of Technology (MIT) United States Institut Curie UMR 144 IC/CNRS France Biomedical Engineering Department University of Michigan United States Department of Biomedical Engineering Macromolecular Science and Engineering Program United States Sibley School of Mechanical and Aerospace Engineering School of Chemical and Molecular Engineering Cornell University United States National University of Singapore Singapore Institute of Bioengineering and Nanotechnology A STAR Singapore

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Decoding position from multiunit activity using a marked point process filter

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BMC Neuroscience 2015年第1期16卷 1-1页

作者： Xinyi Deng Uri T Eden Daniel F Liu Kenneth Kay Loren M Frank Department of Mathematics and Statistics Boston University Boston MA USA UC Berkeley--UCSF Graduate Program in Bioengineering UCSF San Francisco CA USA Department of Physiology UCSF San Francisco CA USA

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