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Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.
Copyright © 2019 Elsevier Ltd. All rights reserved.
SUMMARY - Single cell RNA sequencing is a revolutionary technique to characterize inter-cellular transcriptomics heterogeneity. However, the data are noise-prone because gene expression is often driven by both technical artifacts and genuine biological variations. Proper disentanglement of these two effects is critical to prevent spurious results. While several tools exist to detect and remove low-quality cells in one single cell RNA-seq dataset, there is lack of approach to examining consistency between sample sets and detecting systematic biases, batch effects and outliers. We present scRNABatchQC, an R package to compare multiple sample sets simultaneously over numerous technical and biological features, which gives valuable hints to distinguish technical artifact from biological variations. scRNABatchQC helps identify and systematically characterize sources of variability in single cell transcriptome data. The examination of consistency across datasets allows visual detection of biases and outliers.
AVAILABILITY AND IMPLEMENTATION - scRNABatchQC is freely available at https://github.com/liuqivandy/scRNABatchQC as an R package.
SUPPLEMENTARY INFORMATION - Supplementary data are available at Bioinformatics online.
© The Author(s) 2019. Published by Oxford University Press.
Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.
© 2019 Wiley Periodicals, Inc.
This chapter provides a broad overview of ion mobility-mass spectrometry (IM-MS) and its applications in separation science, with a focus on pharmaceutical applications. A general overview of fundamental ion mobility (IM) theory is provided with descriptions of several contemporary instrument platforms which are available commercially (i.e., drift tube and traveling wave IM). Recent applications of IM-MS toward the evaluation of structural isomers are highlighted and placed in the context of both a separation and characterization perspective. We conclude this chapter with a guided reference protocol for obtaining routine IM-MS spectra on a commercially available uniform-field IM-MS.
Scaffold proteins tether and orient components of a signaling cascade to facilitate signaling. Although much is known about how scaffolds colocalize signaling proteins, it is unclear whether scaffolds promote signal amplification. Here, we used arrestin-3, a scaffold of the ASK1-MKK4/7-JNK3 cascade, as a model to understand signal amplification by a scaffold protein. We found that arrestin-3 exhibited >15-fold higher affinity for inactive JNK3 than for active JNK3, and this change involved a shift in the binding site following JNK3 activation. We used systems biochemistry modeling and Bayesian inference to evaluate how the activation of upstream kinases contributed to JNK3 phosphorylation. Our combined experimental and computational approach suggested that the catalytic phosphorylation rate of JNK3 at Thr-221 by MKK7 is two orders of magnitude faster than the corresponding phosphorylation of Tyr-223 by MKK4 with or without arrestin-3. Finally, we showed that the release of activated JNK3 was critical for signal amplification. Collectively, our data suggest a "conveyor belt" mechanism for signal amplification by scaffold proteins. This mechanism informs on a long-standing mystery for how few upstream kinase molecules activate numerous downstream kinases to amplify signaling.
PURPOSE - The non-uniform fast Fourier transform (NUFFT) involves interpolation of non-uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non-local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non-Cartesian acquisition methods.
METHODS - Here we customize the NUFFT procedure for a radial trajectory and GPU architecture to eliminate the bottlenecks encountered when allowing for arbitrary trajectories and hardware. We call the result TRON, for TRajectory Optimized NUFFT. We benchmark the speed and accuracy TRON on a Shepp-Logan phantom and on whole-body continuous golden-angle radial MRI.
RESULTS - TRON was 6-30× faster than the closest competitor, depending on test data set, and was the most accurate code tested.
CONCLUSIONS - Specialization of the NUFFT algorithm for a particular trajectory yielded significant speed gains. TRON can be easily extended to other trajectories, such as spiral and PROPELLER. TRON can be downloaded at http://github.com/davidssmith/TRON.
© 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
Copyright © 2018 Elsevier Inc. All rights reserved.
Arrays of radiofrequency coils are widely used in magnetic resonance imaging to achieve high signal-to-noise ratios and flexible volume coverage, to accelerate scans using parallel reception, and to mitigate field non-uniformity using parallel transmission. However, conventional coil arrays require complex decoupling technologies to reduce electromagnetic coupling between coil elements, which would otherwise amplify noise and limit transmitted power. Here we report a novel self-decoupled RF coil design with a simple structure that requires only an intentional redistribution of electrical impedances around the length of the coil loop. We show that self-decoupled coils achieve high inter-coil isolation between adjacent and non-adjacent elements of loop arrays and mixed arrays of loops and dipoles. Self-decoupled coils are also robust to coil separation, making them attractive for size-adjustable and flexible coil arrays.
State-of-the-art strategies for proteomics are not able to rapidly interrogate complex peptide mixtures in an untargeted manner with sensitive peptide and protein identification rates. We describe a data-independent acquisition (DIA) approach, microDIA (μDIA), that applies a novel tandem mass spectrometry (MS/MS) mass spectral deconvolution method to increase the specificity of tandem mass spectra acquired during proteomics experiments. Using the μDIA approach with a 10 min liquid chromatography gradient allowed detection of 3.1-fold more HeLa proteins than the results obtained from data-dependent acquisition (DDA) of the same samples. Additionally, we found the μDIA MS/MS deconvolution procedure is critical for resolving modified peptides with relatively small precursor mass shifts that cause the same peptide sequence in modified and unmodified forms to theoretically cofragment in the same raw MS/MS spectra. The μDIA workflow is implemented in the PROTALIZER software tool which fully automates tandem mass spectral deconvolution, queries every peptide with a library-free search algorithm against a user-defined protein database, and confidently identifies multiple peptides in a single tandem mass spectrum. We also benchmarked μDIA against DDA using a 90 min gradient analysis of HeLa and Escherichia coli peptides that were mixed in predefined quantitative ratios, and our results showed μDIA provided 24% more true positives at the same false positive rate.
OBJECTIVE - Changes in microvascular perfusion have been reported in many diseases, yet the functional significance of altered perfusion is often difficult to determine. This is partly because commonly used techniques for perfusion measurement often rely on either indirect or by-hand approaches.
METHODS - We developed and validated a fully automated software technique to measure microvascular perfusion in videos acquired by fluorescence microscopy in the mouse gastrocnemius. Acute perfusion responses were recorded following intravenous injections with phenylephrine, SNP, or saline.
RESULTS - Software-measured capillary flow velocity closely correlated with by-hand measured flow velocity (R = 0.91, P < 0.0001). Software estimates of capillary hematocrit also generally agreed with by-hand measurements (R = 0.64, P < 0.0001). Detection limits range from 0 to 2000 μm/s, as compared to an average flow velocity of 326 ± 102 μm/s (mean ± SD) at rest. SNP injection transiently increased capillary flow velocity and hematocrit and made capillary perfusion more steady and homogenous. Phenylephrine injection had the opposite effect in all metrics. Saline injection transiently decreased capillary flow velocity and hematocrit without influencing flow distribution or stability. All perfusion metrics were temporally stable without intervention.
CONCLUSIONS - These results demonstrate a novel and sensitive technique for reproducible, user-independent quantification of microvascular perfusion.
© 2018 John Wiley & Sons Ltd.