A flexible and scalable approach for protein NMR is introduced that builds on rapid data collection via projection spectroscopy and analysis of the spectral input data via joint decomposition. Input data may originate from various types of spectra, depending on the ultimate goal: these may result from experiments based on triple-resonance pulse sequences, or on TOCSY or NOESY sequences, or mixtures thereof. Flexible refers to the free choice of spectra for the joint decompositions depending on the purpose: assignments, structure, dynamics, interactions. Scalable means that the approach is open to the addition of similar or different experiments, e.g. larger proteins may require a wider selection of triple-resonance based experiments. Central to the proposed approach is the mutual support among the different spectra during the spectral analysis: for example, sparser triple-resonance spectra may help decomposing (separating) spin systems in a TOCSY or identifying unique NOEs. In the example presented, backbone plus side chain assignments of ubiquitin were obtained from the combination of either two or three of the following projection experiments: a 4D HCCCONH, a 4D HNCACO and a 3D HNCACB. In all cases, TOCSY data (4D HCCCONH) proved crucial not only for the side chain assignments, but also for the sequential assignment. Even when total recording time was reduced to about 10 h, nearly complete assignments were obtained, with very few missing assignments and even fewer differences to a reference.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441399 | PMC |
| http://dx.doi.org/10.1007/s10858-019-00226-8 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Nutritional intelligence in the food system: Combining food, health, data and AI expertise.
Nutr Bull
January 2025
Queen's University Belfast, Belfast, UK.
Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system.
View Article and Find Full Text PDFRecent advances in plant protein-based sustainable edible film and coatings for applications in the food-pharma industry - Opportunities and challenges: A review.
Int J Biol Macromol
January 2025
Department of Food Technology, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore 641021, India. Electronic address:
Plant protein-based edible film and coatings have emerged as eco-friendly alternatives to synthetic packaging, offering biodegradable, non-toxic solutions. Their biocompatibility and film-forming properties make them suitable for direct application on food products, reducing reliance on non-degradable plastics and lowering environmental pollution. Despite their promising advantages, challenges remain in optimizing mechanical properties, production scalability, and consumer acceptance.
View Article and Find Full Text PDFAutomatic medical imaging segmentation via self-supervising large-scale convolutional neural networks.
Radiother Oncol
January 2025
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology Atlanta, GA 30308, USA. Electronic address:
Purpose: This study aims to develop a robust, large-scale deep learning model for medical image segmentation, leveraging self-supervised learning to overcome the limitations of supervised learning and data variability in clinical settings.
Methods And Materials: We curated a substantial multi-center CT dataset for self-supervised pre-training using masked image modeling with sparse submanifold convolution. We designed a series of Sparse Submanifold U-Nets (SS-UNets) of varying sizes and performed self-supervised pre-training.
Personalized stress optimization intervention to reduce adolescents' anxiety: A randomized controlled trial leveraging machine learning.
J Anxiety Disord
January 2025
School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China. Electronic address:
Anxiety symptoms are among the most prevalent mental health disorders in adolescents, highlighting the need for scalable and accessible interventions. As anxiety often co-occurs with perceived stress during adolescence, stress interventions may offer a promising approach to reducing anxiety. Previous stress interventions have largely focused on the view that stress is harmful, aiming to manage and mitigate its negative effects.
View Article and Find Full Text PDFGiant Purcell Broadening and Lamb Shift for DNA-Assembled Near-Infrared Quantum Emitters.
ACS Nano
January 2025
Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
Controlling the light emitted by individual molecules is instrumental to a number of advanced nanotechnologies ranging from super-resolution bioimaging and molecular sensing to quantum nanophotonics. Molecular emission can be tailored by modifying the local photonic environment, for example, by precisely placing a single molecule inside a plasmonic nanocavity with the help of DNA origami. Here, using this scalable approach, we show that commercial fluorophores may experience giant Purcell factors and Lamb shifts, reaching values on par with those recently reported in scanning tip experiments.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!