The RosettaCarbohydrate framework is a new tool for modeling a wide variety of saccharide and glycoconjugate structures. This report describes the development of the framework and highlights its applications. The framework integrates with established protocols within the Rosetta modeling and design suite, and it handles the vast complexity and variety of carbohydrate molecules, including branching and sugar modifications. To address challenges of sampling and scoring, RosettaCarbohydrate can sample glycosidic bonds, side-chain conformations, and ring forms, and it utilizes a glycan-specific term within its scoring function. Rosetta can work with standard PDB, GLYCAM, and GlycoWorkbench (.gws) file formats. Saccharide residue-specific chemical information is stored internally, permitting glycoengineering and design. Carbohydrate-specific applications described herein include virtual glycosylation, loop-modeling of carbohydrates, and docking of glyco-ligands to antibodies. Benchmarking data are presented and compared to other studies, demonstrating Rosetta's ability to predict glyco-ligand binding. The framework expands the tools available to glycoscientists and engineers. © 2016 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/jcc.24679 | DOI Listing |
JMIR Mhealth Uhealth
January 2025
Department of Learning and Workforce Development, The Netherlands Organisation for Applied Scientific Research, Soesterberg, Netherlands.
Background: Wearable sensor technologies, often referred to as "wearables," have seen a rapid rise in consumer interest in recent years. Initially often seen as "activity trackers," wearables have gradually expanded to also estimate sleep, stress, and physiological recovery. In occupational settings, there is a growing interest in applying this technology to promote health and well-being, especially in professions with highly demanding working conditions such as first responders.
View Article and Find Full Text PDFConfl Health
January 2025
London School of Hygiene and Tropical Medicine, Department of Non-Communicable Diseases Epidemiology, Keppel street, London, WC1E 7HT, UK.
Background: Non-communicable diseases (NCDs) are the leading cause of death globally, and many humanitarian crises occur in countries with high NCD burdens. Peer support is a promising approach to improve NCD care in these settings. However, evidence on peer support for people living with NCDs in humanitarian settings is limited.
View Article and Find Full Text PDFBMC Med Educ
January 2025
Heidelberg Institute of Global Health (HIGH), University Hospital and University of Heidelberg, Heidelberg, Germany.
Background: Research shows that trauma team formation could potentially improve effectiveness of injury care in rural settings. The aim of this study was to determine the feasibility of rural trauma team training amongst medical trainees and traffic law enforcement professionals in Uganda.
Methods: Prospective multi-centre interrupted time series analysis of an interventional training based on the 4th edition of rural trauma team development course of the American College of Surgeons.
Arch Public Health
January 2025
Laboratory Health Systemic Process (P2S), Research Unit, UR4129, University Claude Bernard Lyon 1, University of Lyon, 11 rue Guillaume Paradin, Lyon, 69008, France.
Background: According to WHO, "noncommunicable diseases (NCDs) kill 41 million people" annually, as the primary cause of death globally. WHO's Global Action Plan for the prevention and control of NCDs 2013-2020 (extended) tackles this issue and its implications regarding inequalities between countries and populations. Based on combined behavioural, environmental and policy approaches, health promotion aims to reduce health inequities and address health determinants through 3 strategies: education, prevention and protection.
View Article and Find Full Text PDFJ Cheminform
January 2025
School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, 06978, Seoul, Republic of Korea.
G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening.
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