Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Unlabelled: xiNET is a visualization tool for exploring cross-linking/mass spectrometry results. The interactive maps of the cross-link network that it generates are a type of node-link diagram. In these maps xiNET displays: (1) residue resolution positional information including linkage sites and linked peptides; (2) all types of cross-linking reaction product; (3) ambiguous results; and, (4) additional sequence information such as domains. xiNET runs in a browser and exports vector graphics which can be edited in common drawing packages to create publication quality figures.
Availability: xiNET is open source, released under the Apache version 2 license. Results can be viewed by uploading data to http://crosslinkviewer.org/ or by downloading the software from http://github.com/colin-combe/crosslink-viewer and running it locally.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4390258 | PMC |
| http://dx.doi.org/10.1074/mcp.O114.042259 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Tunable Mechanical and Dynamic Properties via Cross-Linker Length in Bis(hindered amino)Disulfide-Based Covalent Adaptable Networks.
Macromol Rapid Commun
September 2025
Department of Chemical Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, Meguro-ku, Tokyo, Japan.
Conventional cross-linked polymers are not recyclable or reprocessable due to the formation of permanent cross-links. Covalent adaptable networks (CANs) based on dynamic covalent chemistry attract great attention as materials that exhibit excellent stress relaxation, recyclability, reprocessability, and self-healing properties. Controlling the dynamic properties of CANs is important for both fundamental science and practical applications.
View Article and Find Full Text PDFPropylene-Ethylene Copolymer Covalent Adaptable Networks Synthesized by Resonance-Stabilized, Radical-Based Reactive Processing with Excellent Elevated-Temperature Creep Resistance.
ChemSusChem
August 2025
Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA.
Low-crystallinity propylene-ethylene copolymer (PEC) thermoplastics exhibit creep in the melt and semicrystalline states. To enhance creep resistance while maintaining reprocessability, dynamic covalent cross-links are introduced through one-step, radical-based reactive processing to create covalent adaptable networks (CANs). During reactive processing, it is essential to suppress β-scission of propylene repeat units.
View Article and Find Full Text PDFAIRPred: A Deep Learning Model Predictor for Peptide Intensity Ratios in Cross-Linking Mass Spectrometry Improves Cross-Link Spectrum Matching.
Anal Chem
August 2025
Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP), Berlin 13125, Germany.
Cross-linking mass spectrometry (XL-MS) is a powerful tool in structural proteomics, offering insights into protein conformations, interactions and dynamics by linking spatially proximal residues. However, current cross-linked spectrum match (CSM) scoring methods rely heavily on mass-to-charge ratio (/) comparisons, often neglecting fragment ion intensity information, which limits their ability to accurately distinguish true CSMs from false positives. To overcome this limitation, we present AIRPred, a deep learning model that predicts intensity ratios between cross-linked peptide pairs to improve CSM identification.
View Article and Find Full Text PDFCommunication resource allocation method in vehicular networks based on federated multi-agent deep reinforcement learning.
Sci Rep
August 2025
Key Laboratory of Communication and Network, Dalian University, Dalian, 116622, China.
In highly dynamic vehicular networking scenarios, when Vehicle-to-Infrastructure links and Vehicle-to-Vehicle links share spectrum resources, the traditional distributed resource allocation method lacks global optimization and fails to respond to environmental changes in a timely manner, which leads to low spectral efficiency of the system. A resource allocation method based on federated multi-agent deep reinforcement learning is proposed for Vehicular Networking communication, by fusing Asynchronous Federated Learning (AFL) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Synergistic optimization of resource allocation.
View Article and Find Full Text PDFReprocessable and Highly Creep-Resistant Covalent Adaptable Networks Incorporating Azine Dynamic Cross-Links via Free-Radical Polymerization.
ACS Macro Lett
August 2025
Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States.
Covalent adaptable networks (CANs) are a promising avenue for replacing conventional, unrecyclable thermosets with reprocessable, more sustainable networks incorporating dynamic cross-links. Azine dynamic chemistry has recently been explored and, thus far, has only been incorporated into step-growth CANs. We developed an azine-based cross-linker with methacrylate end groups, enabling radical-based CAN synthesis.
View Article and Find Full Text PDF