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Articles by Moran Chen
XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry - Nature Communications
Abstract Cross-linking mass spectrometry (XL-MS) technology plays an important role in protein structure and protein-protein interaction (PPI) studies. However, there are serious shortcomings in the data processing and analysis methods of XL-MS that limit its ability to perform in-depth analysis. Here, we built a deep learning-based XL-MS versatile platform, XL-MSDigger, to address the bottlenecks of XL-MS technology.
Clickable Multitargeting Cross-Linkers Based on SuFEx for In Vivo Cross-Linking Mass Spectrometry
Abstract Click to copy section linkSection link copied! Cross-linking mass spectrometry (XL-MS) is a powerful technique to study protein structures and protein–protein interactions. The coverage of amino acids determines the depth of the XL-MS analysis and accuracy of the structural resolution. In this work, we designed and synthesized a pair of novel trifunctional cross-linkers, succinimidyl-propargyl-aryl sulfonyl fluoride (SPSF), for multitargeting cross-linking in vivo.
Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery - Nature Communications
Abstract The discovery of highly enantioselective catalysts and elucidating their generality face great challenges due to the complex multidimensional chemical space of asymmetric catalysis and inefficient screening methods. Here, we develop a general strategy for ultra-high-throughput mapping of the chemical space of asymmetric catalysis by escaping the time-consuming chiral chromatography separation.
4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry
The python scripts and detailed data analysis methods for 4D-diaXLMS are stored on GitHub (https://github.com/Chen-micslab/4D-diaXLMS). The supplementary data and example raw data are deposited on the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) with the data set identifier PXD043282. Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions.
High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multi-Dimensional Prediction
Abstract Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is an emerging technology that has been proven to have high precursor ion sampling efficiency and higher precursor identification specificity. However, the current 4D DIA proteomics is still dependent on the building of project-specific experimental library which is time-consuming and limits the coverage for identification/quantification.
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