AIMC Topic: Mass Spectrometry

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Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy.

International journal of molecular sciences Apr 26, 2023
Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions an...
Proteomics Mass Spectrometry Deep Learning Protein Subunits Machine Learning
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Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization.

Current opinion in chemical biology Apr 16, 2023
The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) h...
Mass Spectrometry Proteins Artificial Intelligence
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MS2Query: reliable and scalable MS mass spectra-based analogue search.

Nature communications Mar 29, 2023
Metabolomics-driven discoveries of biological samples remain hampered by the grand challenge of metabolite annotation and identification. Only few metabolites have an annotated spectrum in spectral libraries; hence, searching only for exact library m...
Machine Learning Reproducibility of Results Metabolomics Complex Mixtures Mass Spectrometry
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Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning.

Nature biotechnology Mar 20, 2023
While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of...
Proteins Protein Conformation Deep Learning Mass Spectrometry Algorithms
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Protein complexes in cells by AI-assisted structural proteomics.

Molecular systems biology Feb 23, 2023
Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate-based approach to systematically model novel protein assemblies. Here, we use ...
Proteins Artificial Intelligence Mass Spectrometry Proteomics Molecular Biology
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Toward an Integrated Machine Learning Model of a Proteomics Experiment.

Journal of proteome research Feb 6, 2023
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluat...
Mass Spectrometry Machine Learning Proteomics Algorithms
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Retention time prediction for small samples based on integrating molecular representations and adaptive network.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences Feb 4, 2023
Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data...
Mass Spectrometry Chromatography Machine Learning Neural Networks, Computer
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Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry.

Journal of proteome research Jan 25, 2023
Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states ...
Brain Mass Spectrometry Single-Cell Analysis Machine Learning Workflow
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Deep learning-based method for automatic resolution of gas chromatography-mass spectrometry data from complex samples.

Journal of chromatography. A Dec 29, 2022
Modern gas chromatography-mass spectrometry (GC-MS) is the workhorse for the high-throughput profiling of volatile compounds in complex samples. It can produce a considerable amount of two-dimensional data, and automatic methods are required to disti...
Mass Spectrometry Gas Chromatography-Mass Spectrometry Plant Oils Deep Learning Neural Networks, Computer Oils, Volatile
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Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.

International journal of molecular sciences Dec 16, 2022
Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeuti...
Lignin Non-alcoholic Fatty Liver Disease Molecular Docking Simulation Proteomics Mass Spectrometry Humans Deep Learning Polyphenols
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