Fragmentation methods on the balance: unambiguous top–down mass spectrometric characterization of oxaliplatin–ubiquitin binding sites

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New Approach for the Identification of Isobaric and Isomeric Metabolites

Thomas Rizzo, Ahmed Ben Faleh, Stephan Warnke, Ali H Abikhodr, Teun Van Wieringen

The structural elucidation of metabolite molecules is important in many branches of the life sciences. However, the isomeric and isobaric complexity of metabolites makes their identification extremely challenging, and analytical standards are often require ...

2023

Using Hadamard Transform Multiplexed IR Spectroscopy Together with a Segmented Ion Trap for the Identification of Mobility-Selected Isomers

Thomas Rizzo, Ahmed Ben Faleh, Stephan Warnke, Ali H Abikhodr, Vasyl Yatsyna

The high isomeric complexity of glycans makes them particularly difficult to analyze. While ultra-high-resolution ion mobility spectrometry(IMS) can offer rapid baseline separation of many glycan isomers,their unambiguous identification remains a challengi ...

2023

Investigating the bioaccumulation potential of anionic organic compounds using a permanent rainbow trout liver cell line

Kristin Schirmer, Fabian Gerhard Peter Balk, Juliane Hollender

Permanent rainbow trout (Oncorhynchus mykiss) cell lines represent potential in vitro alternatives to experiments with fish. We here developed a method to assess the bioaccumulation potential of anionic organic compounds in fish, using the rainbow trout li ...

PERGAMON-ELSEVIER SCIENCE LTD2023

De novo designed proteins: a study in engineering novel folds and functions

Alexandra Krina Van Hall-Beauvais

Proteins control nearly every facet of life on a molecular level. Proteins are formed from linear strings of amino acids, which fold into three-dimensional structures that can enact functions. Evolution has created highly efficient proteins in diverse fold ...

EPFL2023

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics

Hamed Khakzad

Author summaryIn recent years, the application of deep learning represented a breakthrough in the mass spectrometry (MS) field by improving the assignment of the correct sequence of amino acids from observable MS spectra without prior knowledge, also known ...

PUBLIC LIBRARY SCIENCE2023

Positive Streamer Initiation in SF6/CO2 Based on Zener's Field Ionization

Guang-Yu Sun

In this study, experiments were performed to measure the inception and breakdown voltages of SF6/CO2 mixed gases with varying mixing ratios at two different pressures (1 and 2 bar). Using the preliminary data obtained from the experiments, a 3D particle mo ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

A new age in protein design empowered by deep learning

Bruno Emanuel Ferreira De Sousa Correia, Michael Bronstein, Casper Alexander Goverde, Ilia Igashov, Arne Schneuing, Hamed Khakzad

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Cell Press2023

Bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols measured in the Swiss container during MOSAiC 2019/2020

Benjamin Jérémy Laurent Heutte, Julia Schmale, Ivo Fabio Beck, Lubna Dada, Hélène Paule Angot, Imad El Haddad

This dataset contains the bulk size-resolved chemical composition and mass concentration of non-refractory submicron aerosols (NR-PM1) measured during the MOSAiC expedition from October 2019 to July 2020. These include the mass concentrations of sulfate (S ...

Zenodo2023

Plasma-Assisted Hybrid Vapour Deposition Technology for Thin Film Fabrication

Caroline Hain

Continuous development of thin film deposition technologies is essential for the fabrication of films that meet the specific requirements of their target applications and working conditions. Therefore, it is necessary to increase the number of accessible a ...

EPFL2023

Generative power of a protein language model trained on multiple sequence alignments

Umberto Lupo, Anne-Florence Raphaëlle Bitbol, Damiano Sgarbossa

Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating no ...

eLIFE SCIENCES PUBL LTD2023

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