Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis.

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).

Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics.

Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.

Correction: Total synthesis and chemical stability of pseudouridimycin.

Correction for 'Total synthesis and chemical stability of pseudouridimycin' by Christopher F. Cain et al., Chem. Commun., 2022, DOI: 10.1039/d1cc07059b.

Total synthesis and chemical stability of pseudouridimycin.

We report the chemical synthesis of pseudouridimycin (1), an antimicrobial natural product that potently and selectively inhibits bacterial RNA polymerase. Chemical stability studies revealed intramolecular hydroxamate bond scission to be a major decomposition pathway for 1 in aqueous buffer. Replacement of the hydroxamate bond with a tertiary amide, as in 16, afforded a conformational isostere resistant to degradation. These studies pave the way for the design and synthesis of analogues with improved chemical stability and biological activity.