Designed allosteric protein logic.

The regulation of protein function by external or internal signals is one of the key features of living organisms. The ability to directly control the function of a selected protein would represent a valuable tool for regulating biological processes. Here, we present a generally applicable regulation of proteins called INSRTR, based on inserting a peptide into a loop of a target protein that retains its function. We demonstrate the versatility and robustness of coiled-coil-mediated regulation, which enables designs for either inactivation or activation of selected protein functions, and implementation of two-input logic functions with rapid response in mammalian cells. The selection of insertion positions in tested proteins was facilitated by using a predictive machine learning model. We showcase the robustness of the INSRTR strategy on proteins with diverse folds and biological functions, including enzymes, signaling mediators, DNA binders, transcriptional regulators, reporters, and antibody domains implemented as chimeric antigen receptors in T cells. Our findings highlight the potential of INSRTR as a powerful tool for precise control of protein function, advancing our understanding of biological processes and developing biotechnological and therapeutic interventions.

The Impact of COVID-19 on Multidrug-Resistant Bacteria at a Slovenian Tertiary Medical Center.

The COVID-19 pandemic has strained healthcare systems globally. Shortages of hospital beds, reassignment of healthcare workers to COVID-19-dedicated wards, an increased workload, and evolving infection prevention and control measures have potentially contributed to the spread of multidrug-resistant bacteria (MDRB). To determine the impact of the COVID-19 pandemic at the University Medical Center Ljubljana, a tertiary teaching hospital, we analyzed the monthly incidence of select bacterial species per patient from 2018 to 2022. The analysis was performed for all isolates and for MDRB isolates. The data were analyzed separately for isolates from all clinical samples, from blood culture only, and from clinical and surveillance samples. Our findings revealed an increased incidence density of patients with Enterococcus faecium, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa isolates from clinical samples during the COVID-19 period in the studied hospital. Notably, the incidence density of MDRB isolates-vancomycin-resistant E. faecium, extended-spectrum betalactamase-producing K. pneumoniae, and betalactam-resistant P. aeruginosa-from clinical samples increased during the COVID-19 period. There were no statistically significant differences in the incidence density of patients with blood culture MDRB isolates. We observed an increase in the overall MDRB burden (patients with MDRB isolates from both clinical and surveillance samples per 1000 patient days) in the COVID-19 period in the studied hospital for vancomycin-resistant E. faecium, carbapenem-resistant K. pneumoniae, and betalactam-resistant P. aeruginosa and a decrease in the methicillin-resistant S. aureus burden.

GC-EI-MS datasets of trimethylsilyl (TMS) and tert-butyl dimethyl silyl (TBDMS) derivatives for development of machine learning-based compound identification approaches.

In the field of environment and health studies, recent trends have focused on the identification of contaminants of emerging concern (CEC). This is a complex, challenging task, as resources, such as compound databases (DBs) and mass spectral libraries (MSLs) concerning these compounds are very poor. This is particularly true for semi polar organic contaminants that have to be derivatized prior to gas chromatography-mass spectrometry (GC-MS) analysis with electron impact ionization (EI), for which it is barely possible to find any records. In particular, there is a severe lack of datasets of GC-EI-MS spectra generated and made publicly available for the purpose of development, validation and performance evaluation of cheminformatics-assisted compound structure identification (CSI) approaches, including novel cutting-edge machine learning (ML)-based approaches [1]. We set out to fill this gap and support the machine learning-assisted compound identification, thus aiding cheminformatics-assisted identification of silylated derivatives in GC-MS laboratories working in the field of environment and health. To this end, we have generated 12 datasets of GC-EI-MS spectra, six of which contain GC-EI-MS spectra of trimethylsilyl (TMS) and six GC-EI-MS spectra of tert-butyldimethylsilyl (TBDMS) derivatives. Four of these datasets, named testing datasets, contain mass spectra acquired by the authors. They are available in full, together with corresponding metadata. Eight datasets, named training datasets, were derived from mass spectra in the NIST 17 Mass Spectral Library. For these, we have only made the metadata publicly available, due to licensing reasons. For each type of derivative, two testing datasets are generated by acquiring and processing GC-EI-MS spectra, such that they include raw and processed GC-EI-MS spectra of TMS and TBDMS derivatives of CECs, along with their corresponding metadata. The metadata contains IUPAC name, exact mass, molecular formula, InChI, InChIKey, SMILES and PubChemID, of each CEC and CEC-TMS or CEC-TBDMS derivative, where available. Eight GC-EI-MS training datasets are generated by using the National Institute of Standards and Technology (NIST)/U.S. Environmental Protection Agency (EPA)/National Institute of Health (NIH) 17 Mass Spectral Library. For each derivative type (TMS and TBDMS), four datasets are given, each corresponding to an original dataset obtained from NIST/EPA/NIH 17 and three variants thereof, obtained after each of the filtering steps of the procedure described below. Only the metadata about the training datasets are available, describing the corresponding NIST/EPA/NIH 17 entires: These include the compound name, CAS Registry number, InChIKey, exact mass, Mw, NIST number and ID number. The datasets we present here were used to train and test predictive models for identification of silylated derivatives built with ML approaches [4]. The models were built by using data curated from the NIST Mass Spectral Library 17 [2] and the machine learning approach of CSI:Output Kernel Regression (CSI:OKR) [2]. Data from the NIST Mass Spectral Library 17 are commercially available from the National Institute of Standards and Technology (NIST)/U.S. Environmental Protection Agency (EPA)/National Institute of Health (NIH) and thus cannot be made publicly available. This highlights the need for publicly available GC-EI-MS spectra, which we address by releasing in full the four testing datasets.