Advancements in biotransformation pathway prediction: enhancements, datasets, and novel functionalities in enviPath.

enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system. SCIENTIFIC CONTRIBUTION: The main scientific contributions are the development of a pathway prediction model applicable to diverse chemicals, a specialized evaluation method for holistic performance assessment, and a novel applicability domain algorithm for user-specific performance estimation. The introduction of two new data sets, and the creation of links to EC classes make enviPath a unique resource in microbial biotransformation research.

Immunity against measles, mumps, rubella, and varicella among homeless individuals in Germany - A nationwide multi-center cross-sectional study.

Introduction: Homeless individuals suffer a high burden of vaccine-preventable infectious diseases. Moreover, they are particularly susceptible to adverse infection outcomes with limited access to the health care system. Data on the seroprevalence of measles, mumps, rubella, and varicella within this cohort are missing. Methods: The seroprevalence of measles, mumps, rubella, and varicella was determined within the homeless population in Germany. Predictors of lacking immune protection were determined using multivariable logistic regression analysis. Results: Homeless individuals in Germany (n = 611) showed a seroprevalence of 88.5% (95% CI: 85.8-91.0) for measles, 83.8% (95% CI: 80.6-86.6) for mumps, 86.1% (95% CI: 83.1-88.7) for rubella, and 95.7% (95% CI 93.8-97.2) for varicella. Measles seroprevalences declined from individuals born in 1965 to individuals born in 1993, with seroprevalences not compatible with a 95% threshold in individuals born after 1980. For mumps, seroprevalences declined from individuals born in 1950 to individuals born in 1984. Here, seroprevalences were not compatible with a 92% threshold for individuals born after 1975. Seronegativity for measles, mumps and rubella was associated with age but not with gender or country of origin. Discussion: Herd immunity for measles and mumps is not achieved in this homeless cohort, while there was sufficient immune protection for rubella and varicella. Declining immune protection rates in younger individuals warrant immunization campaigns also targeting marginalized groups such as homeless individuals. Given that herd immunity thresholds are not reached for individuals born after 1980 for measles, and after 1975 for mumps, vaccination campaigns should prioritize individuals within these age groups.

AdductHunter: identifying protein-metal complex adducts in mass spectra.

Mass spectrometry (MS) is an analytical technique for molecule identification that can be used for investigating protein-metal complex interactions. Once the MS data is collected, the mass spectra are usually interpreted manually to identify the adducts formed as a result of the interactions between proteins and metal-based species. However, with increasing resolution, dataset size, and species complexity, the time required to identify adducts and the error-prone nature of manual assignment have become limiting factors in MS analysis. AdductHunter is a open-source web-based analysis tool that  automates the peak identification process using constraint integer optimization to find feasible combinations of protein and fragments, and dynamic time warping to calculate the dissimilarity between the theoretical isotope pattern of a species and its experimental isotope peak distribution. Empirical evaluation on a collection of 22 unique MS datasetsshows fast and accurate identification of protein-metal complex adducts in deconvoluted mass spectra.