Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry

Summary: Hydrogen deuterium exchange mass spectrometry (HDX-MS) is becoming increasing routine for monitoring changes in the structural dynamics of proteins. Differential HDX-MS allows comparison of individual protein states, such as in the absence or presence of a ligand. This can be used to attribute changes in conformation to binding events, allowing the mapping of entire con-formational networks. As such, the number of necessary cross-state comparisons quickly increas-es as additional states are introduced to the system of study. There are currently very few software packages available that offer quick and informative comparison of HDX-MS datasets and even few-er which offer statistical analysis and advanced visualization. Following the feedback from our origi-nal software Deuteros, we present Deuteros 2.0 which has been redesigned from the ground up to fulfil a greater role in the HDX-MS analysis pipeline. Deuteros 2.0 features a repertoire of facilities for back exchange correction, data summarization, peptide-level statistical analysis and advanced data plotting features. Availability: Deuteros 2.0 can be downloaded from https://github.com/andymlau/Deuteros_2.0 under the Apache 2.0 license. Installation of Deuteros 2.0 requires the MATLAB Runtime Library available free of charge from MathWorks (https://www.mathworks.com/products/compiler/matlab-runtime.html) and is available for both Windows and Mac operating systems.Comment: Application note with 3 pages, 1 figur

Soil microbial community structure and functionality changes in response to long-term metal and radionuclide pollution

Microbial communities are essential for a healthy soil ecosystem. Metals and radionuclides can exert a persistent pressure on the soil microbial community. However, little is known on the effect of long-term co-contamination of metals and radionuclides on the microbial community structure and functionality. We investigated the impact of historical discharges of the phosphate and nuclear industry on the microbial community in the Grote Nete river basin in Belgium. Eight locations were sampled along a transect to the river edge and one location further in the field. Chemical analysis demonstrated a metal and radionuclide contamination gradient and revealed a distinct clustering of the locations based on all metadata. Moreover, a relation between the chemical parameters and the bacterial community structure was demonstrated. Although no difference in biomass was observed between locations, cultivation-dependent experiments showed that communities from contaminated locations survived better on singular metals than communities from control locations. Furthermore, nitrification, a key soil ecosystem process seemed affected in contaminated locations when combining metadata with microbial profiling. These results indicate that long-term metal and radionuclide pollution impacts the microbial community structure and functionality and provides important fundamental insights into microbial community dynamics in co-metal-radionuclide contaminated sites

Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution

Rationale: The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry–based proteomics. Sulphur atoms have a very distinctive elemental isotope definition, and therefore, the presence of sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Hence, knowledge of the number of sulphur atoms can improve the identification of peptides and proteins. Methods: In this paper, we conducted a theoretical investigation on the isotope properties of sulphur-containing peptides. We proposed a gradient boosting approach to predict the number of sulphur atoms based on the aggregated isotope distribution. We compared prediction accuracy and assessed the predictive power of the features using the mass and isotope abundance information from the first three, five and eight aggregated isotope peaks. Results: Mass features alone are not sufficient to accurately predict the number of sulphur atoms. However, we reach near-perfect prediction when we include isotope abundance features. The abundance ratios of the eighth and the seventh, the fifth and the fourth, and the third and the second aggregated isotope peaks are the most important abundance features. The mass difference between the eighth, the fifth or the third aggregated isotope peaks and the monoisotopic peak are the most predictive mass features. Conclusions: Based on the validation analysis it can be concluded that the prediction of the number of sulphur atoms based on the isotope profile fails, because the isotope ratios are not measured accurately. These results indicate that it is valuable for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher-order isotope peaks more accurately