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These data are pure standard mass spectral libraries were collected on Orbitrap (Thermo QE-HF) and Q-TOF (SCIEX 5600) mass spectrometers for 192 compounds using 6 different collision energies, respectively. Libraries were acquired while using different solvent additives, precursor adducts, and ion polarities.
The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization. This package contains soil metaproteomics data in the context of site specific metagenomes from soil depth profiles in three paired control and warming plots from a temperate mixed forest in Northern California. Each paired plot had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. These metaproteomes were collected in 2018 after 4.5 years of warming from five depth intervals (0-10 cm, 10-30 cm, 30-45 cm, 45-60 cm, 60-80 cm). For protein identification, the collected spectra were searched following a target-decoy search strategy against a database of metagenome predicted proteins (covering 96 samples from 2014 to 2021) representing the complete sequence diversity at the site. Data was searched with mass spectrometry database search tool (MS-GF+) using Pacific Northwest National Laboratory (PNNL)'s Data Management System (DMS) Processing pipeline. The metagenomes are published as part of another data package. Raw metaproteomic data and the data products from MS-GF+ are deposited in the Mass Spectrometry Interactive Virtual Environment (MassIVE) database under accession no. MSV000097826. Here we present a dataset that includes spectral counts for the detected proteins across samples (EMSL50964_BrodieAllMAGs_Globals_SC.txt), the sequences of the detected proteins, and sample metadata file that contains site information for the soil metaproteome samples.
When polymer mixtures become increasingly complex, the conventional analysis techniques become insufficient for complete characterization. Mass spectrometric techniques can satisfy this increasing demand for detailed sample characterization. Even though isobaric polymers are indistinguishable using simple mass spectrometry (MS) analyses, more advanced techniques such as tandem MS (MS/MS) or ion mobility (IM) can be used. Here, we report proof of concept for characterizing isomeric polymers, namely poly(2-n-propyl-2-oxazoline) (Pn-PrOx) and poly(2-isopropyl-2-oxazoline) (Pi-PrOx), using MS/MS and IM-MS. Pi-PrOx ions lose in intensity at higher accelerating voltages than Pn-PrOx ions during collision-induced dissociation (CID) MS/MS experiments. A Pn/i-PrOx mixture could also be titrated using survival yield calculations of either precursor ions or cation ejection species. IM-MS yielded shape differences in the degree of polymerization (DP) regions showing the structural rearrangements. Combined MS techniques are thus able to identify and deconvolute the molar mass distributions of the two isomers in a mixture. Finally, the MS/MS and IM-MS behaviors are compared for interpretation. .
In metabolomics, tandem MS (MS2) fragmentation libraries are important for the identification of unknown features, but generating these libraries takes many valuable hours of instrument and operator time. Here, an immediate droplet-on-demand/open port sampling interface was used to rapidly acquire tandem MS of standards arrayed in a 96-well plate format. A workflow was developed for automated, high-throughput control of MS2 library generation. Pure standard mass spectral libraries were collected on Orbitrap and Q-TOF mass spectrometers for 192 compounds using 6 different collision energies with a throughput of 4 and 7.8 s/spectrum, respectively. Libraries were acquired using different solvent additives, precursor adducts, and ion polarities.
We demonstrate increasing the charge state of small molecules using derivatized lysine as our model system. Lysine is chemically tagged with three tertiary amines which enables efficient production of highly charged analytes. A +3 charge state is obtained from direct infusion nanoelectrospray conditions. Collisional activation of the +3 derivatized lysine yielded structurally informative product ions corresponding to cleavages across the analyte backbone and within the proton affinity tags. This suggests a role for multi-charging of metabolites in both targeted MRM analyses and untargeted analyses to help identify novel metabolites. Density functional calculations aid peak assignment and rationalization of structure-property relationships. .
A comprehensive understanding of when and how 5f orbitals participate in complex chemical bonding is important for a variety of applications. The actinides are unique in that they possess 5f orbitals and can access high oxidation states, which make them attractive for use in catalysis. Fundamental studies of actinide–ligand interactions offer a mechanism to examine the activation of the 5f orbitals so that the selectivity of 5f orbitals can be assessed. A previous study examined the reaction of Pu + + CO 2 and determined that the reaction efficiency is restricted by a barrier, namely, promotion from the Pu + ground-state configuration, 5f 6 7s, to a reactive-state configuration, 5f 5 6d 2 . Here, the present study illustrates the benefit of activation of Pu’s 5f orbitals when studying the reaction of Pu + + NO. In this reaction, PuO + forms in an exothermic, barrierless process. The 5f orbitals can and do participate in forming a linear intermediate, [N-Pu-O] + , and this drives the exothermic reaction. Understanding the conditions under which 5f orbitals are active in chemical bonding is the key to exploiting the actinides’ selective catalytic capabilities.
The reaction rates of lanthanide and actinide cations with CO 2 are dictated by the crossing between the potential energy surface (PES) evolving from the ground state reactants (red) and the PES leading to the ground state products (green).
Lanthanide reactivity is limited by the energy required to access an electronic configuration with two unpaired non-f electrons.
We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.