Journal of the American Society for Mass Spectrometry最新文献

The development of matrices for Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) has traditionally relied on experimental efforts. Here, we propose a Goal-Directed artificial intelligence generative model, fueled by computational chemistry calculated data, to construct a chemical space optimized for Electron Transfer (ET) processes in MALDI analysis. We utilized a group of 30 reported ET matrices, subjected to structural enumeration and molecular properties prediction using semiempirical and ab initio calculations, to establish a comprehensive database comprising diverse structural and property data. Subsequently, employing a protocol of structural enumeration with 68 canonical SMILES of Bemis-Murcko (BM) fragments, we expanded the structural complexity of the initial library. This process generated 82753 compounds organized into 10 scaffold levels, with a p50 index from the Cyclic System Retrieval (CSR) curve of scaffolds of 50%. From the resulting enumerated library, a diverse subset of structures was selected by using the Jarvis-Patrick clustering method. These structures, along with their associated properties measured from quantum mechanics and experimental data, were used to train a Machine Learning (ML) model to predict ionization energy (E_i) values. Subsequently, a Scoring Neural Network (SNN), coupled with our Goal-Directed generative model using a Recurrent Neural Network (RNN) with Deep Learning (DL) architectures, was trained. The generative model was guided using a prior network within a Reinforcement/Transfer Learning environment. The final AI-generative model learned that structures with high unsaturation, H/C ratios under 1, and molecular weights between 100 and 300 u are favorable for ET MALDI matrices, as well as those with few aromatic rings and zero aliphatic rings. Other molecular features were also favored. The resulting AI-generated library exhibits E_i values over 8.0 eV, akin to those of reported "good" ET MALDI matrices, indicating successful design with high synthesis accessibility scores. In conclusion, our generative model provided valuable insights into the molecular features ideal for ET MALDI compounds while generating a wide range of structurally diverse molecules within a similar molecular property space. The next critical step in this process is to synthesize a selection of these generated compounds for the experimental validation and further characterization.

Hydrothermal fluid plays a crucial role in the generation and migration of hydrocarbons within sedimentary basins. Herein, we employed bulk analysis and high-resolution mass spectrometry techniques to investigate the transformation dynamics from source rock to hydrocarbons under conditions influenced by magmatic activities in the Kongdian Formation, Huanghua Depression, China. The results revealed that hydrocarbon generation in the Ek₂ shale of the study area was significantly influenced by abnormal heating from hydrothermal fluids. High temperatures associated with these fluids accelerated the conversion of organic matter within source rocks, enhancing hydrocarbon generation rates. Subsequently, the hydrocarbons migrated into fracture networks, where they solidified as low-reflectance solid bitumen, forming trapped fractures of pyrobitumen and authigenic mineral aggregates leached from thermal fluid. High aliphatic fractions were noted in the source rock extracts, while extracts from low-reflectance solid bitumen exhibited higher aromatic fraction. Aliphatic and aromatic compounds in extracts from both the low-reflectance solid bitumen and the source rock exhibited similar maturities and origins. Regarding polar compounds, the compound classes O₁, O₂, N₁O₁, and N₁O₂ showed higher abundances in source rock extracts compared to those in low-reflectance solid bitumen, while the S₁ and N₁ classes showed the opposite trend. Thus, fractionation clearly occurs when hydrocarbons expelled from source rocks by thermal fluids solidify into low-reflectance solid bitumen. This unique study provides valuable insights into understanding the fate of hydrocarbons originating from source rocks heated by thermal fluids, and explores the potential for unconventional oil in regions with intense hydrothermal alteration.

The analysis of complex mixtures poses a challenge due to the high number of compounds present in a mixture, which often exceed the capabilities of analytical methods and instruments. Even more challenging is understanding the structural details of compounds within a complex sample. Most analytical methods provide just bulk information on complex samples, and individual structural details cannot be observed. High-resolution mass spectrometry, the best method to analyze complex samples, suffers from inherent problems for structural studies in complex systems because collision-induced fragmentation (CID) measurements cannot provide data from individual compounds alone. The combination of different steps of chromatographic separation, here the combination of size exclusion chromatography with argentation chromatography, provides sufficient reduction in complexity to implement a method that allows gaining structural details of individual compounds within a complex mixture. The combination of offline size exclusion chromatography followed by online argentation chromatography effectively creates fractions based on the respective properties of the compounds in the mixture (size and number of π-bonds and heteroatoms) and reduces matrix effects to a great extent. Mass spectrometry with ultrahigh resolution provides basic chemical information for each detected compound and also provides the opportunity to gain structural information from MS/MS experiments. The results indicate effectively separated sample fractions yielded by the chromatographic steps with tremendously decreased total numbers of compounds. Especially, argentation chromatography proved to be a valuable separation tool when it comes to heteroatom-containing constituents. In the end, the fragmentation experiments indicated high-quality data due to the clean ion isolation enabled by prior separation. The structural elucidations provided deep insights into the carbon space of crude oil.

Isotope labeling of both ¹⁵N and ¹³C in selected amino acids in a protein, known as sparse labeling, is an alternative to uniform labeling and is particularly useful for proteins that must be expressed using mammalian cells, including glycoproteins. High levels of enrichment in the selected amino acids enable multidimensional heteronuclear NMR measurements of glycoprotein three-dimensional structure. Mass spectrometry provides a means to quantify the degree of enrichment. Mass spectrometric measurements of tryptic peptides of a selectively labeled glycoprotein expressed in HEK293 cells revealed complicated isotope patterns which consisted of many overlapping isotope patterns from intermediately labeled peptides, which complicates the determination of the label incorporation. Two challenges are uncovered by these measurements. Metabolic scrambling of amino groups can reduce the ¹⁵N content of enriched amino acids or increase the ¹⁵N in nontarget amino acids. Also, undefined, unlabeled medium components may dilute the enrichment level of labeled amino acids. The impact of this unexpected metabolic scrambling was overcome by simulating isotope patterns for all isotope-labeled peptide states and generating linear combinations to fit to the data. This method has been used to determine the percent incorporation of ¹⁵N and ¹³C labels and has identified several metabolic scrambling effects that were previously undetected in NMR experiments. Ultrahigh mass resolution is also utilized to obtain isotopic fine structure, from which enrichment levels of ¹⁵N and ¹³C can be assigned unequivocally. Finally, tandem mass spectrometry can be used to confirm the location of heavy isotope labels in the peptides.

Dissolved organic matter (DOM) is a complex mixture of thousands of molecular formulas comprised of an unknown number of chemical compounds, the concentration and composition of which are critical to ecosystem function and biogeochemical cycling. Despite its importance, our understanding of the DOM composition is lacking. This is principally due to its molecular complexity, which means that no single method is capable of describing DOM in its entirety. Quantification is typically done by proxy (e.g., relative to carbon content) and does not necessarily match well to compositional data, due to incomplete analytical windows and selectivity of different analytical methods. We present an integrated liquid chromatography (LC)-diode array detector (DAD)-charged aerosol detector (CAD)-mass spectrometry (MS) pipeline designed to both characterize and quantify solid-phase extractable DOM (SPE-DOM) in a single analysis. We applied this method to a set of eight Swedish water bodies sampled in the summer and winter. Chromophoric SPE-DOM was proportionally higher in samples with higher SPE-DOM concentrations but remained relatively consistent between sampling occasions. Ionizable SPE-DOM was relatively consistent across sites but was proportionally higher in summer. Overall, the carbon content of DOM was very consistently ∼40% across sites in both summer and winter. These findings suggest that SPE-DOM concentration at these sites is driven by (presumably allochthonous) chromophoric inputs, with an increased relative contribution in summer of material that is more ionizable and less chromophoric and may be either autochthonous or selectively enriched from allochthonous sources. Thus, with minimal additional effort, this method provided further compositional insights not attained by any single analysis in isolation.

A rapid analysis method for determination of hydrocarbon types in aviation turbine fuel was investigated in this study. A kind of reversible adsorption material packed as an unsaturated trap was used to separate saturated hydrocarbons and unsaturated hydrocarbons in the GC-MS system. No manual process or organic reagent was needed during the entire analysis process. The contents of 13 kinds of hydrocarbons, including paraffins, monocycloparaffins, dicycloparaffins, tricycloparaffins, monoolefins, cycloolefins, alkylbenzenes, C_nH_2n-8 aromatics, C_nH_2n-10 aromatics, naphthalenes, C_nH_2n-14 aromatics, C_nH_2n-16 aromatics, and C_nH_2n-18 aromatics were calculated by the characteristic mass fragments detected by MS with the modified matrices of ASTM D2425. The overall analysis time was less than 10 min. The precision of this test method has been conducted with 8 laboratories attended and 16 samples analyzed. The performance of this new method was demonstrated by comparing the results tested by ASTM D1319, ASTM D2425, and ASTM D6379. This rapid analysis method can provide hydrocarbon compositions of aviation turbine fuels or other liquid hydrocarbon samples within the same distillation range.

Fragmentation is a phenomenon ubiquitously observed during research and development of therapeutic antibodies. The clear identification of the cleavage site is vital for comprehending fragmentation mechanisms and optimizing processes. Capillary electrophoresis-sodium dodecyl sulfate (CE-SDS) is now widely used to detect and quantify fragments, while its peak identification is hindered by immature capillary electrophoresis-sodium dodecyl sulfate coupled with mass spectrometry techniques. In this study, we developed a systematic workflow for fragment characterization, which integrated direct identification, fragment enrichment, and fragmentation confirmation using multiple techniques, such as microscale peptide mapping (PM), PM of N-termini labeled sample, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) in-gel extraction for molecular weight (MW) and PM measurements. By employing this innovative workflow, we successfully identified the cleavage sites of two therapeutic antibodies. In the first case, through direct liquid chromatography-mass spectrometry (LC-MS) characterization, the cleavages leading to the loss of biological function were identified on the linker of a bispecific antibody. In the second case involving a tetravalent antibody, direct LC-MS analysis failed. However, more sophisticated analysis nailed down the critical cleavage at the LC/HC: G₁₀₅-R₁₀₆ site in the CDR3 region of the antibody. Our systematic workflow provides a clear and accessible method for identifying cleavage sites with broad applicability across pharmaceutical laboratories.

We introduce vacuum resonance-enhanced multiphoton ionization (REMPI) with high-resolution Orbitrap Fourier transform mass spectrometry (FTMS) for analyzing silylated polar compounds. UV laser radiation at 248 nm sensitively and selectively targets aromatic constituents, while high-resolution mass spectrometry (HRMS) enables high-performance mass spectrometric detection. This workflow enhances the detection confidence of polar constituents by identifying unique isotopologue patterns, including at the isotopic fine structure (IFS) level, in analytical standards and complex bio-oils. A direct and derivatized gas chromatography (GC) procedure on a polar standard component mixture allowed us to explore the general ionization and detection characteristics of REMPI FTMS. HRMS enabled the examination of the complex isotopologue profiles, revealing distinct patterns for the CHO_xSi_y-class compounds. Particularly in complex mixtures, this isobaric/isonucleonic complexity exceeded the classical mass resolution capabilities of the employed Orbitrap D30 series and prompted the usage of prolonged transients via an external data acquisition system. This procedure substantially improved mass spectrometric results by recording the unreduced time-domain transient data for up to 2 s. Notably, the ability to distinguish diagnostic isotopic pairs, such as ¹²C/²⁹Si vs ¹³C/²⁸Si with a mass split of ∼3.79 mDa and ¹³C¹²C/²⁸Si²⁹Si vs ¹³C₂/²⁸Si₂, with an approximate mass difference of ∼3.32 mDa, demonstrates a significant and expected performance improvement. Finally, we benchmark the GC HRMS methodology to identify silylated oxygenated and nitrogen-containing constituents in ultracomplex bio-oil samples. The presented approach of utilizing the silicon isotope pattern and unique isotopologue mass splits for increasing attribution confidence can be applied beyond bio-oils toward the general GC analyses of polar oxygenates.

Protein footprinting is a useful method for studying protein higher order structure and conformational changes induced by interactions with various ligands via addition of covalent modifications onto the protein. Compared to other methods that provide single amino acid-level structural resolution, such as cryo-EM, X-ray diffraction, and NMR, mass spectrometry (MS)-based methods can be advantageous as they require lower protein amounts and purity. As with other MS-based proteomic methods, such as post-translational modification analysis, enrichment techniques have proven necessary for both optimal sensitivity and sequence coverage when analyzing highly complex proteomes. Currently used reagents for footprinting via covalent labeling, such as hydroxyl radicals and carbodiimide-based methods, do not yet have a suitable enrichment method, limiting their applicability to whole proteome analysis. Here, we report a method for enrichable covalent labeling built upon the GEE/EDC system commonly used to covalently label aspartic acid and glutamic acid residues. Novel labeling reagents containing alkynyl functionality can be "clicked" to any azido-containing molecule with copper-catalyzed azide-alkyne cycloaddition (CuAAC), allowing for enrichment or further labeling. Multiple azide- and alkyne-containing GEE-like molecules were tested, and the most efficient method was determined to be the EDC-facilitated coupling of glycine propargyl amide (GPA) to proteins. The pipeline we report includes clicking via CuAAC to a commercially available biotin-azide containing a photocleavable linker, followed by enrichment using a streptavidin resin and subsequent cleavage under ultraviolet light. The enrichment process was optimized through the screening of clickable amines, coupling reagents, and enrichment scaffolds and methods with pure model proteins and has also been applied to complex mixtures of proteins isolated from the model plant, Arabidopsis thaliana, suggesting that our method may ultimately be used to measure protein conformation on a proteomic scale.