ACS Publications最新文献

The small ubiquitin-like modifier (SUMO) is an important post-translational modification that regulates the function of various proteins essential for DNA damage repair, genome integrity, and cell homeostasis. To identify protein SUMOylation effectively, an enrichment step is necessary, often requiring exogenous gene expression in cells and immunoaffinity purification of SUMO-remnant peptides following tryptic digestion. Previously, an antibody was developed to enrich tryptic peptides containing the remnant NQTGG on the receptor lysine, although the specifics of the structural interaction motif remained unclear. This study integrates de novo sequencing, intact mass spectrometry, cross-linking mass spectrometry, and molecular docking to elucidate the structural interaction motifs of a SUMO-remnant antibody. Additional cross-linking experiments were performed using SUMOylated peptides and high-field asymmetric waveform ion mobility spectrometry (FAIMS) to enhance the sensitivity and confirm interactions at the paratope interface. This study establishes a robust framework for characterizing antibody-antigen interactions, offering valuable insights into the structural basis of SUMO-remnant peptide recognition.

Coumarin skeletons are significant structural units frequently used in the fields of synthetic chemistry, medicinal chemistry, and materials science. Herein, we report a hemilabile P,N-ligand-assisted gold-catalyzed difunctionalization of activated alkynes with organohalides. The reaction occurs effectively under mild conditions without requiring an external oxidant, producing a variety of 3-arylated and alkenylated coumarin derivatives in high to excellent yields. This method demonstrates a wide substrate tolerance and superb functional group compatibility and is also suitable for heteroaromatic substrates. Further mechanistic investigations strongly support the proposed mechanism of the reaction.

General proteomics research for fundamental science typically addresses laboratory- or patient-derived samples of known origin and composition. However, in a few research areas, such as environmental proteomics, clinical identification of infectious organisms, archeology, art/cultural history, and forensics, attributing the origin of a protein-containing sample to the organisms that produced it is a central focus. A small number of groups have approached this problem and developed software tools for taxonomic characterization and/or identification using bottom-up proteomics. Most such tools identify peptides via database search, and many rely on organism-specific peptides as markers. Our group recently introduced MARLOWE, a software tool for taxonomic characterization of unknown samples based on de novo peptide identification and signal-erosion-resistant strong peptides, which are shared peptides distributed in a taxonomy-dependent manner. In the current work, we further characterize the utility of MARLOWE using publicly available proteomics data from forensically-relevant samples. MARLOWE characterizes samples based on their protein profile, and returns ranked organism lists of potential contributors and taxonomic scores based on shared strong peptides between organisms. Overall, the correct characterization rate ranges between 44 and 100%, depending on the sample type and data acquisition parameters (with lower numbers associated with lower-quality data sets). MARLOWE demonstrates successful characterization of true contributors and close relatives, and provides sufficient specificity to distinguish certain microbial species. MARLOWE demonstrates its ability to provide insight into potential taxonomic sources for a wide range of sample types without prior assumptions about sample contents. This approach can find utility in forensic science and also broadly in bioanalytical applications that utilize proteomics approaches for taxonomic characterization.

ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics data. The application comprises four primary sections designed to address various aspects of mass spectrometry data analysis in a single platform, including label-free and labeled samples (SILAC/iTRAQ/TMT), differential expression analysis, and data visualization. ProteoArk supports postprocessing of Proteome Discoverer, MaxQuant, and MSFragger search results. The tool also includes functional enrichment analyses such as gene ontology, protein-protein interactions, pathway analysis, and differential expression analysis, which incorporate various statistical tests. By streamlining workflows and developing user-friendly interfaces, we created a robust and accessible solution for users with basic bioinformatics skills in proteomic data analysis. Users can easily create manuscript-ready figures with a single click, including principal component analysis, heatmaps (K-means and hierarchical), MA plots, volcano plots, and circular bar plots. ProteoArk is developed using the Django framework and is freely available for users [https://ciods.in/proteoark/]. Users can also download and run the standalone version of ProteoArk using Docker as described in the instructions [https://ciods.in/proteoark/dockerpage]. The application code, input data, and documentation are available online at https://github.com/ArokiaRex/proteoark. A tutorial video is available on YouTube: https://www.youtube.com/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B.

Advancing MS-based proteomics toward clinical applications evolves around developing standardized start-to-finish and fit-for-purpose workflows for clinical specimens. Steps along the method design involve the determination and optimization of several bioanalytical parameters such as selectivity, sensitivity, accuracy, and precision. In a joint effort, eight proteomics laboratories belonging to the MSCoreSys initiative including the CLINSPECT-M, MSTARS, DIASyM, and SMART-CARE consortia performed a longitudinal round-robin study to assess the analysis performance of plasma and serum as clinically relevant samples. A variety of LC-MS/MS setups including mass spectrometer models from ThermoFisher and Bruker as well as LC systems from ThermoFisher, Evosep, and Waters Corporation were used in this study. As key performance indicators, sensitivity, precision, and reproducibility were monitored over time. Protein identifications range between 300 and 400 IDs across different state-of-the-art MS instruments, with timsTOF Pro, Orbitrap Exploris 480, and Q Exactive HF-X being among the top performers. Overall, 71 proteins are reproducibly detectable in all setups in both serum and plasma samples, and 22 of these proteins are FDA-approved biomarkers, which are reproducibly quantified (CV < 20% with label-free quantification). In total, the round-robin study highlights a promising baseline for bringing MS-based measurements of serum and plasma samples closer to clinical utility.

We introduce a novel grouped-bath ansatz that approximates the spin-flip nonorthogonal configuration interaction (SF-NOCI) ansatz, named SF-GNOCI, which significantly reduces computational cost while preserving accuracy. SF-NOCI, originally developed by Mayhall et al., is a robust and nearly "black-box" electronic structure theory well suited for studying charge-transfer phenomena. It captures orbital relaxation effects for all configurations within the active space, providing a balanced correlation among charge transfer and other states. However, including these relaxation effects for all configurations results in a sharp increase in computational cost, especially for the large active spaces commonly encountered in transition metal complexes. To overcome this challenge, we grouped configurations based on the number of electrons associated with each atom. Configurations within each group share a common set of bath orbitals, significantly reducing computational overhead. We demonstrate the performance of SF-GNOCI through benchmark calculations on two systems: the avoided crossing of the lowest singlet states in LiF dissociation and the low-lying charge transfer states of [Fe(SCH₃)₄]^2-/1-. Our results show that SF-GNOCI achieves accuracy comparable to standard SF-NOCI while reducing computational cost by a factor of 10 for ${\left[Fe{\left({SCH}_3\right)}_4\right]}^{2-}$ and 15 for ${\left[Fe{\left({SCH}_3\right)}_4\right]}^{1-}$. We believe that the SF-GNOCI ansatz is a promising reference state for efficiently describing charge transfer phenomena in transition metal complexes.

This study investigates the thermodynamic parameters of 1300 RNA/DNA hybrid duplexes, including both natural and chemically modified forms, using molecular dynamics (MD) simulations. Modified duplexes consist of phosphorothioate (PS) backbones and 2'-O-methoxyethyl (MOE) modifications, both commonly used in therapeutic oligonucleotides. Hybridization enthalpy and entropy were calculated from MD trajectories using molecular mechanics Poisson-Boltzmann surface area (MMPBSA) and molecular mechanics generalized Born surface area (MMGBSA) approaches. To address discrepancies with experimental data, we established empirical relationships by comparing calculated values with known experimental results of natural hybrid duplexes, then extended these relationships to the entire data set. The corrected parameters were subsequently used to generate nearest-neighbor (NN) models, allowing for experimentally reliable melting temperature predictions. In this process, MMGBSA demonstrated superior predictive performance with high convergence and consistency for both natural and modified duplexes. Specifically, MMGBSA captured the stabilizing effects of the MOE modification with minimal bias, while MMPBSA exhibited greater variability and limited reliability. These findings highlight the potential of MMGBSA for accurate thermodynamic modeling of both natural and modified nucleic acids, providing a robust framework and experimentally meaningful insights for applications in nucleic acid-based therapeutic design and biotechnology.

Top-down proteomics is the study of intact proteins and their post-translational modifications with mass spectrometry. Historically, this field is more challenging than its bottom-up counterpart because the species are much bigger and have a larger number of possible combinations of sequences and modifications; thus, there is a great need for technological development. With improvements in instrumentation and a multiplicity of fragmentation modes available, top-down proteomics is quickly gaining in popularity with renewed attention on increasing confidence in identification and quantification. Here, we systematically evaluated the Sciex ZenoTOF 7600 system for top-down proteomics, applying standards in the field to validate the platform and further experimenting with its capabilities in electron-activated dissociation and post-translational modification site localization. The instrument demonstrated robustness in standard proteins for platform QC, as aided by zeno trapping. We were also able to apply this to histone post-translational modifications, achieving high sequence coverage that allowed PTM's site localization across protein sequences with optimized EAD fragmentation. We demonstrated the ability to analyze proteins spanning the mass range and included analysis of glycosylated proteins. This is a reference point for future top-down proteomics experiments to be conducted on the ZenoTOF 7600 system.

As cationic functional groups with excellent alkaline resistance that are potentially applicable to building blocks of robust anion exchange membrane (AEM) materials for water splitting and fuel cell modules, we describe the synthesis of triarylsulfonium (TAS) salts bearing sterically demanding substituents by the reaction of arynes with diaryl sulfides/sulfoxides and by the Friedel-Crafts reaction of diaryl sulfoxides. The TAS cations possessing three substituted benzene rings, such as tris(2,5-dimethylphenyl)sulfonium and bis(2,5-dimethylphenyl)mesitylsulfonium, were effectively produced through the appropriate choice of reactions and reagents. The alkaline stability of the TAS cations thus obtained was evaluated from their time-course ¹H NMR spectra in 1 M KOH/CD₃OD, from which the alkaline resistance of the TAS cations increased dramatically as the steric bulkiness of the aromatic substituents attached to the TAS cations increased. Among them, bis(2,5-dimethylphenyl)mesitylsulfonium was found to exhibit 25 times higher alkaline resistance performance compared to benzyltrimethylammonium, a conventional quaternary ammonium cation. The decomposition mechanism of the TAS cations in the basic methanol media was studied in detail, and it was concluded that the decomposition occurred by the nucleophilic ipso-substitution by the methoxide anions.

Photocatalytic defluorinative cross-coupling reactions of α-trifluoromethyl alkenes with diverse radical precursors have emerged as a powerful strategy for the synthesis of gem-difluoroalkenes. However, the radical defluorinative arylation is relatively rare due to the limitation of aryl radical precursors. Aryl chlorides, as ideal candidates, remain a large challenge in this reaction because of the chemical inertness of the C(sp²)-Cl bond and their high negative reduction potential. Herein, we report a radical defluorinative arylation of α-trifluoromethyl alkenes with aryl chlorides as aryl radical precursors through a consecutive photoinduced electron transfer (ConPET) process. This protocol features mild conditions, operational simplicity, wide substrate scope, and functional group tolerance, producing a diverse range of benzylic gem-difluoroalkenes in moderate to good yields. The scale-up reaction and the valuable transformations of products demonstrate the great potential applications of this approach.