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MassIVE.quant is a repository infrastructure and data resource for reproducible quantitative mass spectrometry–based proteomics, which is compatible with all mass spectrometry data acquisition types and computational analysis tools. A branch structure enables MassIVE.quant to systematically store raw experimental data, metadata of the experimental design, scripts of the quantitative analysis workflow, intermediate input and output files, as well as alternative reanalyses of the same dataset. MassIVE.quant is a data repository and data resource for reproducible quantitative mass spectrometry–based proteomics.

Both identified and unidentified peptide mass spectra can be clustered and represented as consensus spectra in a spectral archive, offering new ways of interpreting proteomics data. A software tool for clustering billions of spectra is presented, as is a 1.18-billion spectra spectral archive. Tandem mass spectrometry (MS/MS) experiments yield multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra they generate. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about peptide spectra that are common across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with new ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ∼1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives rather than be analyzed as disparate datasets, as is mostly the case today.

GNPS is an open-access community-curated analysis platform for sharing natural product mass spectrometry data that enables continuous, automatic reanalysis of deposited 'living' data sets. The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu ), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to mass spectra deposited to public repositories or contained in public spectral libraries.

International sanctions have grown in use significantly since they were deployed in response to Iraq's invasion of Kuwait in August 1990.1 In the decade since the Kuwait crisis, economic and other measures not involving the use of force have been deployed by the international community against the Federal Republic of Yugoslavia (Serbia and Montenegro),2 Libya3 Rwanda4 Haiti5 Liberia,6 Somalia,7 Sudan,8 Sierra Leone,9 Angola,10 Eritrea and Ethiopia11 and, most recently, Afghanistan.12 International sanctions today derive—principally13—from a single source: the Security Council acting on the powers delegated to it by Member States under Article 41 of the UN Charter. However, while the sanctions contemplated by Article 41 have proved to be of continuing relevance in an increasingly interdependent world, the circumstances which enhance the potency of economic sanctions as a means of responding to an international crisis also increase the risk of loss by innocent States. The increased willingness of the Security Council to use its binding authority to impose economic sanctions has raised important questions both as to the cost of economic sanctions to implementing States, and the limits on the Council's powers.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.

Three-dimensional structures of the natural substrate unit for the enzyme N-acetylglucosamine-transferase II, GIcNAc-Man3-GlcNAc2, were investigated by molecular modelling methods. Molecular dynamics (MD) and molecular mechanics calculations on two hexasaccharides, namely GlcNAc-Man3-GlcNAc2-Asn and GlcNAc-Man3-GlcNAc2-OMe were performed by the Biosym/MSI software using the CVFF and CFF95 force fields in vacuum. The MD simulations were calculated for 3 ns at different simulation temperatures and for two values of dielectric constant, epsilon=1 and epsilon=4. From each 3 ns trajectory, 3050 structures have been optimized. The local minima obtained have been clustered into families exhibiting similar values of glycosidic torsional angles phi, psi, and omega. The influence of the simulation conditions and force fields used on the conformational behaviour and structure of the title oligosaccharides is discussed.

In untargeted metabolomics, reference MS/MS libraries are essential for structural annotation, yet currently explain only 6.9% of the more than 1.7 billion MS/MS spectra in public repositories. We hypothesized that many unannotated features arise from simple, biologically plausible transformations of endogenous and exposure-derived compounds. To test this, we created a reference resource by synthesizing over 100,000 compounds using multiplexed reactions that mimic such biochemical transformations. 91% of the compounds synthesized are absent from existing structural databases. Through improvements in the construction of the computational infrastructure that enables pan repository-scale MS/MS comparisons, searching this biologically inspired MS/MS library increased the overall reference-based match rate by 17.4%, yielding over 60 million new matches and raising the global pan-repository MS/MS annotation rate to 8.1%. By facilitating structural hypotheses for previously uncharacterized MS/MS data, this framework expands the accessible detectable biochemical landscape across human, animal, plant, and microbial systems, revealing previously undescribed metabolites such as ibuprofen-carnitine and 5-ASA-phenylpropionic acid conjugates arising from drug-host and host-microbiome co-metabolism.

Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.