Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Improving reproducibility and sensitivity in identifying human proteins by shotgun proteomics

Identifying proteins in cell extracts by shotgun proteomics involves digesting the proteins, sequencing the resulting peptides by data-dependent mass spectrometry (MS/MS), and searching protein databases to identify the proteins from which the peptides are derived. Manual analysis and direct spectral comparison reveal that scores from two commonly used search programs (Sequest and Mascot) validate less than half of potentially identifiable MS/MS spectra (class positive) from shotgun analyses of the human erythroleukemia K562 cell line. Here we demonstrate increased sensitivity and accuracy using a focused search strategy along with a peptide sequence validation script that does not rely exclusively on XCorr or Mowse scores generated by Sequest or Mascot, but uses consensus between the search programs, along with chemical properties and scores describing the nature of the fragmentation spectrum (ion score and RSP). The approach yielded 4.2% false positive and 8% false negative frequencies in peptide assignments. The protein profile is then assembled from peptide assignments using a novel peptide-centric protein nomenclature that more accurately reports protein variants that contain identical peptide sequences. An Isoform Resolver algorithm ensures that the protein count is not inflated by variants in the protein database, eliminating similar to25% of redundant proteins. Analysis of soluble proteins from a human K562 cells identified 5130 unique proteins, with similar to100 false positive protein assignments