Computational Plant Biochemistry

Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule‐based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.

Mass spectrometry (MS) is an important analytical technique for the detection and identification of small compounds. The main bottleneck in the interpretation of metabolite profiling or screening experiments is the identification of unknown compounds from tandem mass spectra.Spectral libraries for tandem MS, such as MassBank or NIST, contain reference spectra for many compounds, but their limited chemical coverage reduces the chance for a correct and reliable identification of unknown spectra outside the database domain.On the other hand, compound databases like PubChem or ChemSpider have a much larger coverage of the chemical space, but they cannot be queried with spectral information directly. Recently, computational mass spectrometry methods and in silico fragmentation prediction allow users to search such databases of chemical structures.We present a new strategy called MetFusion to combine identification results from several resources, in particular, from the in silico fragmenter MetFrag with the spectral library MassBank to improve compound identification. We evaluate the performance on a set of 1062 spectra and achieve an improved ranking of the correct compound from rank 28 using MetFrag alone, to rank 7 with MetFusion, even if the correct compound and similar compounds are absent from the spectral library. On the basis of the evaluation, we extrapolate the performance of MetFusion to the KEGG compound database.

MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron‐ionization mass spectrometry(EI‐MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)‐MSn data of 2337 authentic compounds of metabolites, 11 545 EI‐MS and 834 other‐MS data of 10 286 volatile natural and synthetic compounds, and 3045 ESI‐MS2 data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI‐MS2 data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass‐to‐charge ratio are optimized to the ESI‐MS2 data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI‐MS2 data on an identical compound under different collision‐induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21–23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data.