Plant phytohormone pathways are regulated by an intricate network of signaling components and modulators, many of which still remain unknown. Here, we report a forward chemical genetics approach for the identification of functional SA agonists in Arabidopsis thaliana that revealed Neratinib (Ner), a covalent pan-HER kinase inhibitor drug in humans, as a modulator of SA signaling. Instead of a protein kinase, chemoproteomics unveiled that Ner covalently modifies a surface-exposed cysteine residue of Arabidopsis epoxide hydrolase isoform 7 (AtEH7), thereby triggering its allosteric inhibition. Physiologically, the Ner application induces jasmonate metabolism in an AtEH7-dependent manner as an early response. In addition, it modulates PATHOGENESIS RELATED 1 (PR1) expression as a hallmark of SA signaling activation as a later effect. AtEH7, however, is not the exclusive target for this physiological readout induced by Ner. Although the underlying molecular mechanisms of AtEH7-dependent modulation of jasmonate signaling and Ner-induced PR1-dependent activation of SA signaling and thus defense response regulation remain unknown, our present work illustrates the powerful combination of forward chemical genetics and chemical proteomics for identifying novel phytohormone signaling modulatory factors. It also suggests that marginally explored metabolic enzymes such as epoxide hydrolases may have further physiological roles in modulating signaling.

Enzyme-based synthetic chemistry provides a green way to synthesize industrially important chemical scaffolds and provides incomparable substrate specificity and unmatched stereo-, regio-, and chemoselective product formation. However, using biocatalysts at an industrial scale has its challenges, like their narrow substrate scope, limited stability in large-scale one-pot reactions, and low expression levels. These limitations can be overcome by engineering and fine-tuning these biocatalysts using advanced protein engineering methods. A detailed understanding of the enzyme structure and catalytic mechanism and its structure–function relationship, cooperativity in binding of substrates, and dynamics of substrate–enzyme–cofactor complexes is essential for rational enzyme engineering for a specific purpose. This Review covers all these aspects along with an in-depth categorization of various industrially and pharmaceutically crucial bisubstrate enzymes based on their reaction mechanisms and their active site and substrate/cofactor-binding site structures. As the bisubstrate enzymes constitute around 60% of the known industrially important enzymes, studying their mechanism of actions and structure–activity relationship gives significant insight into deciding the targets for protein engineering for developing industrial biocatalysts. Thus, this Review is focused on providing a comprehensive knowledge of the bisubstrate enzymes’ structure, their mechanisms, and protein engineering approaches to develop them into industrial biocatalysts.

The rapid annotation and identification by mass spectrometry techniques of flavonoids remains a challenge, due to their structural diversity and the limited availability of reference standards. This study applies a workflow to characterize two isoflavonoids, the orobol-C-glycosides analogs, using high-energy collisional dissociation (HCD)- and collision-induced dissociation (CID)-type fragmentation patterns, and also to evaluate the antioxidant effects of these compounds by ferric reducing antioxidant power (FRAP), 2,2′-azino-bis(3-ethylbenzothiazolin acid) 6-sulfonic acid (ABTS), and 2,2-diphenyl-1-picrylhydrazyl (DPPH) methods. By the CID-type fragmentation, in positive mode and at all high-resolution mass spectrometry (HRMS) multiple stage, there were shown differences in the annotation of the compounds, mainly concerning some ratios of relative abundance. At CID-MS2 20 eV, the compounds could be efficiently characterized, because they present distinct base peaks [M + H]+ and [M + H–H2O]+ for the orobol-8-C- and orobol-6-C-glycoside, respectively. Similarly, by the HCD-type fragmentation, in HRMS2 stage, differences between orobol analogs in both mode of ionization were observed. However, the HR HCD-MS2 at 80 eV, in positive mode, generated more ions and each isomer presented different base peaks ions, [0,2X]+ for the orobol-8-C-glycoside and [0,3X]+ for the orobol-6-C-glycoside. By the DPPH, the 8-C-derivative showed a very close value compared with the standard rutin and, in the ABTS method, a higher radical-scavenging activity. In both methods, the EC50 of orobol-8-C-glycoside was almost twice better compared with orobol-6-C-glycoside. In FRAP, both C-glycosides showed a good capacity as Fe+3 reducing agents. We could realize that combined MS techniques, highlighting the positive mode of ionization, can be used to evaluate the isoflavones analogs being useful to differentiate between the isomeric flavones; therefore, these data are important to mass spectrometry dereplication studies become more efficient.

Long‐chain ferulic acid esters, such as eicosyl ferulate (1), show a complex and analytically valuable fragmentation behavior under negative‐ion electrospay collision‐induced dissociation ((‐)‐ESI‐CID) mass spectrometry, as studied by use of a high‐resolution (Orbitrap) mass spectrometer. In a strong contrast to the very simple fragmentation of the [M + H]+ ion, which is discussed briefly, the deprotonated molecule, [M ‐ H]‐, exhibits a rich secondary fragmentation chemistry. It first loses a methyl radical (MS2) and the ortho‐quinoid [M ‐ H ‐ Me]‐• radical anion thus formed then dissociates by loss of an extended series of neutral radicals, CnH2n+1• (n = 0‐16) from the long alkyl chain, in competition with the expulsion of CO and CO2 (MS3). The further fragmentation (MS4) of the [M ‐ H ‐ Me ‐ C3H7]‐ ion, discussed as an example, and the highly specific losses of alkyl radicals from the [M ‐ H ‐ Me ‐ CO]‐• and [M ‐ H ‐ Me ‐ CO2]‐• ions provide some mechanistic and structural insights.

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.

Representative compounds with a 1,3‐dihydroxybenzene substructure belonging to different important polyphenol classes (stilbenes, flavones, isoflavones, flavonols, flavanones, flavanols, phloroglucinols, anthraquinones and bisanthraquinones) were investigated based on detailed high‐resolution tandem mass spectrometry measurements with an Orbitrap system under negative ion electrospray conditions. The mass spectral behaviour of these compound classes was compared among each other not only with respect to previously described losses of CO, CH2CO and C3O2 but also concerning the loss of CO2 and successive specific fragmentations. Furthermore, some unusual fragmentations such as the loss of a methyl radical during mass spectral decomposition are discussed. The obtained results demonstrate both similarities and differences in their mass spectral fragmentation under MSn conditions, allowing a characterization of the corresponding compound type.

The parathyroid hormone (PTH) is an 84-residue peptide, which regulates the blood Ca2+ level via GPCR binding and subsequent activation of intracellular signaling cascades. PTH is posttranslationally phosphorylated in the parathyroid glands; however, the functional significance of this processes is not well characterized. In the present study, mass spectrometric analysis revealed three sites of phosphorylation, and NMR spectroscopy assigned Ser1, Ser3, and Ser17 as modified sites. These sites are located at the N-terminus of the hormone, which is important for receptor recognition and activation. NMR shows further that the three phosphate groups remotely disturb the α-helical propensity up to Ala36. An intracellular cAMP accumulation assay elucidated the biological significance of this phosphorylation because it ablated the PTH-mediated signaling. Our studies thus shed light on functional implications of phosphorylation at native PTH as an additional level of regulation.

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.

A series of prevailing prenylated furanocoumarins from leaves of Dorstenia gigas and Dorstenia foetida (Moraceae) were investigated by liquid chromatography/electrospray tandem mass spectrometry. The mass spectral behavior of the furanocoumarins under positive ion electrospray conditions is discussed using both an ion trap and a triple quadrupole system. It is demonstrated that both methods represent valuable tools not only for the rapid classification of this type of compounds, but also with respect to their substitution pattern.

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.