A mass spectrometry-based method for differentiation of positional isomers of monosubstituted pyrazine N-oxides using metal ion complexes

Cyclic heptapeptides with metal binding properties isolated from the fungus Cadophora malorum from Antarctic soil

The Antarctic fungus Cadophora malorum produces previously undescribed cyclic heptapeptides (cadophorin A and B) containing an anthranilic acid residue. The planar structure of these peptides was determined by high-resolution mass spectrometry combined with extensive 1D and 2D NMR spectroscopy. The absolute configuration of the amino acids was determined by Marfey's method, with HPLC analysis of FDVA (Nα-(2,4-dinitro-5-fluorphenyl)-L-valinamide) derivatives making use of a PFP column. Remarkably, cadophorin 2 possesses both the uncommon D-Ile and D-allo-Ile in its structure. The peptides have metal binding properties as shown by LCMS with post column addition of metal salt solutions. These results were supported by DFT calculations.

Mass spectrometric-based investigation of differentially protected azatryptophan derivatives using Orbitrap mass spectrometry: Differentiation of positional isomers under protonation and alkali-cationization conditions

RATIONALE

Differentiation and structural characterization of positional isomers of differentially protected azatryptophan derivatives using electrospray ionization high-resolution tandem mass spectrometry (ESI-HRMS/MS) is important from the perspective of drug discovery research. Also, these derivatives can be used as building blocks for the synthesis of various biologically active compounds and have attracted significant attention in the field of modern drug discovery, especially peptide-based drugs, protein folding and protein-protein interactions because of their interesting spectral properties.

METHODS

ESI-HRMS/MS in positive ionization mode was used to differentiate and characterize positional isomers of protected azatryptophan derivatives.

RESULTS

ESI-HRMS/MS of [M + H]⁺ and [M + Na]⁺ ions of positional isomers of differentially protected azatryptophan derivatives display distinct fragmentation patterns. The MS/MS of [M + H]⁺ ion of isomer 1 showed an additional ion at m/z 358.0846 ([M + H-Boc-C₁₄ H₁₀ -HF]⁺ ) which was not present for 4. The fragment ion at m/z 332.0857 was observed for 1 and not for 4 which would be formed by the expulsion of butyloxycarbonyl (Boc) and fluorenylmethyloxycarbonyl (Fmoc) groups. Moreover, the ions 422.0812 and 378.0912 are found to be relatively more abundant for isomer 4 which could be probably attributed to the formation of stable ions. Similarly, other positional isomers exhibited distinct fragmentation from one another.

CONCLUSIONS

The present study demonstrates that ESI-HRMS/MS can be used for differentiation and structural characterization of positional isomers of protected azatryptophan derivatives. The MS/MS of [M + H]⁺ and [M + Na]⁺ ions of these positional isomers displayed differences in their fragmentation behaviour. The impact of different substitutions at different positions (1 and 6) of protected azatryptophan derivatives (1-6) on their fragmentation behaviour was also investigated in detail. Also, the nitrogen atom at different positions in the pyrrolopyridine ring led to different fragmentation patterns.

Prediction of Metal Ion Binding Sites of Transmembrane Proteins

The metal ion binding of transmembrane proteins (TMPs) plays a fundamental role in biological processes, pharmaceutics, and medicine, but it is hard to extract enough TMP structures in experimental techniques to discover their binding mechanism comprehensively. To predict the metal ion binding sites for TMPs on a large scale, we present a simple and effective two-stage prediction method TMP-MIBS, to identify the corresponding binding residues using TMP sequences. At present, there is no specific research on the metal ion binding prediction of TMPs. Thereby, we compared our model with the published tools which do not distinguish TMPs from water-soluble proteins. The results in the independent verification dataset show that TMP-MIBS has superior performance. This paper explores the interaction mechanism between TMPs and metal ions, which is helpful to understand the structure and function of TMPs and is of great significance to further construct transport mechanisms and identify potential drug targets.

Spectral trends in GC-EI-MS data obtained from the SWGDRUG mass spectral library and literature: A resource for the identification of unknown compounds

Rapid identification of new or emerging psychoactive substances remains a critical challenge in forensic drug chemistry laboratories. Current analytical protocols are well-designed for confirmation of known substances yet struggle when new compounds are encountered. Many laboratories initially attempt to classify new compounds using gas chromatography-electron ionization-mass spectrometry (GC-EI-MS). Though there is a large body of research focused on the analysis of illicit substances with GC-EI-MS, there is little high-level discussion of mass spectral trends for different classes of drugs. This manuscript compiles literature information and performs simple exploratory analyses on evaluated GC-EI-MS data to investigate mass spectral trends for illicit substance classes. Additionally, this work offers other important aspects: brief discussions of how each class of drugs is used; illustrations of EI mass spectra with proposed structures of commonly observed ions; and summaries of mass spectral trends that can help an analyst classify new illicit compounds.

Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model

Metal binding proteins or metallo-proteins are important for the stability of the protein and also serve as co-factors in various functions like controlling metabolism, regulating signal transport, and metal homeostasis. In structural genomics, prediction of metal binding proteins help in the selection of suitable growth medium for overexpression's studies and also help in obtaining the functional protein. Computational prediction using machine learning approach has been widely used in various fields of bioinformatics based on the fact all the information contains in amino acid sequence. In this study, random forest machine learning prediction systems were deployed with simplified amino acid for prediction of individual major metal ion binding sites like copper, calcium, cobalt, iron, magnesium, manganese, nickel, and zinc.