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Effects of transition metal ion coordination on the collision-induced dissociation of polyalanines

Transition metal-polyalanine complexes were analyzed in a high-capacity quadrupole ion trap after electrospray ionization. Polyalanines have no polar amino acid side chains to coordinate metal ions, thus allowing the effects metal ion interaction with the peptide backbone to be explored. Positive mode mass spectra produced from peptides mixed with salts of the first row transition metals Cr(III), Fe(II), Fe(III), Co(II), Ni(II), Cu(I), and Cu(II) yield singly and doubly charged metallated ions. These precursor ions undergo collision-induced dissociation (CID) to give almost exclusively metallated N-terminal product ions whose types and relative abundances depend on the identity of the transition metal. For example, Cr(III)-cationized peptides yield CID spectra that are complex and have several neutral losses, whereas Fe(III)-cationized peptides dissociate to give intense non-metallated products. The addition of Cu(II) shows the most promise for sequencing. Spectra obtained from the CID of singly and doubly charged Cu-heptaalanine ions, [M + Cu - H](+) and [M + Cu](2+) , are complimentary and together provide cleavage at every residue and no neutral losses. (This contrasts with [M + H](+) of heptaalanine, where CID does not provide backbone ions to sequence the first three residues.) Transition metal cationization produces abundant metallated a-ions by CID, unlike protonated peptides that produce primarily b- and y-ions. The prominence of metallated a-ions is interesting because they do not always form from b-ions. Tandem mass spectrometry on metallated (Met = metal) a- and b-ions indicate that [b(n)  + Met - H](2+) lose CO to form [a(n)  + Met - H](2+), mimicking protonated structures. In contrast, [a(n)  + Met - H](2+) eliminate an amino acid residue to form [a(n-1)  + Met - H](2+), which may be useful in sequencing.

Improved peptide elution time prediction for reversed-phase liquid chromatography-MS by incorporating peptide sequence information

We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed-phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest-neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using approximately 345,000 nonredundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of approximately 1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis, K.; Kangas, L. J.; Ferguson, P. L.; Anderson, G. A.; Pasa-Tolic, L.; Lipton, M. S.; Auberry, K. J.; Strittmatter, E. F.; Shen, Y.; Zhao, R.; Smith, R. D. Anal. Chem. 2003, 75, 1039-1048) and other previously reported models.