title: UVnovo: A de Novo Sequencing Algorithm Using Single Series of Fragment Ions via Chromophore Tagging and 351 nm Ultraviolet Photodissociation Mass Spectrometry.
abstract: De novo peptide sequencing by mass spectrometry represents an important strategy for characterizing novel peptides and proteins, in which a peptide's amino acid sequence is inferred directly from the precursor peptide mass and tandem mass spectrum (MS/MS or MS(3)) fragment ions, without comparison to a reference proteome. This method is ideal for organisms or samples lacking a complete or well-annotated reference sequence set. One of the major barriers to de novo spectral interpretation arises from confusion of N- and C-terminal ion series due to the symmetry between b and y ion pairs created by collisional activation methods (or c, z ions for electron-based activation methods). This is known as the "antisymmetric path problem" and leads to inverted amino acid subsequences within a de novo reconstruction. Here, we combine several key strategies for de novo peptide sequencing into a single high-throughput pipeline: high-efficiency carbamylation blocks lysine side chains, and subsequent tryptic digestion and N-terminal peptide derivatization with the ultraviolet chromophore AMCA yield peptides susceptible to 351 nm ultraviolet photodissociation (UVPD). UVPD-MS/MS of the AMCA-modified peptides then predominantly produces y ions in the MS/MS spectra, specifically addressing the antisymmetric path problem. Finally, the program UVnovo applies a random forest algorithm to automatically learn from and then interpret UVPD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring. We show this combined strategy provides high-performance de novo peptide sequencing, enabling the de novo sequencing of thousands of peptides from an Escherichia coli lysate at high confidence.
authors.affiliation: Department of Chemistry, University of Texas , Austin, Texas 78712, United States.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Robotham
authors.affiliation: Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas , Austin, Texas 78712, United States.
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Horton
authors.affiliation: Department of Chemistry, University of Texas , Austin, Texas 78712, United States.
authors.forename: Joe R
authors.initials: JR
authors.lastname: Cannon
authors.affiliation: Department of Chemistry, University of Texas , Austin, Texas 78712, United States.
authors.forename: Victoria C
authors.initials: VC
authors.lastname: Cotham
authors.affiliation: Center for Systems and Synthetic Biology, Department of Molecular Biosciences, University of Texas , Austin, Texas 78712, United States.
authors.forename: Edward M
authors.initials: EM
authors.lastname: Marcotte
authors.affiliation: Department of Chemistry, University of Texas , Austin, Texas 78712, United States.
authors.forename: Jennifer S
authors.initials: JS
authors.lastname: Brodbelt
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D004926:Escherichia coli / Q000737:chemistry; D007477:Ions / Q000737:chemistry; D013058:Mass Spectrometry; D010455:Peptides / Q000737:chemistry*; D055668:Photochemical Processes; D020539:Sequence Analysis, Protein; D014466:Ultraviolet Rays
chemical_list: D007477:Ions; D010455:Peptides