Byonic is the name of a software package for peptide and protein identification by tandem mass spectrometry. This software, which has only recently become commercially available, facilitates a much wider range of search possibilities than previous search software such as SEQUEST and Mascot. Byonic allows the user to define an essentially unlimited number of variable modification types. Byonic also allows the user to set a separate limit on the number of occurrences of each modification type, so that a search may consider only one or two chance modifications such as oxidations and deamidations per peptide, yet allow three or four biological modifications such as phosphorylations, which tend to cluster together. Hence, Byonic can search for tens or even hundreds of modification types simultaneously without a prohibitively large combinatorial explosion. Byonic's Wildcard Search allows the user to search for unanticipated or even unknown modifications alongside known modifications. Finally, Byonic's Glycopeptide Search allows the user to identify glycopeptides without prior knowledge of glycan masses or glycosylation sites. Curr. Protoc. Bioinform. 40:13.20.1‐13.20.14. © 2012 by John Wiley & Sons, Inc.

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The MLS surface [Levin 2003], used for modeling and rendering with point clouds, was originally defined algorithmically as the output of a particular meshless construction. We give a new explicit definition in terms of the critical points of an energy function on lines determined by a vector field. This definition reveals connections to research in computer vision and computational topology.Variants of the MLS surface can be created by varying the vector field and the energy function. As an example, we define a similar surface determined by a cloud of surfels (points equipped with normals), rather than points.We also observe that some procedures described in the literature to take points in space onto the MLS surface fail to do so, and we describe a simple iterative procedure which does.

Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new “parsimonious” charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma properdin glycoprotein, the new algorithm could deconvolve monomer and dimer simultaneously and, when focused on the m/z range of the monomer, gave accurate and interpretable masses for glycoforms that had previously been analyzed manually using m/z peaks rather than deconvolved masses. On therapeutic antibodies, the new algorithm facilitated the analysis of extensions, truncations, and Fab glycosylation. The algorithm facilitates the use of native mass spectrometry for the qualitative and quantitative analysis of protein and protein assemblies.

The target-decoy approach to estimating and controlling false discovery rate (FDR) has become a de facto standard in shotgun proteomics, and it has been applied at both the peptide-to-spectrum match (PSM) and protein levels. Current bioinformatics methods control either the PSM- or the protein-level FDR, but not both. In order to obtain the most reliable information from their data, users must employ one method when the number of tandem mass spectra exceeds the number of proteins in the database and another method when the reverse is true. Here we propose a simple variation of the standard target-decoy strategy that estimates and controls PSM and protein FDRs simultaneously, regardless of the relative numbers of spectra and proteins. We demonstrate that even if the final goal is a list of PSMs with a fixed low FDR and not a list of protein identifications, the proposed two-dimensional strategy offers advantages over a pure PSM-level strategy.