Shared Peptides in Mass Spectrometry Based Protein Quantification

Abstract

Abstract. In analyzing the proteome using mass spectrometry, the mass values help identify the molecules, and the intensities help quantify them, relative to their abundance in other samples. Peptides that are shared across different protein sequences are typically discarded as being uninformative w.r.t each of the parent proteins. In this paper, we investigate the use of shared peptides which are ubiquitous (∼ 50% of peptides) in mass spectrometric data-sets. In many cases, shared peptides can help compute the relative amounts of different proteins that share the same peptide. Also, proteins with no unique peptide in the sample can still be analyzed for relative abundance. Our paper is the first attempt to use shared peptides in protein quantification, and makes use of combinatorial optimization to reduce the error in relative abundance measurements. We describe the topological and numerical properties required for robust estimates, and use them to improve our estimates for ill-conditioned systems. Extensive simulations validate our approach even in the presence of experimental error. We apply our method to a model of Arabidopsis root knot nematode infection, and elucidate the differential role of many protein family members in mediating host response to the pathogen.

Parameterized algorithms for double hypergraph dualization with rank limitation and maximum minimal vertex cover

“…Hence we must add to C another neighbor of y in branch (iii), or another neighbor of x in branch (iv). In both cases this improves the branching vector to (2,3,3,4) and the branching number to 1.62. Note carefully that, for every vertex we put in C , we have also excluded some neighbor from C .…”

“…If the degrees in N[u] are (2, 2, 3) or larger, we take (2, 2, 3) directly as the worst branching vector and obtain 1.62, too. Finally note that the branching numbers for d ≥ 3 are all better than 1.59, because the worst case among them is (3,3,3,3).…”

“…This appears difficult even for s = 3 (as opposed to the minimization problem Hitting Set where such improvements are easy to obtain). We cannot simply generalize our approach for MMVC to MMHS, since even for rank 3 …”

“…Quantitative and probabilistic approaches to the inference of proteins with shared peptides also take the abundance and detectability of peptides into account [3], other heuristics only aim at the computation of one most likely mixture relative to some scoring function [4], but in the present paper we are concerned with the representation of the set of all possible mixtures (without quantification) once the hypergraph of peptides and candidate proteins is given.…”

A high performance profile-biomarker diagnosis for mass spectral profiles

“…Many methods and protocols are proposed and being developed to enhance mass spectral data reproducibility from biological and technological aspects. They include employing peptide profiling to replace proteomics profiling to get extremely high resolution data, improving experimental designs to avoid mingles between biological and technological variables, and developing more robust preprocessing algorithms [ 2 - 5 ]. However, mass spectral data reproducibility enhancement seems to be facing a built-in challenge from the technology itself [ 6 ], i.e., almost any small, even tiny changes in the part of proteome will be amplified to rather large even huge differences in mass spectra, no matter whether the sources of the changes are from biological factors or experimental conditions.…”