Efficiency of Database Search for Identification of Mutated and Modified Proteins via Mass Spectrometry

Abstract

Although protein identification by matching tandem mass spectra (MS/MS) against protein databases is a widespread tool in mass spectrometry, the question about reliability of such searches remains open. Absence of rigorous significance scores in MS/MS database search makes it difficult to discard random database hits and may lead to erroneous protein identification, particularly in the case of mutated or post-translationally modified peptides. This problem is especially important for high-throughput MS/MS projects when the possibility of expert analysis is limited. Thus, algorithms that sort out reliable database hits from unreliable ones and identify mutated and modified peptides are sought. Most MS/MS database search algorithms rely on variations of the Shared Peaks Count approach that scores pairs of spectra by the peaks (masses) they have in common. Although this approach proved to be useful, it has a high error rate in identification of mutated and modified peptides. We describe new MS/MS database search tools, MS-CONVOLUTION and MS-ALIGNMENT, which implement the spectral convolution and spectral alignment approaches to peptide identification. We further analyze these approaches to identification of modified peptides and demonstrate their advantages over the Shared Peaks Count. We also use the spectral alignment approach as a filter in a new database search algorithm that reliably identifies peptides differing by up to two mutations/modifications from a peptide in a database.

Expanding the organismal scope of proteomics: Cross‐species protein identification by mass spectrometry and its implications

“…A second possibility is to interpret tandem mass spectra of peptides using specialized software that creates amino acid sequences de novo [21][22][23][24]. Although the software utilizes different computational principles, sequences of short peptides can be produced rapidly and accurately.…”

“…However, the absence of a rigorous scoring system may lead to erroneous identifications as the correct sequence may be present in the list, but may not be ranked among the top hits. Even though it is difficult to use these sequences for cloning (where the requirement is that the sequences should be long, 100% accurate, and encode for low degeneracy primers), they can be successfully used for identifying proteins in a sequence database using various sequence similarity search algorithms [23][24][25].…”

Development of new methodologies for the mass spectrometry study of bioorganic macromolecules

“…Thus, it is necessary to control the results by statistical analysis (Eriksson, Chait, & Fenyö, 2000;Pevzner et al, 2001).…”

Comparison of Spectra in Unsequenced Species

“…However, this solution, leading to an exponential number of possibilities, is of course too time consuming [8]. The other one, SpectralAlignment [1,9,10], is a dynamic programming algorithm that has been designed to identify peptides even in presence of modifications. This method works rather well for one or two modifications, but for a larger number of modifications, SpectralAlignment is not really sustainable [9].…”

“…The other one, SpectralAlignment [1,9,10], is a dynamic programming algorithm that has been designed to identify peptides even in presence of modifications. This method works rather well for one or two modifications, but for a larger number of modifications, SpectralAlignment is not really sustainable [9]. Spectra comparison has an essential advantage, namely the precision in the comparison, allowing information to be drawn even from spectra which used to be unexploitable with a de novo approach.…”