Peptide Identification from Mixture Tandem Mass Spectra

Abstract

The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of singlepeptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra. Molecular & Cellular Proteomics 9: 1476 -1485, 2010. The success of tandem MS (MS/MS1 ) approaches to peptide identification is partly due to advances in computational techniques allowing for the reliable interpretation of MS/MS spectra. Mainstream computational techniques mainly fall into two categories: database search approaches that score each spectrum against peptides in a sequence database (1-4) or de novo techniques that directly reconstruct the peptide sequence from each spectrum (5-8). The combination of these methods with advances in high-throughput MS/MS have promoted the accelerated growth of spectral libraries, collections of peptide MS/MS spectra the identification of which were validated by accepted statistical methods (9, 10) and often also manually confirmed by mass spectrometry experts. The similar concept of spectral archives was also recently proposed to denote spectral libraries including "interesting" nonidentified spectra (11) (i.e. recurring spectra with good de novo reconstructions but no database match). The growing availability of these large collections of MS/MS spectra has reignited the development of alternative peptide identification approaches based on spectral matching (12-14) and alignment (15-17) algorithms.However, mainstream approaches were developed under the (often unstated) assumption that each MS/MS spectrum is generated from a single peptide. Although chromatographic procedures greatly contribute to making this a reasonable assumption, there are several situations where it is difficult or even impossible to separate pairs of peptides. Examples include certain permutations of the peptide sequence or posttranslational modifications (see (18) for examples of co-eluting histone modification variants). In addition,...

Computing Exact p-values for a Cross-correlation Shotgun Proteomics Score Function

“…Note, however, that separately scoring each peptide in a chimeric spectrum, as we have done, requires that both peptides yield peaks with similar overall intensities. Joint identification of more difficult chimeras, with peptides whose fragment ions exhibit significantly different intensities, will likely require custom score functions and search algorithms (22,23).…”

MixGF: Spectral Probabilities for Mixture Spectra from more than One Peptide

“…To test MixGF's ability in these tasks, we built a set of simulated mixture spectra by linearly combining pairs of single-peptide spectra same as before (21). Because we know a priori the peptides that generated each simulated mixture spectrum, we can extract the top-scoring correct, half-correct, and incorrect matches returned by MixDB and compute their joint and conditional probabilities.…”

“…Data sets and Data Processing-The performance of MixGF was first evaluated on a set of simulated mixture spectra (21). In brief, mixture spectra were created by linearly combining two single-peptide spectra with predefined mixture coefficients ␣ -a parameter that reflects the relative abundance of the two peptides in the mixture spectrum.…”

“…low reproducibility (16)) and potentially increase the throughput of peptide identification 5-10 fold (4,17). However, despite the growing importance of mixture spectra in various contexts, there are still only a few computational tools that can analyze mixture spectra from more than one peptide (18,19,20,21,8,22). Our recent analysis indicated that current database search methods for mixture spectra still have relatively low sensitivity compared with their single-peptide counterpart and the main bottleneck is their limited ability to separate true matches from false positive matches (8).…”

Design and Application of a Data-Independent Precursor and Product Ion Repository

“…The technical advantages and limitations of DDA and DIA methods, including their main differences, have been discussed in detail [9,10,16]. Lastly, data processing errors associated with charge state assignment, de-isotoping and centroiding can be especially problematic when processing low abundance, overlapping isotopic cluster data [11,[17][18][19].…”