A better scoring model for de novo peptide sequencing: the symmetric difference between explained and measured masses

Abstract

BackgroundGiven a peptide as a string of amino acids, the masses of all its prefixes and suffixes can be found by a trivial linear scan through the amino acid masses. The inverse problem is the ideal de novo peptide sequencing problem: Given all prefix and suffix masses, determine the string of amino acids. In biological reality, the given masses are measured in a lab experiment, and measurements by necessity are noisy. The (real, noisy) de novo peptide sequencing problem therefore has a noisy input: a few of the prefix and suffix masses of the peptide are missing and a few other masses are given in addition. For this setting, we ask for an amino acid string that explains the given masses as accurately as possible.ResultsPast approaches interpreted accuracy by searching for a string that explains as many masses as possible. We feel, however, that it is not only bad to not explain a mass that appears, but also to explain a mass that does not appear. We propose to minimize the symmetric difference between the set of given masses and the set of masses that the string explains. For this new optimization problem, we propose an efficient algorithm that computes both the best and the k best solutions. Proof-of-concept experiments on measurements of synthesized peptides show that our approach leads to better results compared to finding a string that explains as many given masses as possible.ConclusionsWe conclude that considering the symmetric difference as optimization goal can improve the identification rates for de novo peptide sequencing. A preliminary version of this work has been presented at WABI 2016.Electronic supplementary materialThe online version of this article (doi:10.1186/s13015-017-0104-1) contains supplementary material, which is available to authorized users.

A Potential Golden Age to Come—Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

“…Notably, however, some peptides were only correctly identified using unprocessed data, suggesting that the deconvolution strategy still needs to be improved. Tschager et al . recently suggested a new scoring model for de novo sequencing, in which the algorithm minimizes the symmetric difference between explained and measured masses.…”

Improved de novo peptide sequencing using LC retention time information

“…We briefly describe the algorithm DeNovo [ 14 ] for computing a string of mass M that minimizes without considering retention times. We refer to [ 14 ] for a detailed description and a proof of correctness. Then, we describe algorithms for solving the de novo sequencing problem for each considered prediction model.…”

“…Similarly, we do not consider the fragment mass offsets of different ion types in the description. However, we do consider both offsets in the implementation of our algorithms using techniques described in [ 14 ].…”

“…We recall the de novo peptide sequencing problem with respect to the symmetric difference scoring model [ 14 ]: Given a mass M and a set of fragment masses X (measured by the mass spectrometer), find a string of mass M that minimizes . Equivalently to computing a string with mass M that minimizes , we can compute a string that maximizes , as X is a fixed input and can be chosen.…”

“…In the experimental evaluation, we are primarily interested in the impact of using the retention time information. We compare the identification rates of proposed algorithms for two prediction models with the identification rates of DeNovo [ 14 ], an algorithm that uses the same symmetric difference scoring model, but no retention time information. The symmetric difference scoring model already shows improved identification rates compared to the prevalent shared peak count scoring model [ 5 ] and this is further improved considering the retention time.…”