Peptide retention time prediction

Moruz, Luminita; Käll, Lukas

doi:10.1002/mas.21488

Cited by 78 publications

(74 citation statements)

References 60 publications

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“…These approaches either (i) predict RTs from peptide sequences or (ii) align empirically measured RTs. Estimated peptide RTs have a wide range of uses, such as scheduling targeted MS/MS experiments [21], building efficient inclusion and exclusion lists for LC-MS/MS [22,23], or augmenting MS2 mass spectra to increase identification rates [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…The predicted RTs can be improved by implementing machine learning algorithms that incorporate confident, observed peptides as training data [14,18,[31][32][33][34]. Predicted peptide RTs are mostly used for scheduling targeted MS/MS analyses where acquisition time is limited, e.g., multiple reaction monitoring [21]. They can also be used to aid peptide sequencing, as exemplified by "peptide fingerprinting" -a method that identifies peptides based on an ion's RT and mass over charge (m/z) [27,[35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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DART-ID increases single-cell proteome coverage

Chen¹,

Franks

Slavov³

2018

Preprint

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DART-ID increases single-cell proteome coverage

Chen¹,

Franks

Slavov³

2018

Preprint

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“…These advances require instrumentation capable of high-accuracy measurements, LC systems with sufficient RT precision, as well as precise prediction algorithms for relative RT [62,94,93].…”

Section: Research Objective and Motivationmentioning

confidence: 99%

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Liang

Lajoie

Zhang

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches.MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting intact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin.In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In the result validation, we propose two new target-decoy search strategies to estimate the false discovery rate (FDR). The experiments on simulated, authentic, and simulated authentic dataset demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.i

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“…This reduction means that there is less ionization competition, improved sensitivity for data dependent/independent analysis, and reduced chimericity in fragmentation spectra (MS 2 ) 2, 3 . In addition to these benefits, the retention time measurement itself provides an additional dimension of information to interpret the signals generated by a peptide 4 . In order to interpret these acquired signals, they need to be matched with earlier observations of the same peptides or with a prediction of the signal.…”

Section: Introductionmentioning

confidence: 99%

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

Bouwmeester

Gabriels

Hulstaert

et al. 2020

Preprint

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The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex LC-MS identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open modification searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity.We here therefore present DeepLC, a novel deep learning peptide retention time predictor utilizing a new peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides, and, more importantly, accurately predicts retention times for modifications not seen during training. DeepLC is available under the permissive Apache 2.0 open source license and comes with a user-friendly graphical user interface, as well as a Python package on PyPI, Bioconda, and BioContainers for effortless workflow integration.

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Peptide retention time prediction

Cited by 78 publications

References 60 publications

DART-ID increases single-cell proteome coverage

DART-ID increases single-cell proteome coverage

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

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