Prediction of Peptide Fragment Ion Mass Spectra by Data Mining Techniques

Dong, Naiping; Liang, Yi‐Zeng; Xu, Qingsong; Mok, Daniel K. W.; Yi, Lunzhao; Lü, Hongmei; He, Min

doi:10.1021/ac501094m

Cited by 16 publications

(12 citation statements)

References 59 publications

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“…[5,6] Several algorithms were also developed to predict the MS/MS spectra, such as MassAnalyzer, PeptideART, MS2PBPI, etc. [7][8][9][10][11][12][13] However, most of the popular search engines, for example, Mascot, [14] MaxQuant, [15] MS-GF+, [16] MSFragger, [17] and pFind, [18,19] only consider the mass-to-charge ratio (m/z) values of fragment ions, ignoring the peak intensities while calculating the similarity between the experimental and theoretical spectra. SEQUEST considers the theoretical intensities in its XCorr score, but it uses manually assigned intensities to differentiate b ions, y ions, and their ammonia/water loss ions.…”

Section: Doi: 101002/pmic201900345mentioning

confidence: 99%

A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics

Sheng

Bai

et al. 2020

Proteomics

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Spectrum prediction using machine learning or deep learning models is an emerging method in computational proteomics. Several deep learning-based MS/MS spectrum prediction tools have been developed and showed their potentials not only for increasing the sensitivity and accuracy of data-dependent acquisition search engines, but also for building spectral libraries for data-independent acquisition analysis. Different tools with their unique algorithms and implementations may result in different performances. Hence, it is necessary to systematically evaluate these tools to find out their preferences and intrinsic differences. In this study, multiple datasets with different collision energies, enzymes, instruments, and species, are used to evaluate the performances of the deep learning-based MS/MS spectrum prediction tools, as well as, the machine learning-based tool MS2PIP. The evaluations may provide helpful insights and guidelines of spectrum prediction tools for the corresponding researchers.

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Section: Doi: 101002/pmic201900345mentioning

confidence: 99%

A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics

Sheng

Bai

et al. 2020

Proteomics

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“…There have been several different approaches previously used for predicting fragmentation in mass spectrometry setting [13][14][15][16] . To our knowledge MS2PIP 16 tool, combining XGBoost and random forest algorithms, is the state of the art in terms of prediction of fragment intensities.…”

Section: Comparison To Existing Methodsmentioning

confidence: 99%

Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models

2019

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Tandem mass-spectrometry has become the method of choice for high-throughput, quantitative analysis in proteomics. However, since the link between the peptides and the proteins they originate from is typically broken, identification of the analyzed peptides relies on matching of the fragmentation spectra (MS2) to theoretical spectra of possible candidate peptides, often filtered for precursor ion mass. To this end, peptidespectrum matching algorithms score the concordance between the experimental and the theoretical spectra of candidate peptides, by evaluating the number (or proportion) of theoretically possible fragment ions observed in the experimental spectra, without any discrimination. However, the assumption that each theoretical fragment is just as likely to be observed is inaccurate. On the contrary, MS2 spectra often have few dominant fragments.We propose a novel prediction algorithm based on a hidden Markov model, which allow for the training process to be carried out very efficiently. Using millions of MS/MS spectra generated in our lab, we found an overall good reproducibility across different fragmentation spectra, given the precursor peptide and charge state. This result implies that there is indeed a pattern to fragmentation that justifies using machine learning methods.Furthermore, the overall agreement between spectra of the same peptide at the same charge state serves as an upper limit on how well prediction algorithms can be expected to perform.We have investigated the performance of a third order HMM model, trained on several million MS2 spectra, in various ways. Compared to a mock model, in which the fragment ions and their intensities are shuffled, we see a clear difference in prediction accuracy using our model. This result indicates that our model can pick up meaningful patterns, i.e. we can indeed learn the fragmentation process. Secondly, looking at the variability of the prediction performance by varying the train/test data split, in a K-fold cross validation scheme, we observed an overall robust model that performs well independent of the specific peptides that are present in the training data.Last but not least, we propose that the real value of this model is as a pre-processing step in the peptide identification process, by discerning fragment ions that are unlikely to be intense for a given candidate peptide, rather than using the actual predicted intensities. As such, probabilistic measures of concordance between experimental and theoretical spectra, would leverage better statistics.

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“…[58][59][60][61] MassAnalyzer is a popular tool in this category. [58] Data-driven methods, or more generally machine learning-based methods, include traditional machine learning-based tools, such as PeptideART, [55,62] MS 2 PIP, [63][64][65] MS 2 PBPI, [66] and other tools, [67,68] and deep learning-based tools as shown in Table 2, such as pDeep, [57,69] Prosit, [29] DeepMass:Prism, [45] MS 2 CNN, [70] DeepDIA, [30] Predfull [56] and the model proposed in Guan et. al [46] (Figure 2).…”

Section: Deep Learning For Ms/ms Spectrum Predictionmentioning

confidence: 99%

Deep Learning in Proteomics

et al. 2020

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Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

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Prediction of Peptide Fragment Ion Mass Spectra by Data Mining Techniques

Cited by 16 publications

References 59 publications

A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics

A Comprehensive Evaluation of MS/MS Spectrum Prediction Tools for Shotgun Proteomics

Improving Peptide-Spectrum Matching by Fragmentation Prediction Using Hidden Markov Models

Deep Learning in Proteomics

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