Current Status of Computational Approaches for Protein Identification Using Tandem Mass Spectra

Zhang, Xiang; Oh, Cheolhwan; Riley, Catherine P.; Buck, C. Loren

doi:10.2174/157016407783221349

Cited by 6 publications

(6 citation statements)

References 66 publications

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“…Many computational approaches have been proposed to analyze and process experimental data generated from MS for proteomics research [ 22 , 32 ]. Among these techniques, ANN-based methods are good choices for their capability of deriving useful information from complicated or imprecise data without the need of a detailed understanding of the underlying phenomena [ 23 , 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…Peptide selection, fractionation and separation on chromatographic columns may be modelled with various methods. We have developed an algorithm to predict the fractionation of peptides in strong anion exchange (SAX) chromatography using a pattern classification technique based on artificial neural network (ANN) method [ 22 , 23 ]. An ANN has also been used to predict peptide separation in reversed phase (RP) chromatography [ 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry

et al. 2010

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BackgroundThere is an increasing usage of ion mobility-mass spectrometry (IMMS) in proteomics. IMMS combines the features of ion mobility spectrometry (IMS) and mass spectrometry (MS). It separates and detects peptide ions on a millisecond time-scale. IMS separates peptide ions based on drift time that is determined by the collision cross-section of each peptide ion in a given experiment condition. A peptide ion's collision cross-section is related to the ion size and shape resulted from the peptide amino acid sequence and their modifications. This inherent relation between the drift time of peptide ion and peptide sequence indicates that the drift time of peptide ions can be used to infer peptide sequence and therefore, for peptide identification.ResultsThis paper describes an artificial neural networks (ANNs) regression model for the prediction of peptide ion drift time in IMMS. Each peptide in this work was represented using three descriptors (i.e., molecular weight, sequence length and a two-dimensional sequence index). An ANN predictor consisting of four input nodes, three hidden nodes and one output node was constructed for peptide ion drift time prediction. For the model training and testing, a 10-fold cross-validation strategy was employed for three datasets each containing different charge states. Dataset one contains 212 singly-charged peptide ions, dataset two has 306 doubly-charged peptide ions, and dataset three has 77 triply-charged peptide ions. Our proposed method achieved 94.4%, 93.6% and 74.2% prediction accuracy for singly-, doubly- and triply-charged peptide ions, respectively.ConclusionsAn ANN-based method has been developed for predicting the drift time of peptide ions in IMMS. The results achieved here demonstrate the effectiveness and efficiency of the prediction model. This work can enhance the confidence of protein identification by combining with current database search approaches for protein identification.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry

et al. 2010

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“…To identify the most effective predictor, we have investigated 12 different classifiers that performed outstandingly in numerous biological quandaries [39], [43], [57], [60], [70]- [80]. These classifiers are: Extreme Gradient Boosting (XGBoost) [39], Adaptive Boosting (AdaBoost) [43], Support Vector Machine (SVM) [57], [60], Random Forest (RF) [70], [71], Light Gradient Boosting Machine (Light-GBM) [72], [73], Linear Discriminant Analysis (LDA) [74], Quadratic Discriminant Analysis (QDA) [75], Bootstrap Aggregating (Bagging) [76], Decision Tree (DT) [77], TABLE 3. Performance comparison between our proposed method and MaloPred [37], kmal-sp [39] for predicting the malonylation sites of the individual species (Homo sapiens, Mus musculus) and total species (six species) based on the independent test.…”

Section: E Classification Algorithmmentioning

confidence: 99%

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

et al. 2020

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Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/) INDEX TERMS Cluster centroid based majority under-sampling technique, evolutionary information, light gradient boosting, lysine malonylation, machine learning, post translational modifications.

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“…However, the use of probability-based or statistical methods in current algorithms is very restrictive, and most of these ultimately take a heuristic approach. McHugh and Arthur6 , 7 recognize the limiting factor in current protein identification methods as a trade-off between false positives and false negatives.…”

Section: Introductionmentioning

confidence: 99%

Multivariate approach for protein identification based on mass spectrometric data

Lee

2012

Stat Methods Med Res

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Protein mass spectrometry provides a powerful tool for detecting and identifying proteins. Several database searching algorithms may be used for this purpose. However, most of them depend on the heuristic approaches and the use of probability-based or statistical approach was very restrictive in the current algorithms. In this study, we present a statistical modelling of scores based on a generalized linear mixed model and provide a feasible computation method using penalized generalized weighted least squares. This model incorporates the dependency among matches into a new statistical scoring function, and uses the beta-binomial distribution to derive the score. Based on simulation experiments and analysis using real examples, we have improved protein searching performance and provided feasible computation procedures to deal with very large datasets. In particular, our methods may significantly increase accuracy in identifying medium and small proteins.

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Current Status of Computational Approaches for Protein Identification Using Tandem Mass Spectra

Cited by 6 publications

References 66 publications

Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry

Artificial neural networks for the prediction of peptide drift time in ion mobility mass spectrometry

Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features

Multivariate approach for protein identification based on mass spectrometric data

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