High-Accuracy Peptide Mass Fingerprinting Using Peak Intensity Data with Machine Learning

Yang, Dongmei; Ramkissoon, Kevin; Hamlett, Eric D.; Giddings, Morgan C.

doi:10.1021/pr070088g

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…The confidence interval (95% of confidence level) is better for the SVM prediction compares to the values from the Cargile and Bjellqvist methods. The use of SVM algorithms and machine learning techniques in general, give the possibility to find a new model to predict the isoelectric point given some background knowledge (reliable identifications) from all fractions [20][21][22][23]. Another published dataset was also used to demonstrate that the model can predict accurately across diverse dataset and experimental settings.…”

mentioning

confidence: 99%

Isoelectric point optimization using peptide descriptors and support vector machines

Perez‐Riverol

Audain

Millan

et al. 2012

Journal of Proteomics

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mentioning

confidence: 99%

Isoelectric point optimization using peptide descriptors and support vector machines

Perez‐Riverol

Audain

Millan

et al. 2012

Journal of Proteomics

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“…Each species of peptide has a different ionisation efficiency and hence a different abundance/intensity relationship. Towards the goal of absolute quantification 30 and the revitalisation of PMF protein identification 31, correlations between ionisation efficiency and amino acid sequence have recently been investigated.…”

Section: Introductionmentioning

confidence: 99%

Image analysis tools and emerging algorithms for expression proteomics

et al. 2010

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Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-D Gel Electrophoresis (2-DE) technique of protein separation, and by first covering signal analysis for Mass Spectrometry (MS), we also explain the current image analysis workflow for the emerging high-throughput ‘shotgun’ proteomics platform of Liquid Chromatography coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whilst existing commercial and academic packages and their workflows are described from both a user’s and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

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“…Unfortunately, many of the proteins listed in Tables 2 and 3 were not available, and we were not able to evaluate the exact amounts by combining with the results of QCM-D. But fortunately, there have been several works that attempted to predict peak intensities of peptides from their sequences by using techniques of informatics [42][43][44]. These approaches may realize the exact evaluation of the protein composition in the future.…”

Section: Resultsmentioning

confidence: 99%

Proteomic Analysis of Biomaterial Surfaces after Contacting with Body Fluids by MALDI-ToF Mass Spectroscopy

et al. 2019

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We developed a method to identify proteins adsorbed on solid surfaces from a solution containing a complex mixture of proteins by using Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass (MALDI-ToF mass) spectroscopy. In the method, we performed all procedures of peptide mass fingerprint method including denaturation, reduction, alkylation, digestion, and spotting of matrix on substrates. The method enabled us to avoid artifacts of pipetting that could induce changes in the composition. We also developed an algorithm to identify the adsorbed proteins. In this work, we demonstrate the identification of proteins adsorbed on self-assembled monolayers (SAMs). Our results show that the composition of proteins on the SAMs critically depends on the terminal groups of the molecules constituting the SAMs, indicating that the competitive adsorption of protein molecules is largely affected by protein-surface interaction. The method introduced here can provide vital information to clarify the mechanism underlying the responses of cells and tissues to biomaterials.

show abstract

High-Accuracy Peptide Mass Fingerprinting Using Peak Intensity Data with Machine Learning

Cited by 11 publications

References 18 publications

Isoelectric point optimization using peptide descriptors and support vector machines

Isoelectric point optimization using peptide descriptors and support vector machines

Image analysis tools and emerging algorithms for expression proteomics

Proteomic Analysis of Biomaterial Surfaces after Contacting with Body Fluids by MALDI-ToF Mass Spectroscopy

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