Mobility modeling of peptides in capillary electrophoresis

S. Mittermayr, M. Olajos, T. Chovan, G. K. Bonn, A. Guttman

Research output: Contribution to journalArticle

27 Citations (Scopus)

Abstract

Recent rapid developments in proteomics require high-resolution separation of a large number of peptides for their downstream identification by mass spectrometry. Capillary electrophoresis (CE) is an electric-field-mediated bioanalytical technique capable of rapid, high-resolution separation of very complex sample mixtures. Development of CE methods for adequate separation of a large number of peptides is usually a time-consuming task. Application of model-based approaches to predict peptide mobilities in CE from known physicochemical properties can shorten tedious experimental optimization of separation. This endeavor requires specification of structural descriptors followed by selection of appropriate modeling methods. To date, numerous theoretical predictive models have been developed, mostly based on Stokes' Law to relate peptide mobilities to structural properties (e.g., charge and size). However, these two-variable models could not successfully predict electrophoretic mobilities for all categories of peptides with a reasonable degree of accuracy. To address the shortcomings of the two-variable models, new strategies were recently introduced, including the usage of additional peptide descriptors or applying non-linear modeling (e.g., artificial neural networks), to attain more accurate, robust prediction. Effective application of machine-learning techniques to the development of predictive models has consolidated conjecture on non-linear relationships between peptide structural descriptors and their electrophoretic mobilities. In this article, we review recent advances in CE mobility modeling of peptides, particularly in respect to predicting optimal separation conditions for the analysis of highly complex peptide mixtures in proteomics applications.

Original languageEnglish
Pages (from-to)407-417
Number of pages11
JournalTrAC - Trends in Analytical Chemistry
Volume27
Issue number5
DOIs
Publication statusPublished - May 2008

Fingerprint

Capillary electrophoresis
Peptides
Electrophoretic mobility
Complex Mixtures
Mass spectrometry
Learning systems
Structural properties
Electric fields
Neural networks
Specifications

Keywords

  • Artificial neural network
  • Capillary electrophoresis
  • Electrophoretic mobility
  • Machine-learning
  • Mobility modeling
  • Multi-variable modeling
  • Peptide
  • Proteomics
  • Semi-empirical modeling

ASJC Scopus subject areas

  • Analytical Chemistry

Cite this

Mobility modeling of peptides in capillary electrophoresis. / Mittermayr, S.; Olajos, M.; Chovan, T.; Bonn, G. K.; Guttman, A.

In: TrAC - Trends in Analytical Chemistry, Vol. 27, No. 5, 05.2008, p. 407-417.

Research output: Contribution to journalArticle

Mittermayr, S. ; Olajos, M. ; Chovan, T. ; Bonn, G. K. ; Guttman, A. / Mobility modeling of peptides in capillary electrophoresis. In: TrAC - Trends in Analytical Chemistry. 2008 ; Vol. 27, No. 5. pp. 407-417.
@article{f736ab13eacc4ac7bd833996bc4d72b2,
title = "Mobility modeling of peptides in capillary electrophoresis",
abstract = "Recent rapid developments in proteomics require high-resolution separation of a large number of peptides for their downstream identification by mass spectrometry. Capillary electrophoresis (CE) is an electric-field-mediated bioanalytical technique capable of rapid, high-resolution separation of very complex sample mixtures. Development of CE methods for adequate separation of a large number of peptides is usually a time-consuming task. Application of model-based approaches to predict peptide mobilities in CE from known physicochemical properties can shorten tedious experimental optimization of separation. This endeavor requires specification of structural descriptors followed by selection of appropriate modeling methods. To date, numerous theoretical predictive models have been developed, mostly based on Stokes' Law to relate peptide mobilities to structural properties (e.g., charge and size). However, these two-variable models could not successfully predict electrophoretic mobilities for all categories of peptides with a reasonable degree of accuracy. To address the shortcomings of the two-variable models, new strategies were recently introduced, including the usage of additional peptide descriptors or applying non-linear modeling (e.g., artificial neural networks), to attain more accurate, robust prediction. Effective application of machine-learning techniques to the development of predictive models has consolidated conjecture on non-linear relationships between peptide structural descriptors and their electrophoretic mobilities. In this article, we review recent advances in CE mobility modeling of peptides, particularly in respect to predicting optimal separation conditions for the analysis of highly complex peptide mixtures in proteomics applications.",
keywords = "Artificial neural network, Capillary electrophoresis, Electrophoretic mobility, Machine-learning, Mobility modeling, Multi-variable modeling, Peptide, Proteomics, Semi-empirical modeling",
author = "S. Mittermayr and M. Olajos and T. Chovan and Bonn, {G. K.} and A. Guttman",
year = "2008",
month = "5",
doi = "10.1016/j.trac.2008.03.010",
language = "English",
volume = "27",
pages = "407--417",
journal = "TrAC - Trends in Analytical Chemistry",
issn = "0165-9936",
publisher = "Elsevier",
number = "5",

}

TY - JOUR

T1 - Mobility modeling of peptides in capillary electrophoresis

AU - Mittermayr, S.

AU - Olajos, M.

AU - Chovan, T.

AU - Bonn, G. K.

AU - Guttman, A.

PY - 2008/5

Y1 - 2008/5

N2 - Recent rapid developments in proteomics require high-resolution separation of a large number of peptides for their downstream identification by mass spectrometry. Capillary electrophoresis (CE) is an electric-field-mediated bioanalytical technique capable of rapid, high-resolution separation of very complex sample mixtures. Development of CE methods for adequate separation of a large number of peptides is usually a time-consuming task. Application of model-based approaches to predict peptide mobilities in CE from known physicochemical properties can shorten tedious experimental optimization of separation. This endeavor requires specification of structural descriptors followed by selection of appropriate modeling methods. To date, numerous theoretical predictive models have been developed, mostly based on Stokes' Law to relate peptide mobilities to structural properties (e.g., charge and size). However, these two-variable models could not successfully predict electrophoretic mobilities for all categories of peptides with a reasonable degree of accuracy. To address the shortcomings of the two-variable models, new strategies were recently introduced, including the usage of additional peptide descriptors or applying non-linear modeling (e.g., artificial neural networks), to attain more accurate, robust prediction. Effective application of machine-learning techniques to the development of predictive models has consolidated conjecture on non-linear relationships between peptide structural descriptors and their electrophoretic mobilities. In this article, we review recent advances in CE mobility modeling of peptides, particularly in respect to predicting optimal separation conditions for the analysis of highly complex peptide mixtures in proteomics applications.

AB - Recent rapid developments in proteomics require high-resolution separation of a large number of peptides for their downstream identification by mass spectrometry. Capillary electrophoresis (CE) is an electric-field-mediated bioanalytical technique capable of rapid, high-resolution separation of very complex sample mixtures. Development of CE methods for adequate separation of a large number of peptides is usually a time-consuming task. Application of model-based approaches to predict peptide mobilities in CE from known physicochemical properties can shorten tedious experimental optimization of separation. This endeavor requires specification of structural descriptors followed by selection of appropriate modeling methods. To date, numerous theoretical predictive models have been developed, mostly based on Stokes' Law to relate peptide mobilities to structural properties (e.g., charge and size). However, these two-variable models could not successfully predict electrophoretic mobilities for all categories of peptides with a reasonable degree of accuracy. To address the shortcomings of the two-variable models, new strategies were recently introduced, including the usage of additional peptide descriptors or applying non-linear modeling (e.g., artificial neural networks), to attain more accurate, robust prediction. Effective application of machine-learning techniques to the development of predictive models has consolidated conjecture on non-linear relationships between peptide structural descriptors and their electrophoretic mobilities. In this article, we review recent advances in CE mobility modeling of peptides, particularly in respect to predicting optimal separation conditions for the analysis of highly complex peptide mixtures in proteomics applications.

KW - Artificial neural network

KW - Capillary electrophoresis

KW - Electrophoretic mobility

KW - Machine-learning

KW - Mobility modeling

KW - Multi-variable modeling

KW - Peptide

KW - Proteomics

KW - Semi-empirical modeling

UR - http://www.scopus.com/inward/record.url?scp=44649168806&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44649168806&partnerID=8YFLogxK

U2 - 10.1016/j.trac.2008.03.010

DO - 10.1016/j.trac.2008.03.010

M3 - Article

VL - 27

SP - 407

EP - 417

JO - TrAC - Trends in Analytical Chemistry

JF - TrAC - Trends in Analytical Chemistry

SN - 0165-9936

IS - 5

ER -