{"id":{"repo_id":"ncsu","oai_identifier":"oai:repository.lib.ncsu.edu:1840.16/1563"},"canonical_url":"https://search.dev.ndltd.org/etd/ncsu/oai:repository.lib.ncsu.edu:1840.16/1563","repository":{"repo_id":"ncsu","name":"North Carolina State University","base_url":"https://repository.lib.ncsu.edu/server/oai/request"},"display":{"title":"Prediction of Peptide Maps in CZE and MEKC Systems","abstract":"A new Quantitative Structure-Migration Relationships(QSMR) model was developed to predict the electrophoretic mobilities of peptides in capillary zone electrophoresis(CZE). A three-step strategy was used: first, select the best charge-size term from the existing models; second, develop a muilti-linear regression(MLR) model to study the linear characteristics of peptide mobility using the best charge-size term and other descriptors; third, generate an artificial neural network(ANN) to investigate the nonlinear behavior of peptide mobility and use this ANN model to predict peptide migration behavior in CZE. To test the robustness of the QSMR model, it was applied to the data published by another research group. Very accurate predictions were achieved. To study the influence of peptide sequence on the migration of a peptide in CZE, a series of 'sequence-related' descriptors were developed. These descriptors were used to develop MLR models for peptide mobility prediction. With the 'sequence-related' descriptor, more accurate mobility could be predicted for peptides with same amino acid composition but different sequences. Group contribution approach(GCA) was used to determine the individual contribution of each amino acid residue and both N-, C- terminal to the peptide mobility in Tween20 system. Data of a relatively small number of peptides were used for this purpose. The sum of individual contributions was calculated for each peptide and used as a new descriptor in developing MLR models for the prediction of peptide mobilities in Tween20 system. Good preliminary results were achieved.","abstract_html":"A new Quantitative Structure-Migration Relationships(QSMR) model was developed to predict the electrophoretic mobilities of peptides in capillary zone electrophoresis(CZE). A three-step strategy was used: first, select the best charge-size term from the existing models; second, develop a muilti-linear regression(MLR) model to study the linear characteristics of peptide mobility using the best charge-size term and other descriptors; third, generate an artificial neural network(ANN) to investigate the nonlinear behavior of peptide mobility and use this ANN model to predict peptide migration behavior in CZE. To test the robustness of the QSMR model, it was applied to the data published by another research group. Very accurate predictions were achieved. To study the influence of peptide sequence on the migration of a peptide in CZE, a series of 'sequence-related' descriptors were developed. These descriptors were used to develop MLR models for peptide mobility prediction. With the 'sequence-related' descriptor, more accurate mobility could be predicted for peptides with same amino acid composition but different sequences. Group contribution approach(GCA) was used to determine the individual contribution of each amino acid residue and both N-, C- terminal to the peptide mobility in Tween20 system. Data of a relatively small number of peptides were used for this purpose. The sum of individual contributions was calculated for each peptide and used as a new descriptor in developing MLR models for the prediction of peptide mobilities in Tween20 system. Good preliminary results were achieved.","abstract_has_math":false,"creators":["Shen, Yang"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Edmond F. Bowden, Committee Member","Charles B. Boss, Committee Member","Morteza G. Khaledi, Committee Chair"],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-04-24","date_published":"2005-04-24","updated_at":"2026-08-21T22:21:56Z","subjects":["peptide mapping separation prediction"],"languages":[],"rights":["I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-01242005-145443"],"render_values":[{"text":"etd-01242005-145443","href":null,"code":true}]}]},"links":{"outbound_url":"http://www.lib.ncsu.edu/resolver/1840.16/1563","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://repository.lib.ncsu.edu/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Arepository.lib.ncsu.edu%3A1840.16%2F1563","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Edmond F. Bowden, Committee Member","Charles B. Boss, Committee Member","Morteza G. 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I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-01242005-145443"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://www.lib.ncsu.edu/resolver/1840.16/1563"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["North Carolina State University Theses Chemistry."]},{"key":"dc:description.abstract","label":"Abstract","values":["A new Quantitative Structure-Migration Relationships(QSMR) model was developed to predict the electrophoretic mobilities of peptides in capillary zone electrophoresis(CZE). A three-step strategy was used: first, select the best charge-size term from the existing models; second, develop a muilti-linear regression(MLR) model to study the linear characteristics of peptide mobility using the best charge-size term and other descriptors; third, generate an artificial neural network(ANN) to investigate the nonlinear behavior of peptide mobility and use this ANN model to predict peptide migration behavior in CZE. To test the robustness of the QSMR model, it was applied to the data published by another research group. Very accurate predictions were achieved. To study the influence of peptide sequence on the migration of a peptide in CZE, a series of 'sequence-related' descriptors were developed. These descriptors were used to develop MLR models for peptide mobility prediction. With the 'sequence-related' descriptor, more accurate mobility could be predicted for peptides with same amino acid composition but different sequences. Group contribution approach(GCA) was used to determine the individual contribution of each amino acid residue and both N-, C- terminal to the peptide mobility in Tween20 system. Data of a relatively small number of peptides were used for this purpose. The sum of individual contributions was calculated for each peptide and used as a new descriptor in developing MLR models for the prediction of peptide mobilities in Tween20 system. Good preliminary results were achieved."]},{"key":"dc:format","label":"Dc Format","values":["Thesis (M.S.)--North Carolina State University."]},{"key":"dc:title","label":"Title","values":["Prediction of Peptide Maps in CZE and MEKC Systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Edmond F. Bowden, Committee Member","Charles B. Boss, Committee Member","Morteza G. Khaledi, Committee Chair"],"dc:creator":["Shen, Yang"],"dc:date.accessioned":["2010-04-02T18:05:08Z"],"dc:date.available":["2010-04-02T18:05:08Z"],"dc:date.issued":["2005-04-24"],"dc:description":["North Carolina State University Theses Chemistry."],"dc:description.abstract":["A new Quantitative Structure-Migration Relationships(QSMR) model was developed to predict the electrophoretic mobilities of peptides in capillary zone electrophoresis(CZE). A three-step strategy was used: first, select the best charge-size term from the existing models; second, develop a muilti-linear regression(MLR) model to study the linear characteristics of peptide mobility using the best charge-size term and other descriptors; third, generate an artificial neural network(ANN) to investigate the nonlinear behavior of peptide mobility and use this ANN model to predict peptide migration behavior in CZE. To test the robustness of the QSMR model, it was applied to the data published by another research group. Very accurate predictions were achieved. To study the influence of peptide sequence on the migration of a peptide in CZE, a series of 'sequence-related' descriptors were developed. These descriptors were used to develop MLR models for peptide mobility prediction. With the 'sequence-related' descriptor, more accurate mobility could be predicted for peptides with same amino acid composition but different sequences. Group contribution approach(GCA) was used to determine the individual contribution of each amino acid residue and both N-, C- terminal to the peptide mobility in Tween20 system. Data of a relatively small number of peptides were used for this purpose. The sum of individual contributions was calculated for each peptide and used as a new descriptor in developing MLR models for the prediction of peptide mobilities in Tween20 system. Good preliminary results were achieved."],"dc:format":["Thesis (M.S.)--North Carolina State University."],"dc:identifier.other":["etd-01242005-145443"],"dc:identifier.uri":["http://www.lib.ncsu.edu/resolver/1840.16/1563"],"dc:rights":["I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."],"dc:subject":["peptide mapping separation prediction"],"dc:title":["Prediction of Peptide Maps in CZE and MEKC Systems"]},"updated_at":"2026-08-21T22:21:56Z"}