Classical normal mode analysis (CNMA) has been widely acknowledged as one of the most useful simulation tools for studying protein dynamics. CNMA uses a fine-grained all-atom model of proteins and a complex empirical potential. In addition, CNMA requires a structure that must be energetically minimized, which makes the method cumbersome to use, especially for large proteins. In contrast, elastic network models (ENM) use coarse-grained protein models and adopt a simplified potential function. ENM is much faster than CNMA but is less accurate. To take the advantages of both CNMA and ENM, the spring-based normal mode analysis (sbNMA) was developed. It uses a fine-grained all-atom model for proteins and an all-atom empirical force field to maintain accuracy while reducing the computing complexity by eliminating the minimization step. In the previous work on sbNMA, only the CHARMM force field was explored. In this work, we extend the analyses to AMBER, another widely-used force field. We investigate the dependence of sbNMA's performance on force fields. This work provides also insightful understandings of the differences between CHARMM and AMBER.
","abstract_html":"<p>Classical normal mode analysis (CNMA) has been widely acknowledged as one of the most useful simulation tools for studying protein dynamics. CNMA uses a fine-grained all-atom model of proteins and a complex empirical potential. In addition, CNMA requires a structure that must be energetically minimized, which makes the method cumbersome to use, especially for large proteins. In contrast, elastic network models (ENM) use coarse-grained protein models and adopt a simplified potential function. ENM is much faster than CNMA but is less accurate. To take the advantages of both CNMA and ENM, the spring-based normal mode analysis (sbNMA) was developed. It uses a fine-grained all-atom model for proteins and an all-atom empirical force field to maintain accuracy while reducing the computing complexity by eliminating the minimization step. In the previous work on sbNMA, only the CHARMM force field was explored. In this work, we extend the analyses to AMBER, another widely-used force field. We investigate the dependence of sbNMA's performance on force fields. This work provides also insightful understandings of the differences between CHARMM and AMBER.</p>","abstract_has_math":false,"creators":["Song, Jaekyun"],"institution":null,"degree_name":"Master of Science","degree_level":"thesis","degree_discipline":"Computer Science","degree_department":"Department of Computer Science","school":null,"contributors":[],"advisors":["Guang Song"],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01","date_published":"2016-01-01","updated_at":"2026-07-24T02:39:53Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.31274/etd-180810-5440"],"render_values":[{"text":"https://doi.org/10.31274/etd-180810-5440","href":"https://doi.org/10.31274/etd-180810-5440","code":true}]},{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/etd/15812/"],"render_values":[{"text":"archive/lib.dr.iastate.edu/etd/15812/","href":null,"code":true}]}]},"links":{"outbound_url":"https://dr.lib.iastate.edu/handle/20.500.12876/29995","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Guang Song"]},{"key":"dc:contributor.department","label":"Department","values":["Department of Computer Science"]},{"key":"dc:creator","label":"Author","values":["Song, Jaekyun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-08-11T14:48:42.000"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-06-30T03:06:37Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-06-30T03:06:37Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-01-01"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["archive/lib.dr.iastate.edu/etd/15812/"]},{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.31274/etd-180810-5440"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://dr.lib.iastate.edu/handle/20.500.12876/29995"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Classical normal mode analysis (CNMA) has been widely acknowledged as one of the most useful simulation tools for studying protein dynamics. CNMA uses a fine-grained all-atom model of proteins and a complex empirical potential. In addition, CNMA requires a structure that must be energetically minimized, which makes the method cumbersome to use, especially for large proteins. In contrast, elastic network models (ENM) use coarse-grained protein models and adopt a simplified potential function. ENM is much faster than CNMA but is less accurate. To take the advantages of both CNMA and ENM, the spring-based normal mode analysis (sbNMA) was developed. It uses a fine-grained all-atom model for proteins and an all-atom empirical force field to maintain accuracy while reducing the computing complexity by eliminating the minimization step. In the previous work on sbNMA, only the CHARMM force field was explored. In this work, we extend the analyses to AMBER, another widely-used force field. We investigate the dependence of sbNMA's performance on force fields. This work provides also insightful understandings of the differences between CHARMM and AMBER.
"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Investigating the effects of different force fields on spring-based normal mode analysis"]}]}],"canonical_facts":{"dc:contributor.advisor":["Guang Song"],"dc:contributor.department":["Department of Computer Science"],"dc:creator":["Song, Jaekyun"],"dc:date":["2018-08-11T14:48:42.000"],"dc:date.accessioned":["2020-06-30T03:06:37Z"],"dc:date.available":["2020-06-30T03:06:37Z"],"dc:date.issued":["2016-01-01"],"dc:description.abstract":["Classical normal mode analysis (CNMA) has been widely acknowledged as one of the most useful simulation tools for studying protein dynamics. CNMA uses a fine-grained all-atom model of proteins and a complex empirical potential. In addition, CNMA requires a structure that must be energetically minimized, which makes the method cumbersome to use, especially for large proteins. In contrast, elastic network models (ENM) use coarse-grained protein models and adopt a simplified potential function. ENM is much faster than CNMA but is less accurate. To take the advantages of both CNMA and ENM, the spring-based normal mode analysis (sbNMA) was developed. It uses a fine-grained all-atom model for proteins and an all-atom empirical force field to maintain accuracy while reducing the computing complexity by eliminating the minimization step. In the previous work on sbNMA, only the CHARMM force field was explored. In this work, we extend the analyses to AMBER, another widely-used force field. We investigate the dependence of sbNMA's performance on force fields. This work provides also insightful understandings of the differences between CHARMM and AMBER.
"],"dc:format.mimetype":["application/pdf"],"dc:identifier":["archive/lib.dr.iastate.edu/etd/15812/"],"dc:identifier.doi":["https://doi.org/10.31274/etd-180810-5440"],"dc:identifier.uri":["https://dr.lib.iastate.edu/handle/20.500.12876/29995"],"dc:language.iso":["en"],"dc:title":["Investigating the effects of different force fields on spring-based normal mode analysis"],"dc:type":["thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T02:39:53Z"}