Analyzing single-molecule manipulation experiments
Single‐molecule manipulation studies can provide quantitative information about the physical properties of complex biological molecules without ensemble artifacts obscuring the measurements. We demonstrate computational techniques which aim at more fully utilizing the wealth of information contained...
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| Veröffentlicht in: | Journal of molecular recognition 2009-09, Vol.22 (5), p.356-362 |
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| container_end_page | 362 |
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| container_title | Journal of molecular recognition |
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| creator | Calderon, Christopher P. Harris, Nolan C. Kiang, Ching-Hwa Cox, Dennis D. |
| description | Single‐molecule manipulation studies can provide quantitative information about the physical properties of complex biological molecules without ensemble artifacts obscuring the measurements. We demonstrate computational techniques which aim at more fully utilizing the wealth of information contained in noisy experimental time series. The “noise” comes from multiple sources e.g., inherent thermal motion, instrument measurement error, etc. The primary focus of this paper is a methodology that uses time domain based methods to extract the effective molecular friction from single‐molecule pulling data. We studied molecules composed of eight tandem repeat titin I27 domains, but the modeling approaches have applicability to other single‐molecule mechanical studies. The merits and challenges associated with applying such a computational approach to existing single‐molecule manipulation data are also discussed. Copyright © 2009 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/jmr.959 |
| format | Article |
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We demonstrate computational techniques which aim at more fully utilizing the wealth of information contained in noisy experimental time series. The “noise” comes from multiple sources e.g., inherent thermal motion, instrument measurement error, etc. The primary focus of this paper is a methodology that uses time domain based methods to extract the effective molecular friction from single‐molecule pulling data. We studied molecules composed of eight tandem repeat titin I27 domains, but the modeling approaches have applicability to other single‐molecule mechanical studies. The merits and challenges associated with applying such a computational approach to existing single‐molecule manipulation data are also discussed. 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Mol. Recognit</addtitle><description>Single‐molecule manipulation studies can provide quantitative information about the physical properties of complex biological molecules without ensemble artifacts obscuring the measurements. We demonstrate computational techniques which aim at more fully utilizing the wealth of information contained in noisy experimental time series. The “noise” comes from multiple sources e.g., inherent thermal motion, instrument measurement error, etc. The primary focus of this paper is a methodology that uses time domain based methods to extract the effective molecular friction from single‐molecule pulling data. We studied molecules composed of eight tandem repeat titin I27 domains, but the modeling approaches have applicability to other single‐molecule mechanical studies. The merits and challenges associated with applying such a computational approach to existing single‐molecule manipulation data are also discussed. Copyright © 2009 John Wiley & Sons, Ltd.</description><subject>Computation</subject><subject>Computer Simulation</subject><subject>Connectin</subject><subject>effective friction</subject><subject>Error analysis</subject><subject>Friction</subject><subject>Humans</subject><subject>Information dissemination</subject><subject>Likelihood Functions</subject><subject>local maximum likelihood</subject><subject>Mathematical models</subject><subject>Microscopy, Atomic Force</subject><subject>Molecular Conformation</subject><subject>Muscle Proteins - chemistry</subject><subject>Physical properties</subject><subject>Protein Kinases - chemistry</subject><subject>Recognition</subject><subject>single-molecule manipulation</subject><subject>stochastic differential equation</subject><subject>Time series</subject><issn>0952-3499</issn><issn>1099-1352</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkUtvEzEURi1ERUNB_AOUFSChKdfv3A1SVaAPSpF4iKXlOHeKi2cmjDPQ8OvraKICC-jGXtyj4-vvY-wRh30OIF5cNv0-arzDJhwQKy61uMsmgFpUUiHusvs5XwKUmYZ7bJejsmiVnTBx0Pq0_hXbi2kuR6Kq6RKFIdG08W1cDsmvYtdO6WpJfWyoXeUHbKf2KdPD7b3HPr95_enwuDp7f3RyeHBWBQMCK5K15WJRz7GmsoghEtwujKrRSGkMUJgLCkjaALdGoQ81BGtIeQFywUHusZejdznMG1qE8nbvk1uWNXy_dp2P7u9JG7-6i-6HE9aAkhvBk62g774PlFeuiTlQSr6lbsjOWD0rMYhbQalmiBz0raAANFqrDfjsvyCfARQpl7agT0c09F3OPdU3P-TgNuW6Uq4r5Rby8Z-B_Oa2bRbg-Qj8jInW__K403cfRl010jGv6OqG9v23koy02n05P3Lnr061eGs-OpDXdzm8tw</recordid><startdate>200909</startdate><enddate>200909</enddate><creator>Calderon, Christopher P.</creator><creator>Harris, Nolan C.</creator><creator>Kiang, Ching-Hwa</creator><creator>Cox, Dennis D.</creator><general>John Wiley & Sons, Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>7QO</scope><scope>FR3</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>200909</creationdate><title>Analyzing single-molecule manipulation experiments</title><author>Calderon, Christopher P. ; Harris, Nolan C. ; Kiang, Ching-Hwa ; Cox, Dennis D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c6029-e3f712dfb9fe0956ee217d64f9633660ecb2ec9e56017649acf0c76e4a203d103</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Computation</topic><topic>Computer Simulation</topic><topic>Connectin</topic><topic>effective friction</topic><topic>Error analysis</topic><topic>Friction</topic><topic>Humans</topic><topic>Information dissemination</topic><topic>Likelihood Functions</topic><topic>local maximum likelihood</topic><topic>Mathematical models</topic><topic>Microscopy, Atomic Force</topic><topic>Molecular Conformation</topic><topic>Muscle Proteins - chemistry</topic><topic>Physical properties</topic><topic>Protein Kinases - chemistry</topic><topic>Recognition</topic><topic>single-molecule manipulation</topic><topic>stochastic differential equation</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Calderon, Christopher P.</creatorcontrib><creatorcontrib>Harris, Nolan C.</creatorcontrib><creatorcontrib>Kiang, Ching-Hwa</creatorcontrib><creatorcontrib>Cox, Dennis D.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biotechnology Research Abstracts</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of molecular recognition</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Calderon, Christopher P.</au><au>Harris, Nolan C.</au><au>Kiang, Ching-Hwa</au><au>Cox, Dennis D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analyzing single-molecule manipulation experiments</atitle><jtitle>Journal of molecular recognition</jtitle><addtitle>J. Mol. Recognit</addtitle><date>2009-09</date><risdate>2009</risdate><volume>22</volume><issue>5</issue><spage>356</spage><epage>362</epage><pages>356-362</pages><issn>0952-3499</issn><eissn>1099-1352</eissn><abstract>Single‐molecule manipulation studies can provide quantitative information about the physical properties of complex biological molecules without ensemble artifacts obscuring the measurements. We demonstrate computational techniques which aim at more fully utilizing the wealth of information contained in noisy experimental time series. The “noise” comes from multiple sources e.g., inherent thermal motion, instrument measurement error, etc. The primary focus of this paper is a methodology that uses time domain based methods to extract the effective molecular friction from single‐molecule pulling data. We studied molecules composed of eight tandem repeat titin I27 domains, but the modeling approaches have applicability to other single‐molecule mechanical studies. 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| ispartof | Journal of molecular recognition, 2009-09, Vol.22 (5), p.356-362 |
| issn | 0952-3499 1099-1352 |
| language | eng |
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| source | MEDLINE; Wiley Online Library All Journals |
| subjects | Computation Computer Simulation Connectin effective friction Error analysis Friction Humans Information dissemination Likelihood Functions local maximum likelihood Mathematical models Microscopy, Atomic Force Molecular Conformation Muscle Proteins - chemistry Physical properties Protein Kinases - chemistry Recognition single-molecule manipulation stochastic differential equation Time series |
| title | Analyzing single-molecule manipulation experiments |
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