Please use this identifier to cite or link to this item: https://doi.org/10.1007/11732990_34
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dc.titlePredicting experimental quantities in protein folding kinetics using stochastic roadmap simulation
dc.contributor.authorChiang, T.-H.
dc.contributor.authorApaydin, M.S.
dc.contributor.authorBrutlag, D.L.
dc.contributor.authorHsu, D.
dc.contributor.authorLatombe, J.-C.
dc.date.accessioned2013-07-04T07:57:55Z
dc.date.available2013-07-04T07:57:55Z
dc.date.issued2006
dc.identifier.citationChiang, T.-H.,Apaydin, M.S.,Brutlag, D.L.,Hsu, D.,Latombe, J.-C. (2006). Predicting experimental quantities in protein folding kinetics using stochastic roadmap simulation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3909 LNBI : 410-424. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/11732990_34" target="_blank">https://doi.org/10.1007/11732990_34</a>
dc.identifier.isbn3540332952
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40156
dc.description.abstractThis paper presents a new method for studying protein folding kinetics. It uses the recently introduced Stochastic Roadmap Simulation (SRS) method to estimate the transition state ensemble (TSE) and predict the rates and Φ-values for protein folding. The new method was tested on 16 proteins. Comparison with experimental data shows that it estimates the TSE much more accurately than an existing method based on dynamic programming. This leads to better folding-rate predictions. The results on Φ-value predictions are mixed, possibly due to the simple energy model used in the tests. This is the first time that results obtained from SRS have been compared against a substantial amount of experimental data. The success further validates the SRS method and indicates its potential as a general tool for studying protein folding kinetics. © Springer-Verlag Berlin Heidelberg 2006.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/11732990_34
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/11732990_34
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume3909 LNBI
dc.description.page410-424
dc.identifier.isiutNOT_IN_WOS
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