<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Plaku, E.</style></author><author><style face="normal" font="default" size="100%">Stamati, H.</style></author><author><style face="normal" font="default" size="100%">Clementi, C.</style></author><author><style face="normal" font="default" size="100%">L. E. Kavraki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast and Reliable Analysis of Molecular Motion Using Proximity Relations and Dimensionality Reduction</style></title><secondary-title><style face="normal" font="default" size="100%">Proteins: Structure, Function, and Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword><keyword><style  face="normal" font="default" size="100%">nonlinear dimensionality reduction for the analysis of protein motion</style></keyword><keyword><style  face="normal" font="default" size="100%">project_Proximity</style></keyword><keyword><style  face="normal" font="default" size="100%">protein motion</style></keyword><keyword><style  face="normal" font="default" size="100%">proximity relations</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www3.interscience.wiley.com/cgi-bin/abstract/114195666/ABSTRACT</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">67</style></volume><pages><style face="normal" font="default" size="100%">897--907</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The analysis of molecular motion starting from extensive sampling of
               molecular configurations remains an important and challenging task in
               computational biology. Existing methods require a significant amount
               of time to extract the most relevant motion information from such data
               sets. In this work, we provide a practical tool for molecular motion
               analysis. The proposed method builds upon the recent ScIMAP (Scalable
               Isomap) method, which, by using proximity relations and dimensionality
               reduction, has been shown to reliably extract from simulation data a
               few parameters that capture the main, linear and/or nonlinear, modes
               of motion of a molecular system. The results we present in the context
               of protein folding reveal that the proposed method characterizes the
               folding process essentially as well as ScIMAP. At the same time, by
               projecting the simulation data and computing proximity relations in a
               low-dimensional Euclidean space, it renders such analysis
               computationally practical. In many instances, the proposed method
               reduces the computational cost from several CPU months to just a few
               CPU hours, making it possible to analyze extensive simulation data in
               a matter of a few hours using only a single processor. These results
               establish the proposed method as a reliable and practical tool for
               analyzing motions of considerably large molecular systems and proteins
               with complex folding mechanisms.</style></abstract><work-type><style face="normal" font="default" size="100%">article</style></work-type></record></records></xml>
