<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miguel L. Teodoro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling Protein Flexibility Using Collective Modes of Motion: Applications to Drug Design</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">computer-assisted drug design</style></keyword><keyword><style  face="normal" font="default" size="100%">kavrakilab</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Rice University</style></publisher><pub-location><style face="normal" font="default" size="100%">Houston, TX</style></pub-location><pages><style face="normal" font="default" size="100%">255</style></pages><abstract><style face="normal" font="default" size="100%">This work shows how to decrease the complexity of modeling flexibility in 
proteins by reducing the number of dimensions necessary to model important 
macromolecular motions such as the induced fit process. Induced fit occurs during the 
binding of a protein to other proteins, nucleic acids or small molecules (ligands) and is 
a critical part of protein function. It is now widely accepted that conformational 
changes of proteins can affect their ability to bind other molecules and that any progress 
in modeling protein motion and flexibility will contribute to the understanding of key 
biological functions. However, modeling protein flexibility has proven a very difficult 
task. Experimental laboratory methods such as X-ray crystallography produce rather 
limited information, while computational methods such as molecular dynamics are too 
slow for routine use with large systems. In this work we show how to use the Principal 
Component Analysis method, a dimensionality reduction technique, to transform the 
original high-dimensional representation of protein motion into a lower dimensional 
representation that captures the dominant modes of motions of proteins. For a medium- 
sized protein this corresponds to reducing a problem with a few thousand degrees of 
freedom to one with less than fifty. Although there is inevitably some loss in accuracy, 
we show that we can approximate conformations that have been observed in laboratory 
experiments, starting from different initial conformations and working in a drastically 
reduced search space. As shown in this work, the accuracy of protein approximations 
using this method is similar to the tolerance of current rigid protein docking programs 
to structural variations in receptor models. </style></abstract><work-type><style face="normal" font="default" size="100%">Ph.D.</style></work-type></record></records></xml>