Abstract:
In this paper, we present a novel approach for discovering kinetically metastable states of biomolecular conformations. Several kinetically-aware metrics which encode both geometric and kinetic information about biomolecules are proposed. We embed the new metrics into k-center clustering and r-cover clustering algorithms to estimate the metastable states. Those clustering algorithms using kinetically-aware metrics are tested on a large scale biomolecule conformation dataset. It turns out that our algorithms are able to identify the kinetic meaningful clusters.
Bibtex:
@inproceedings{gjg-kcdmd-11,
author = {Chen Gu and Xiaoye Jiang and Leonidas Guibas},
title = {Kinetically-aware Conformational Distances in Molecular Dynamics},
booktitle = {Proceedings of the 23rd Canadian Conference on Computational Geometry},
pages = {217--222},
year = {2011},
}
