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|
| Type |
Poster Presentation |
| Area |
Physical Chemistry |
| Room No. |
Exhibition Hall 2+3 |
| Time |
10월 20일 (금요일) 13:00~14:30 |
| Code |
PHYS.P-86 |
| Subject |
Protein Conformational Space Discretization by Using Thermodynamic Order Parameter |
| Authors |
Song-Ho Chong, Sihyun Ham*
Department of Chemistry, Sookmyung Women's University, Korea
|
| Abstract |
Dimensionality reduction and clustering are commonly used for analyzing high-dimensional time-series data generated by atomistic biomolecular simulations. Their practical success depends critically on the choice of good order parameters or reaction coordinates. So far, geometric order parameters, such as the root mean square deviation from a reference structure, fraction of native amino acid contacts, and collective coordinates that best characterize rare conformational transitions, have been prevailing in the protein state space discretization. Here, we show that the solvent-averaged effective energy, which is unambiguously defined for a given protein conformation, serves as a good order parameter of protein folding. This is illustrated through the application to the folding-unfolding simulation trajectory of villin headpiece subdomain. The most distinctive feature of this thermodynamic order parameter is that it does not require any information on the native structure. We rationalize the suitability of the effective energy as an order parameter in terms of the funneledness of the underlying protein free energy landscape. We also demonstrate that an improved conformational space discretization is achieved by incorporating the effective energy. Because of its ab initio nature, the use of the effective energy will find wide applications in combination with unsupervised machine learning techniques. |
| E-mail |
songho.chong@gmail.com |
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120th General Meeting of the KCS