Density-based clustering of small peptide conformations sampled from a molecular dynamics simulation.
This study describes the application of a density-based algorithm to clustering small peptide conformations after a molecular dynamics simulation. We propose a clustering method for small peptide conformations that enables adjacent clusters to be separated more clearly on the basis of neighbor density. Neighbor density means the number of neighboring conformations, so if a conformation has too few neighboring conformations, then it is considered as noise or an outlier and is excluded from the list of cluster members. With this approach, we can easily identify clusters in which the members are densely crowded in the conformational space, and we can safely avoid misclustering individual clusters linked by noise or outliers. Consideration of neighbor density significantly improves the efficiency of clustering of small peptide conformations sampled from molecular dynamics simulations and can be used for predicting peptide structures.
Insilicotech Co. Ltd., A-1101, Kolontripolis, 210, Geumgok-Dong, Bundang-Gu, Seongnam-Shi 463-943, Korea.
Minkyoung
M
Kim
Seung-Hoon
SH
Choi
Junhyoung
J
Kim
Kihang
K
Choi
Jae-Min
JM
Shin
Sang-Kee
SK
Kang
Yun-Jaie
YJ
Choi
Dong Hyun
DH
Jung
D000465:Algorithms; D016000:Cluster Analysis; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry*; D011487:Protein Conformation
D010455:Peptides