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.
Kim
Minkyoung
M
Insilicotech Co. Ltd., A-1101, Kolontripolis, 210, Geumgok-Dong, Bundang-Gu, Seongnam-Shi 463-943, Korea.
Choi
Seung-Hoon
SH
Kim
Junhyoung
J
Choi
Kihang
K
Shin
Jae-Min
JM
Kang
Sang-Kee
SK
Choi
Yun-Jaie
YJ
Jung
Dong Hyun
DH
D000465:Algorithms; D016000:Cluster Analysis; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry*; D011487:Protein Conformation
D010455:Peptides