Density-based clustering of small peptide conformations sampled from a molecular dynamics simulation

Minkyoung Kim, Seung Hoon Choi, Junhyoung Kim, Kihang Choi, Jae Min Shin, Sang Kee Kang, Yun Jaie Choi, Dong Hyun Jung

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2528-2536
Number of pages9
JournalJournal of Chemical Information and Modeling
Volume49
Issue number11
DOIs
Publication statusPublished - 2009 Nov 23

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences

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