Clustering Peptide Structures through Identification of Commonly Exposed Groups

Abstract

A clustering analysis method using the number of commonly exposed groups identified as a clustering criterion for a group of peptide structures generated from an in vacuo molecular dynamics simulation is presented. The number of commonly exposed groups is identified as the number of atoms of the same type which appear on vertices of groups of three dimensional convex hulls computed for groups of structures sampled and collected as blocks. Blocks of structures of high structural similarity are classified as clusters if their corresponding number of commonly exposed groups identified are larger than a preset criterion. Linkages between blocks are provided with the generation of blocks consisting of overlapping structures. However, the linkage can be eliminated by employing a minimal distance criterion for each block generated. The feasibility of this proposed clustering method is tested through a comparison of results obtained from a conventional and a hierarchical clustering method. Since change in fine structural features can be detected as the change in the number of commonly exposed groups identified, we find that the method is superior to the conventional clustering one in partitioning compact and well-separated clusters.

A strategy for analysis of (molecular) equilibrium simulations: Configuration space density estimation, clustering, and visualization

“…Two clustering algorithms were compared and single linkage found to be inappropriate by Torda and van Gunsteren [58]. Clustering based on convex molecular hulls was shown by Lin et al [44]. The data used in the present study has already been analyzed with a non-hierarchical cluster analysis [17,49].…”

Bayesian Model Based Clustering Analysis: Application to a Molecular Dynamics Trajectory of the HIV-1 Integrase Catalytic Core

“…12,13 Nonhierarchical clustering was demonstrated with Euclidean distances in the parameter space of dihedral angles. 9,14 Hierarchical clustering was performed on the basis of convex molecular hulls 15 and the pairwise root-mean-square deviation (RMSD) of C-R atoms. 16 Among hierarchical agglomerative methods, a complete link algorithm with Hamming distances 17 and a single-linkage algorithm 18 were presented.…”

“…Various clustering methods have been previously applied in classifying conformational data from MD trajectories. For example, fuzzy clustering was proposed to analyze MD trajectories of proteins and polypeptides. , Nonhierarchical clustering was demonstrated with Euclidean distances in the parameter space of dihedral angles. , Hierarchical clustering was performed on the basis of convex molecular hulls and the pairwise root-mean-square deviation (RMSD) of C-α atoms . Among hierarchical agglomerative methods, a complete link algorithm with Hamming distances and a single-linkage algorithm were presented.…”

The Influence of Different Structure Representations on the Clustering of an RNA Nucleotides Data Set

“…Some techniques try to visualize the relationships between the 3D-objects by projecting the structures of interest in a low dimensional space. This reduces the dimensionality of the data and is done either by defining new variables describing some global features of the structures or by making linear combinations of the original variables. , Also clustering techniques can be used to explore the underlying structure of the data. , The resulting clusters can reveal interesting information about the structural behavior of the considered molecules. Which structures are grouped, and how does this relate to correspondences/differences in chemical behavior?…”

“…3,4 Also clustering techniques can be used to explore the underlying structure of the data. 5,6 The resulting clusters can reveal interesting information about the structural behavior of the considered molecules. Which structures are grouped, and how does this relate to correspondences/ differences in chemical behavior?…”