Conformational Analysis in a Multidimensional Energy Landscape: Study of an Arginylglutamate Repeat

Abstract

The identification of the distinct conformation classes of a molecule is a common and often crucial step in establishing structure-function relationships. Many different methods have been suggested for that purpose which differ in their choice of a (dis)similarity measure and clustering algorithm. The present study discusses and analyzes these issues, proposing a method based on principal component analysis (PCA), which is applied to conformations obtained from molecular dynamics (MD) simulations of an arginylglutamate repeat. Simulations are done at different pH values, using both standard MD and constant-pH MD methods, with the peptide displaying a very high conformational variety. The conformational analysis starts with a comprehensive comparison of different sets of conformational coordinates and of their ability to preserve structural similarity between conformations. The selected set of conformational coordinates is then used to investigate the preservation of structural similarity after PCA transformation, concluding the need of using a multidimensional conformation space. This conformation space is then used to derive a multidimensional probability density and the corresponding energy landscape. The application of a simple cutoff algorithm to the resulting multidimensional landscape is then shown to produce a consistent set of distinct and homogeneous conformation classes. Overall, this methodology provides an efficient way to identify the major conformation classes of a molecule in a way that directly reflects the density of states in the multidimensional conformation space, contrasting with the more heuristic nature of standard clustering methods.

Structural Characterization of Histatin 5–Spermidine Conjugates: A Combined Experimental and Theoretical Study

“…For that purpose, a principal component analysis (PCA) of the peptide backbone for all five systems was performed, as devised by Campos and Baptista. 41 In general, we found that the first two principal components (PCs) comprise the majority of the variance in each system. Furthermore, visual inspection and root-mean-square calculations (not shown) prove that corresponding minima in the free energy landscapes enclose closely related conformations and that different minima relate to distinct conformational classes, whose dissimilarity increases with their distance in the landscape.…”

F508del disturbs the dynamics of the nucleotide binding domains of CFTR before and after ATP hydrolysis

“…51,52 The probability densities maps were obtained by estimating densities in a two-dimensional grid with a spacing of 0.01 nm 2 using a Gaussian Kernel estimator. 55 The error bars correspond to the 95% confidence interval obtained by bootstrapping, 56 unless stated otherwise. PyMOL 2.0 was used to visualize trajectories and produce the pictures presented.…”

Asymmetric Diels–Alder cycloadditions of d-erythrose 1,3-butadienes to achiral t-butyl 2H-azirine 3-carboxylate

“…The multidimensional free energy landscape was calculated using a principal component analysis (PCA) approach based on the structural dissimilarity of all pairs of conformations recorded. The protocol for the PCA calculations and identification of the lowest free energy conformations used herein was the same as described by Sara et al 17 …”