Algorithmic dimensionality reduction for molecular structure analysis

William M. Brown

doi:10.1063/1.2968610

2008-08-14

William M. Brown *

Shawn Martin

Sara Pollock

Evangelos A. Coutsias

Jean-Paul Watson

DOI：10.1063/1.2968610

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摘要

En 中文

Dimensionality reduction approaches have been used to exploit the redundancy in a Cartesian coordinate representation of molecular motion by producing low-dimensional representations of molecular motion. This has been used to help visualize complex energy landscapes, to extend the time scales of simulation, and to improve the efficiency of optimization. Until recently, linear approaches for dimensionality reduction have been employed. Here, we investigate the efficacy of several automated algorithms for nonlinear dimensionality reduction for representation of trans, trans-1,2,4-trifluorocyclo-octane conformation-a molecule whose structure can be described on a 2-manifold in a Cartesian coordinate phase space. We describe an efficient approach for a deterministic enumeration of ring conformations. We demonstrate a drastic improvement in dimensionality reduction with the use of nonlinear methods. We discuss the use of dimensionality reduction algorithms for estimating intrinsic dimensionality and the relationship to the Whitney embedding theorem. Additionally, we investigate the influence of the choice of high-dimensional encoding on the reduction. We show for the case studied that, in terms of reconstruction error root mean square deviation, Cartesian coordinate representations and encodings based on interatom distances provide better performance than encodings based on a dihedral angle representation. (C) 2008 American Institute of Physics.

Keyword:

FREE-ENERGY LANDSCAPES

PROTEIN DYNAMICS

HARMONIC-ANALYSIS

LARGE SYSTEMS

PRINCIPAL COMPONENTS

SIMULATION

CONFORMATIONS

MODEL

CYCLOOCTANE

RELAXATION