title: Fast and reliable analysis of molecular motion using proximity relations and dimensionality reduction.
abstract: The analysis of molecular motion starting from extensive sampling of molecular configurations remains an important and challenging task in computational biology. Existing methods require a significant amount of time to extract the most relevant motion information from such data sets. In this work, we provide a practical tool for molecular motion analysis. The proposed method builds upon the recent ScIMAP (Scalable Isomap) method, which, by using proximity relations and dimensionality reduction, has been shown to reliably extract from simulation data a few parameters that capture the main, linear and/or nonlinear, modes of motion of a molecular system. The results we present in the context of protein folding reveal that the proposed method characterizes the folding process essentially as well as ScIMAP. At the same time, by projecting the simulation data and computing proximity relations in a low-dimensional Euclidean space, it renders such analysis computationally practical. In many instances, the proposed method reduces the computational cost from several CPU months to just a few CPU hours, making it possible to analyze extensive simulation data in a matter of a few hours using only a single processor. These results establish the proposed method as a reliable and practical tool for analyzing motions of considerably large molecular systems and proteins with complex folding mechanisms.
authors: Department of Computer Science, Rice University, Houston, Texas 77005, USA.
authors: Erion
authors: E
authors: Plaku
authors: Hernán
authors: H
authors: Stamati
authors: Cecilia
authors: C
authors: Clementi
authors: Lydia E
authors: LE
authors: Kavraki
mesh_terms: D003198:Computer Simulation; D008958:Models, Molecular; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D013997:Time Factors
chemical_list: D011506:Proteins