Kavraki Lab

Physical and Biological Computing

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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.