Accelerated Conformational Entropy Calculations Using Graphic Processing Units

Conformational
entropy calculation, usually computed by normal-mode
analysis (NMA) or quasi harmonic analysis (QHA), is extremely time-consuming.
Here, instead of NMA or QHA, a solvent accessible surface area (SASA)
based model was employed to compute the conformational entropy, and
a new fast GPU-based method called MURCIA (Molecular Unburied Rapid
Calculation of Individual Areas) was implemented to accelerate the
calculation of SASA for each atom. MURCIA employs two different kernels
to determine the neighbors of each atom. The first kernel (K1) uses
brute force for the calculation of the neighbors of atoms, while the
second one (K2) uses an advanced algorithm involving hardware interpolations
via GPU texture memory unit for such purpose. These two kernels yield
very similar results. Each kernel has its own advantages depending
on the protein size. K1 performs better than K2 when the size is small
and vice versa. The algorithm was extensively evaluated for four protein
data sets and achieves good results for all of them. This GPU-accelerated
version is ∼600 times faster than the former sequential algorithm
when the number of the atoms in a protein is up to 105.