##
An Analysis of the Structural and Energetic Properties of Deoxyribose by
Potential Energy Methods

###
We discuss the three fundamental issues of a computational approach in
structure prediction by potential energy minimization, and analyze them
for the nucleic acid component deoxyribose. Predicting the conformation
of deoxyribose is important not only because of the molecule's central
conformational role in the nucleotide backbone, but also because energetic
and geometric discrepancies from experimental data have exposed some underlying
uncertainties in potential energy calculations. The three fundamental issues
examined here are: i) choice of coordinate system to represent the molecular
conformation; ii) construction of the potential energy function; and iii)
choice of the minimization technique. For our study, we use the following
combination. First, the molecular conformation is represented in cartesian
coordinate space with the full set of degrees of freedom. This provides
an opportunity for comparison with the pseudorotation approximation. Second,
the potential energy function is constructed so that all the interactions
other than the nonbonded terms are represented by *polynomials*
of the coordinate variables. Third, two powerful Newton methods that are
globally and quadratically convergent are implemented: Gill and Murray's
Modified Newton Method and a Truncated Newton method, specifically developed
for potential energy minimization. These strategies have produced the two
experimentally-observed structures of deoxyribose with geometric data (bond
angles and dihedral angles) in very good agreement with experiment. More
generally, the application of these modeling and minimization techniques
to potential energy investigations is promising. The use of cartesian variables
and polynomial representation of bond length, bond angle and torsional
potentials promotes efficient second-derivative computation and hence
application of Newton methods. The truncated Newton in particular, is ideally
suited for potential energy minimization not only because the storage and
computational requirements of Newton methods are made manageable, but also
because it contains an important algorithmic adaptive feature: the minimization
search is diverted from regions where function is nonconvex and is directed
quickly toward physically interesting regions.

###

Click to go back to the publication list

###### Webpage design by Igor Zilberman

######