N. Gautham

Title: Exploring Conformational Space Using a Mean Field Technique with MOLS

Department of Crystallography and Biophysics, University of Madras, Guindy
Campus, Chennai 600 025, India
e-mail: n_gautham@hotmail.com

The computational identification of all the low energy structures of a
peptide given only its sequence is not an easy task even for small peptides,
due to the multiple-minima problem and combinatorial explosion. We have
developed an algorithm that addresses this problem. In statistical
experimental design, mutually orthogonal Latin squares (MOLS) are used to
systematically sample the space of the variables. This allows the
experimenter to conduct the experiment with a relatively small number of
runs, instead of examining all possible combinations of values of the
variables. We have recast the problem of searching for minimum energy
molecular structures on the potential energy surface as one that could also
be similarly solved by MOLS sampling, with the results of the sample being
analyzed by a variant of the mean field technique. This has lead to a number
of applications. Conformational studies of oligopeptides, including loop
sequences in proteins have been carried out using this technique. In general
the calculations identified all the folds determined by previous studies,
and in addition picked up other energetically favorable structures. The
method was also used to map the energy surface of the peptides. The unique
sets of structures identified were used to visualize the entire potential
energy landscape as two-dimensional projections and as minimum energy
envelopes. In another application, we have combined the MOLS technique,
using it to generate a library of low energy structures of an oligopeptide,
with a genetic algorithm to predict protein structures. The protein sequence
is divided into oligopeptides, and a structure library is generated for
each. These libraries are used in a newly defined mutation operator that,
together with variation, crossover, and diversity operators, is used in a
modified genetic algorithm to make the prediction. The method has been
further applied to the problem of docking a ligand in its receptor site,
with encouraging results.