Background: Designing amino acid sequences that are stable in a given target structure
amounts to maximizing a conditional probability. A straightforward approach to accomplish
this is a nested Monte Carlo where the conformation space is explored over and over
again for different fixed sequences, which requires excessive computational demand.
Several approximate attempts to remedy this situation, based on energy minimization
for fixed structure or high-\(T\) expansions, have been proposed. These methods are
fast but often not accurate since folding occurs at low \(T\).
Results: We develop a multisequence Monte Carlo procedure, where both sequence and
conformation space are simultaneously probed with efficient prescriptions for pruning
sequence space. The method is explored on hydrophobic/polar models. We first discuss
short lattice chains, in order to compare with exact data and with other methods.
The method is then successfully applied to lattice chains with up to 50 monomers,
and to off-lattice 20-mers.
Conclusions: The multisequence Monte Carlo method offers a new approach to sequence
design in coarse-grained models. It is much more efficient than previous Monte Carlo
methods, and is, as it stands, applicable to a fairly wide range of two-letter models.