  A new method for sequence optimization in protein models is presented. The
approach, which has inherited its basic philosophy from recent work by Deutsch
and Kurosky [Phys. Rev. Lett. 76, 323 (1996)] by maximizing conditional
probabilities rather than minimizing energy functions, is based upon a novel
and very efficient multisequence Monte Carlo scheme. By construction, the
method ensures that the designed sequences represent good folders
thermodynamically. A bootstrap procedure for the sequence space search is
devised making very large chains feasible. The algorithm is successfully
explored on the two-dimensional HP model with chain lengths N=16, 18 and 32.
