Sequence Design in Coarse-Grained Protein Models

Designing amino acid sequences that are stable in a given target structure amounts to max- imizing 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 dierent fixed sequences. In this paper we discuss an alternative Monte Carlo approach, multisequence de- sign, where conformation and sequence degrees of freedom are simultaneously probed. The method is explored on hydrophobic/polar models. A statistical analysis of sequence corre- lations is also discussed. It is found that hydrophobic/polar model sequences and enzymes display hydrophobicity correlations that are qualitatively similar.