IDENTIFICATION OF AMINO-ACID-SEQUENCES WITH GOOD FOLDING PROPERTIES IN AN OFF-LATTICE MODEL

Folding properties of a two-dimensional toy protein model containing o nly two amino acid types, hydrophobic and hydrophilic, respectively, a re analyzed. An efficient Monte Carlo procedure is employed to ensure that the ground states are found. The thermodynamic properties are fou nd to be strongly sequence dependent in contrast to the kinetic ones. Hence, criteria for good folders are defined entirely in terms of ther modynamic fluctuations. With these criteria sequence patterns that fol d well are isolated. For 300 chains with 20 randomly chosen binary res idues approximately 10% meet these criteria. Also, an analysis is perf ormed by means of statistical and artificial neural network methods fr om which it is concluded that the folding properties can be predicted to a certain degree given the binary numbers characterizing the sequen ces.