Figure 5. Sequences from later genetic algorithm generations contribute more in interface design prediction than in protein stability design prediction.

The total Boltzmann weights in the final PWM for the new sequences sampled in each generation were calculated. The distribution of contributions for each generation across the 200 simulations (one simulation for each backbone in the backrub ensemble) is shown. Boxes span from the first quartile to the third quartile, with the line indicating the median. Whiskers extend to the most extreme data point within 1.5 times the interquartile range of the box. Circles show data points beyond that limit. A. Because the fitness function used for protein-protein interfaces (here shown for a complex between the second PDZ domain of DLG1 and peptides) is different from the fitness function used for optimization of side chain packing, the genetic algorithm is important for enriching the population in sequences predicted to be better binders. B. For optimization of protein fold stability (designing positions in the GB1 core), the initial full protein design phase is very effective at finding a low energy sequence, which dominates the contribution to the position weight matrix (PWM) when the same Boltzmann factor (kT = 0.23) is used. C. When the Boltzmann factor is optimized to minimize the average absolute difference between experiment and computation (kT = 0.59), the contribution of the later generations increases significantly.