Using a new database of 20 proteins not included in any of the previously used training datasets, we have incorporated multiple alignment information from homologous proteins into two well-characterized prediction methods: COMBINE (a jury method) and the Q-L (or quadratic-logistic) method. It is found that the increase in accuracy from the use of related proteins is similar for both methods (5.8% and 6.3%, respectively) yielding a per residue prediction accuracy (Q3) of 68.7% and 69.0%, respectively, for a three state prediction. Most of the improvement came from consideration of averaging, profiling or consensus predictions. Of this improvement, a small amount (0.5%) came from recognition that "gap-permissive" positions in the alignment are most frequently in the coil state. Our finding is consistent with the hypothesis of a common secondary structure for the aligned family, and that improved accuracy is due to reduced noise in the prediction.