FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution
Fig 3
General scheme of FilterDCA: Our approach combines the results of plmDCA applied to two-domain MSAs with structural filters constructed as average contact matrices using 6 contact classes.
Structural supervision is used to learn a logistic regression based on the plmDCA score itself, and the best correlation with one of the six structural filters.