An ACL profile is composed of a core constraint language, which is shared with the other profiles, and a MOF architecture metamodel.
The core constraint may be incorporated into other alignment algorithms as well.
For some families, including Macro and PDZ, all members benefited from the core constraint.
Constructing high-quality alignments between well-defined core regions, in contrast, benefits tremendously from the core constraint.
The clear shortcoming of the core constraint is that at some level of divergence, core regions cannot be aligned correctly without insertions or deletions, hence methods without the core constraint are more suited to remote homolog recognition and alignment.
The core constraint exploits relationships between profile columns, prohibiting insertions or deletions within blocks, rather than pursuing improvements through refinement of the column scoring function.
Although the core constraint reduces the space of possible alignment solutions, it does not necessarily constrain the alignment to only one good solution.
The higher Sbalanced scores for both CORAL methods over the other methods suggest that the core constraint played a significant role in improving performance for several families.
We describe the profile alignment algorithm with core constraint in terms of required modifications to the canonical algorithm for local alignment (Smith and Waterman, 1981).
To better capture this property, we propose a method CORAL (CORe ALigner) to align core regions from two protein families without indels within blocks, which we will refer to as the core constraint.
The core constraint may also be incorporated into profile alignment algorithms with more sophisticated scoring methods to improve on both CORAL and the original method for aligning conserved cores.
