Annotations of the genes and their products are largely guided by inferring homology. Sequence
similarity is the primary measure used for annotation purpose however, the domain content and
order were given less importance albeit the fact that domain insertion, deletion, positional
changes can bring in functional varieties. Of late, several methods developed quantify domain
architecture similarity depending on alignments of their sequences and are focused on only homologous
proteins. We present an alignment-free domain architecture-similarity search (ADASS) algorithm that
identifies proteins that share very poor sequence similarity yet having similar domain architectures.
We introduce a “singlet matching-triplet comparison” method in ADASS, wherein triplet of domains is
compared with other triplets in a pair-wise comparison of two domain architectures. Different events
in the triplet comparison are scored as per a scoring scheme and an average pairwise distance score
(Domain Architecture Distance score - DAD Score) is calculated between protein domains architectures.
We use domain architectures of a selected domain termed as centric domain and cluster them based on DAD score.
The algorithm has high Positive Prediction Value (PPV) with respect to the clustering of the sequences of selected
domain architectures. A comparison of domain architecture based dendrograms using ADASS method and an existing
method revealed that ADASS can classify proteins depending on the extent of domain architecture level similarity.
ADASS is more relevant in cases of proteins with tiny domains having little contribution to the overall sequence
similarity but contributing significantly to the overall function.