An alignment-free domain architecture similarity search (ADASS) algorithm for inferring homology between multi-domain proteins

Abstract

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.

GenDiS database update with improved approach and features to recognize homologous sequences of protein domain superfamilies

“…Henceforth, this result is referred as Pfam-DA. DA of homologues was compared, and homologues were clustered using an in-house tool called alignment-free DA similarity search (ADASS) algorithm (21). The strings of domains between sequences are used to score the extent of dissimilarity amongst sequences employed to create phylogeny.…”

Evolution of Protein Domain Architectures

“…The DAAC algorithm [98] explicitly transfers functional annotation to query sequences based on domain architectural similarity to annotated homologs, as does FACT [93]. In the same vein, similarity measures between architectures are available using the WDAC [99] tool and in ADASS [100]. Domain architecture similarity is used for orthology detection in the porthoDom software [68].…”

The language of the protein universe

“…MDAT [38] uses a domain similarity matrix to score domain pairs and aligns the domain arrangements by a progressive alignment method. ADASS [39] compares and classifies protein domain architectures by recognizing similarity between the domain architectures even if the proteins share very poor sequence similarity; it includes neighbor information in it’s score. The Enrichment of Network Topological Similarity (ENTS), is a framework to improve the performance of large-scale similarity searches; it considers a continuous protein space and performs well on the fold recognition problem [40].…”