Clustering of multi‐domain protein sequences

• Published in: Proteins, structure, function, and bioinformatics Vol. 86; no. 7; pp. 759 - 776
• Main Authors: Mehrotra, Prachi, Ami, Vimla Kany G., Srinivasan, Narayanaswamy
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01-07-2018
• Subjects: Alignment; alignment‐free method; Amino acid sequence; Classification; Classification schemes; Clustering; Datasets; Information processing; multi‐domain protein; Nucleotide sequence; protein classification; Protein-tyrosine-phosphatase; Proteins; sequence classification; Sequences; Structure-function relationships; Tyrosine; multi-domain protein; alignment-free method; protein classification; sequence classification
• ISSN: 0887-3585; 1097-0134
• DOI: 10.1002/prot.25510

The overall function of a multi‐domain protein is determined by the functional and structural interplay of its constituent domains. Traditional sequence alignment‐based methods commonly utilize domain‐level information and provide classification only at the level of domains. Such methods are not capable of taking into account the contributions of other domains in the proteins, and domain‐linker regions and classify multi‐domain proteins. An alignment‐free protein sequence comparison tool, CLAP (CLAssification of Proteins) was previously developed in our laboratory to especially handle multi‐domain protein sequences without a requirement of defining domain boundaries and sequential order of domains. Through this method we aim to achieve a biologically meaningful classification scheme for multi‐domain protein sequences. In this article, CLAP‐based classification has been explored on 5 datasets of multi‐domain proteins and we present detailed analysis for proteins containing (1) Tyrosine phosphatase and (2) SH3 domain. At the domain‐level CLAP‐based classification scheme resulted in a clustering similar to that obtained from an alignment‐based method. CLAP‐based clusters obtained for full‐length datasets were shown to comprise of proteins with similar functions and domain architectures. Our study demonstrates that multi‐domain proteins could be classified effectively by considering full‐length sequences without a requirement of identification of domains in the sequence.