CLUSS: Clustering of protein sequences based on a new similarity measure

Background: The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is espec...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Abdellali Kelil, Shengrui Wang, Ryszard Brzezinski, Alain Fleury
• Keywords: BMCBI 2007, Protein Sequences, Proteins, Sequences