Abstract
This paper presents a novel approach for solving the Multiple Sequence Alignment (MSA) problem. K-Means clustering is combined with the Rubber Band Technique (RBT) to introduce an iterative optimization algorithm, namely RBT-Km, to find the optimal alignment for a set of input protein sequences. In this technique, the MSA problem is modeled as a Rubber Band, while the solution space is modeled as plate with several poles corresponding locations in the input sequences that are most likely to be correlated and/or biologically related. K-Means clustering is then used to discriminate biologically related locations from those that may appear by chance. RBT-Km is tested with one of the well-known benchmarks in this field (BALiBASE 2.0). The results demonstrate the superiority of the proposed technique even in the case of formidable sequences.
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