Progressively Multiple Protein Sequences Alignment Using Intuitionistic Fuzzy Approach

The progressive alignment approach constitutes one of the most convenient and effective ways to alignmultiple sequences. Atanassov modifies the fuzzy set by proposing an intuitionistic concept. In this concept,a related degree and a non-relationship degree is detected. However, the sum of the relationship and nonrelationshipdegrees is bare than or equal to one. As a result, the hesitancy degree equals one minus from thewhole of the related degree and the non-relationship degree. Traditional hierarchical clustering algorithmsare employed broadly to cluster numerical information. Some modifications need the formal hierarchicalclustering algorithms to deal with the data expressed in an intuitionistic fuzzy set. In this study, we proposeda measure of the distance between pairs of protein sequences by intuitionistic fuzzy approach andconstruction merge the tree by a hierarchical grouping to improve the sensitivity of progressive multiplesequence alignments. Both unweighted paired groups with arithmetic mean (UPGMA)- and neighbor-joining(NJ)-based hierarchical clustering were employed to evaluate the algorithm performance. The mergingcontinues until one group remains. Ultimately, the sequences have progressively aligned according to thebranching order in the merge tree. Reference sequences from BALiBASE ۴.۰ (hand-aligned), PREFAB ۴.۰(structurally supervised), and OXBench were employed to evaluate the method performance. We computedthe quality of the alignments using the Friedman ranks test in the P<۰.۰۵ statistical significance level, notonly in terms of SP-and C- but also TC-score. The UPGMA and NJ-groping of the proposed method performwell in improving the alignment sensitivity and accuracy. Comparatively, where the sequences are not closeto each other, the NJ clustering model has more reliable performance. However, UPGMA clustering was thetop performer in aligning all the BALiBASE reference sequence sets. The drawback of this approach is itshigher time complexity with similar memory usage to the ClustalW.

Keywords:

Intuitionistic fuzzy approach , Multiple sequence alignment , Progressive alignment