Ranking and compacting binding segments of protein families using aligned pattern clusters.

ABSTRACT:

Background

Discovering sequence patterns with variation can unveil functions of a protein family that are important for drug discovery. Exploring protein families using existing methods such as multiple sequence alignment is computationally expensive, thus pattern search, called motif finding in Bioinformatics, is used. However, at present, combinatorial algorithms result in large sets of solutions, and probabilistic models require a richer representation of the amino acid associations. To overcome these shortcomings, we present a method for ranking and compacting these solutions in a new representation referred to as Aligned Pattern Clusters (APCs). To tackle the problem of a large solution set, our method reveals a reduced set of candidate solutions without losing any information

SUBMITTER: Lee ES 

PROVIDER: S-EPMC3907781 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature