Insights from analyses of low complexity regions with canonical methods for protein sequence comparison. Issue 5 (1st August 2022)

• Record Type: Journal Article
• Authors: Jarnot, Patryk; Ziemska-Legiecka, Joanna; Grynberg, Marcin; Gruca, Aleksandra
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-08-01
• Subjects: comparison methods -- low complexity regions -- protein sequence similarity; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac299
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.