Abstract
Multiple comparison or alignmentof protein sequences has become a fundamental tool in many different domains in modern molecular biology, from evolutionary studies to prediction of 2D/3D structure, molecular function and inter-molecular interactions etc. By placing the sequence in the framework of the overall family, multiple alignments can be used to identify conserved features and to highlight differences or specificities. In this paper, we describe a comprehensive evaluation of many of the most popular methods for multiple sequence alignment (MSA), based on a new benchmark test set. The benchmark is designed to represent typical problems encountered when aligning the large protein sequence sets that result from today's high throughput biotechnologies. We show that alignmentmethods have significantly progressed and can now identify most of the shared sequence features that determine the broad molecular function(s) of a protein family, even for divergent sequences. However,we have identified a number of important challenges. First, the locally conserved regions, that reflect functional specificities or that modulate a protein's function in a given cellular context,are less well aligned. Second, motifs in natively disordered regions are often misaligned. Third, the badly predicted or fragmentary protein sequences, which make up a large proportion of today's databases, lead to a significant number of alignment errors. Based on this study, we demonstrate that the existing MSA methods can be exploited in combination to improve alignment accuracy, although novel approaches will still be needed to fully explore the most difficult regions. We then propose knowledge-enabled, dynamic solutions that will hopefully pave the way to enhanced alignment construction and exploitation in future evolutionary systems biology studies.
Highlights
Evolutionary theory provides a unifying framework for analysing genomics data and for studying various phenomena in molecular, cell, or developmental biology [1]
By placing the sequence in the framework of the overall family, multiple sequence alignment (MSA) can be used to characterise important features thatdetermine the broad molecular function(s) of the protein, such as the 3-dimensional structure or catalytic sites, and that have been conserved throughout evolution.most proteins act in complex, dynamic networks that are dependent on the biological context, for example subcellular localisation, temporal and spatial expression patterns, or environment
We have used a new alignment benchmark to investigate whether MSA programs are capable of constructing high quality alignments for the sequences resulting from modern biotechnologies
Summary
Evolutionary theory provides a unifying framework for analysing genomics data and for studying various phenomena in molecular, cell, or developmental biology [1]. Evolutionarybased inference systems are playing an increasingly important role in diverse areas, such as elucidation of the tree of life [2], studies of epidemiology and virulence [3], drug design [4], human genetics [5], cancer [6] or biodiversity [7] Essential prerequisites for such evolutionary-based studies are the multiple sequence alignment (MSA) and its subsequent analysis [8,9,10]. Iterative algorithms were developed to construct more reliable multiple alignments, using for example iterative refinement strategies [17], Hidden Markov Models [18] or Genetic Algorithms [19] These methods were shown to be more successful at aligning the most conserved regions for a wide variety of test cases, some accuracy was lost for distantly related sequences, in the ‘twilight zone’ of evolutionary relatedness [20,21]
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